Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • View all journals
  • My Account Login
  • Explore content
  • About the journal
  • Publish with us
  • Sign up for alerts
  • Review Article
  • Open access
  • Published: 18 August 2021

The role of prefrontal cortex in cognitive control and executive function

  • Naomi P. Friedman   ORCID: orcid.org/0000-0002-4901-808X 1 &
  • Trevor W. Robbins   ORCID: orcid.org/0000-0003-0642-5977 2  

Neuropsychopharmacology volume  47 ,  pages 72–89 ( 2022 ) Cite this article

77k Accesses

297 Citations

156 Altmetric

Metrics details

  • Cognitive control
  • Human behaviour
  • Psychiatric disorders

Concepts of cognitive control (CC) and executive function (EF) are defined in terms of their relationships with goal-directed behavior versus habits and controlled versus automatic processing, and related to the functions of the prefrontal cortex (PFC) and related regions and networks. A psychometric approach shows unity and diversity in CC constructs, with 3 components in the most commonly studied constructs: general or common CC and components specific to mental set shifting and working memory updating. These constructs are considered against the cellular and systems neurobiology of PFC and what is known of its functional neuroanatomical or network organization based on lesioning, neurochemical, and neuroimaging approaches across species. CC is also considered in the context of motivation, as “cool” and “hot” forms. Its Common CC component is shown to be distinct from general intelligence ( g ) and closely related to response inhibition. Impairments in CC are considered as possible causes of psychiatric symptoms and consequences of disorders. The relationships of CC with the general factor of psychopathology (p) and dimensional constructs such as impulsivity in large scale developmental and adult populations are considered, as well as implications for genetic studies and RDoC approaches to psychiatric classification.

Similar content being viewed by others

critical thinking and executive function

The role of PFC networks in cognitive control and executive function

Vinod Menon & Mark D’Esposito

critical thinking and executive function

A canonical trajectory of executive function maturation from adolescence to adulthood

Brenden Tervo-Clemmens, Finnegan J. Calabro, … Beatriz Luna

critical thinking and executive function

A cognitive neurogenetic approach to uncovering the structure of executive functions

Junjiao Feng, Liang Zhang, … Gui Xue

Introduction

Many psychiatric disorders and neurological conditions are associated with deficits in cognitive control (CC) and/or dysfunction of the prefrontal cortex (PFC) and its associated circuitry [ 1 , 2 , 3 , 4 ]. Consequently, there is a considerable premium on elucidating the basic psychological and neuronal mechanisms underlying the PFC’s role within the neural networks that regulate behavior and cognition.

CC is a term usually associated with the healthy functioning of the PFC and related regions such as the cingulate cortex [ 5 ]. Deriving from a cybernetic and cognitive neuroscience perspective, CC has often been considered synonymous with the earlier notion of executive function (EF), which has its roots in studies of clinical neuropsychology. In both cases, a core process of behavioral regulation is envisaged that optimises goal-directed behavior and counters automaticity. This process has many similarities with the distinction between controlled and automatic responding [ 6 ], which approximately aligns with the learning theory distinction between goal-directed and habitual responding [ 7 ]. One would expect the absence of CC to produce automatic behavior; controlled responding is goal-directed and flexible.

Miller and Cohen (2001) [ 8 ] proposed that “[CC] stems from the active maintenance of patterns of activity in the PFC that represent goals and the means to achieve them. They provide bias signals to other brain structures whose net effect is to guide the flow of activity along neural pathways that establish the proper mappings between inputs, internal states, and outputs needed to perform a given task” (p. 167). This seminal account of the role of PFC in CC essentially consists of the contextual biasing of attention (for example, via instructions) to resolve conflicts and exert attentional control. The typical example is the much-used Stroop interference paradigm (see Fig.  1 ), in which participants are required to name the color (e.g., green) of the ink used to print words whose meaning is incongruent with that color (e.g, RED). The greater pre-potency of reading words over-reporting color causes interference, manifested as a slowing of decisional latency and activation of the anterior cingulate cortex (ACC) [ 9 ]. The conflict can be resolved by focusing attention on the color of the ink, associated with control (or bias) exerted by PFC regions. The theory was supported by an fMRI study showing that ACC activation was accompanied by activations of the dorsolateral (dl)PFC associated with top-down adjustments of response control [ 10 ]. Hence, in the simple model proposed by Miller and Cohen [ 8 ], the ACC detects conflict that is resolved by top-down biasing of response options from the dlPFC [ 9 ]. This theoretical scheme has provided one of the first demonstrations of a CC process to be mediated by specific, interactive PFC circuitry.

figure 1

When relevant, text above each schematic indicates different conditions, and text below indicates correct responses. The faces included in the emotional n -back illustration are taken from the NimStim set of models who have granted permission to publish their images in scientific journals [ 246 ].

One question to be considered here is whether CC is best considered as a unitary construct, a set of component processes, or a hybrid product of these extremes. If CC is best characterized as a set of distinct component processes, another question is how many of these can be identified and can they be further defined? The possible fractionation of CC can be related broadly to the heterogeneous nature of the PFC itself, which comprises, across species, many distinctive sub-regions, characterized by their cytoarchitectonic characteristics and by their connectivity with other brain regions. A related question then arises as to whether the PFC’s role in CC is that of a unitary entity or “multiple demand” (MD) system [ 11 ]. The MD system appeals to the enormous neural plasticity shown to be inherent within the PFC, so that the same neuronal ensembles can be recruited for superficially different tasks using “adaptive coding” [ 12 , 13 , 14 ]. However, an alternative viewpoint would be that the PFC sub-regions have somewhat different functions, potentially mediating the specific domains of CC. A more sophisticated version conceives specific PFC subregions of having multiple functions, as a consequence of the network-like nature of brain organization that has been revealed by brain imaging (see Haber et al., this issue, and Menon & D’Esposito, this issue [ 15 , 16 ]). Yet a further view would argue that CC is emergent from network processing in the brain, and there is no particular network area that mediates control (see [ 17 , 18 ]).

This article will consider these fundamental questions, beginning with the key issues of how CC is measured and how its unity and possible diversity have been evaluated at both the behavioral and neurobiological levels. In considering the neural substrates of CC, we draw upon the human neuropsychological and neuroimaging literature, as well as basic neuroscience studies in experimental animals. Clearly, these studies are well poised to address the question of dissociation of component processes, for example, via interventional techniques including lesions and neuropharmacological manipulations. A particular issue is how CC operates in different states of the internal or external environment, for example, following stress, that may alter the neurochemical ambience and functioning of the PFC and produce curvilinear, “inverted U-shaped,” functions of performance efficacy [ 19 ]. CC or EF in the past has also been related to other unitary constructs such as general intelligence, or g [ 11 ]. To what extent, therefore, are these entities the same, also entailing presumably similar neural substrates? In fact, we will discuss evidence that although these unitary neurocognitive constructs overlap to some extent, they are different.

Finally, we will consider clinical implications, particularly how CC or EF functions relate to personality traits or dimensions relevant to psychopathology, such as impulsivity and compulsivity, as well as the general psychopathological factor p , which captures covariance across a range of mental health disorders [ 20 ]. We will conclude with future research priorities, with an ultimate aim of determining how CC can be optimized for dealing with behavioral problems and mental health disorders.

Psychological organization of CC

The neuroanatomical connectivity of the PFC to most parts of the cortical and subcortical brain makes it well suited for participating in a number of neural networks and carrying out CC operations in different functional domains (e.g., spatial, visual, and verbal). Moreover, PFC functions probably depend on specializations of dendritic branching and spine density of pyramidal cells, especially in the cycloarchitectonically distinct regions of the granular PFC (Fig.  2 ) [ 21 , 22 , 23 ]. The cellular physiology of these regions is characterized by rapid firing and properties of neural plasticity that may enable such functions as goal maintenance in working memory and the flexible functioning of an MD network [ 24 , 25 , 26 , 27 ].

figure 2

The top panel depicts a lateral view, and the bottom panel depicts a medial view. Numbers indicate Brodmann Areas (BA). Note that the commonly described “ventromedial prefrontal cortex” potentially subsumes several BAs: 25, 32, 14, and possibly 11 and 13.

However, it is a major challenge to deduce how the PFC is organized to mediate the range of cognitive processes referred to as CC/EF, which include stopping automatic or dominant responses, controlling interference, switching between tasks, coordinating multiple tasks, updating working memory, monitoring, and planning [ 28 , 29 , 30 ] (see Fig.  1 for illustrations of some example tasks used to assess these cognitive processes). The term “unity and diversity” was first used in 1972 to describe the relationships among such diverse frontal lobe processes: Teuber [ 31 ] observed a “bewildering variety in man’s reaction even to fairly restricted and non-progressive [prefrontal] lesions” (p. 637), that nevertheless, could be described broadly in terms of levels of “compulsiveness” or “abnormally stimulus-bound behavior” (p. 640). Similarly, Duncan and colleagues [ 32 ] reiterated this unity and diversity term to describe frontal lobe deficits after head injury: They observed uniformly low correlations among frontal lobe tests, yet a common element of “goal neglect, or disregard of a known task requirement” (p. 713).

The term “unity and diversity” has also been used to describe the pattern of correlations among laboratory CC tests in individuals without brain lesions. Specifically, Miyake et al. [ 33 ] investigated the structure of CC in college students by administering a battery of tasks, each designed to tap one of three CC abilities: inhibiting a prepotent response (stopping an automatic response, sometimes in order to make an alternative response), updating working memory (continuously replacing no-longer relevant information in working memory with newly relevant information when it is detected in the environment), or shifting between mental sets (switching between two alternative tasks). They administered multiple tasks to assess each of these three functions so that they could estimate a latent variable (a statistical extraction of the common variance in a set of tasks) for each function. They then examined the relationships among these functions at the level of these latent variables, rather than at the level of individual tasks. The basic model they examined is shown in Fig.  3a , along with alternative models examined in later studies (Fig.  3b, c ).

figure 3

Proposed CC functions are represented as latent variables (depicted with ellipses) that predict variation in performance on specific tasks (rectangles) chosen to measure those abilities. Factor loadings are depicted with single-headed arrows between the factors and nine measured tasks. The short arrows indicate residual variances, the unique variance in each task that is unrelated to the CC factors, attributable to measurement error as well as reliable task-specific variation. a In a correlated factors model, tasks are predicted by CC factors that are allowed to be correlated, and unity and diversity are represented in the correlations between factors (represented with curved double-headed arrows). The numbers shown are the average correlations and the range of correlations from six studies using a similar battery ( N s = 137–786). b A higher-order “Common” CC factor can also be used to model the correlations among the factors [ 39 , 158 ]. This higher-order factor predicts the lower-order factors, and they correlate to the extent to which they are jointly predicted by the common factor. In such models, the diversity is captured by the residuals of these factors after the variance due to the common factor is removed (inhibiting-specific, updating-specific, and shifting-specific variances). The numbers shown indicate the average and range of factor loadings for the Common CC factor, and the corresponding averages and ranges for the residual variances for inhibiting, updating, and shifting factors (i.e., the variance not explained by the common factor), derived from the correlations in panel a . *indicates the standardized loadings were bound at 1, and the residual variances bound at zero. c Alternative model structures (called nested factors models or bifactor models) can be used to capture unity and diversity factors more directly. In these models, all tasks load on a common factor, but also load on orthogonal specific factors. These models thus partition each factor into variance that is common across all tasks and variance that is unique to tasks assessing particular processes. Although these alternative parameterizations typically do not result in appreciably different fits to the data, they can make it more convenient to examine relationships to other constructs of interest: Because the unity and diversity components are represented with orthogonal latent variables rather than in the correlations between factors or with residual variances, it is straightforward to discern whether a construct is related to the unity vs. diversity components.

This approach was motivated by the recognition that the low correlations observed in prior studies might reflect “task impurity” of CC measures. That is, CC is by definition control of other cognitive processes, and so performance in CC tasks may reflect variation in those other processes as well as the CC process of interest. Evaluating a CC ability in multiple contexts and statistically extracting what is common enables purer measures that are also free of random measurement error. Indeed, Miyake et al. [ 33 ] found that although the individual tasks showed relatively low correlations ( r  = –0.05 to 0.34), consistent with much prior research, the latent variable correlations were stronger ( r  = 0.42–0.63). These correlations were all significantly greater than zero, indicating that these three CC processes indeed shared something in common (they showed some “unity”). However, these three factors were also somewhat separable (they also showed some “diversity”): A model in which these nine tasks were explained by three correlated factors was superior to models that used fewer factors. Since that initial study, this psychometric latent variable approach has been used in a large number of studies to show that unity and diversity of CC is evident across samples and ages (e.g., [ 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 ]), although there are some studies that suggest more unity (higher correlations between factors) in early childhood [ 35 , 42 , 43 , 44 ].

Although latent variable studies often focus on the same three constructs selected by Miyake et al. [ 33 ], their model was never intended to be comprehensive. Other candidate components of CC and taxonomies are considered in Box  1 . Miyake et al. also recognized that commonly examined CC processes might comprise multiple components. For example, measures of working memory capacity and updating can include separable sub-functions like maintenance and removal of items in working memory [ 45 ]. Mental set-shifting tasks may require multiple sub-processes, including interference control, retrieval of task sets, and task-set reconfiguration [ 46 ]. And these intermediate levels may be combined with other functions (e.g., sequencing subgoals) to result in more complex postulated CC functions like planning [ 33 ].

Finally, goal-directed behavior or associated outcomes have motivational and value-based elements in decision-making cognition that raise the issue of whether CC can be distinguished from motivational control and value-based processes. Thus, for example, Koechlin [ 47 ] firmly distinguishes between CC and “motivational control.” Most models of CC focus on so-called “cool” tasks that use non-emotional stimuli, such as the color-word Stroop task or the n -back task with letters or neutral words. “Hot” CC can be measured with similar paradigms but using emotional stimuli (Fig.  1 ), thus assessing control over motivational or emotional information. For example, hot Stroop tasks might require naming the font color of emotionally salient words (e.g., “failure”) [ 48 ] or categorizing the emotional valence of words (e.g., “miserable”) in the context of faces with emotional expressions that conflict with those words (e.g., [ 49 ]). Hot CC can also be measured with tasks that do not have a cool analogue, such as gambling tasks and delay of gratification tasks [ 50 , 51 ] (Fig.  1 ). Research with children suggests separability of hot and cool CC in terms of their relations with each other and with other measures [ 50 , 52 , 53 , 54 , 55 , 56 , 57 ]. However, neuroimaging studies comparing CC tasks with non-emotional and emotional stimuli find that they involve similar CC regions [ 58 , 59 ] (dorsal ACC, anterior insula, and lateral and medial PFC), but tasks with emotional conflict also recruit distinct neural regions related to salience and emotional processing (amygdala, more rostral areas of the ACC and medial PFC, and orbitofrontal cortex) [ 60 , 61 , 62 , 63 , 64 , 65 , 66 ]. Such patterns suggest that there may be common CC processes across hot and cool tasks.

Box 1 Alternative candidate components (or taxonomies) of CC

Miyake et al.’s [ 33 ] model recognized that there could be other potential, separable CC constructs, besides the traditional triad of working memory, cognitive flexibility, and inhibition. How other candidate processes such as (attentional) monitoring; dual-tasking; strategic retrieval and generativity (including e.g., verbal fluency and episodic memory) [ 233 , 247 , 248 ]; “compositionality” [ 70 ]; self-report (e.g., impulsiveness); and metacognition [ 249 ] including social aspects (e.g., “theory of mind”), might relate to or derive from the original triad is unclear. Studies that have tested the relations of some of these candidate CC components have found that they are correlated with the more commonly examined CC processes but also show some separability [ 233 , 247 , 248 ]. Thus, the notion of unity and diversity is likely to apply to models that include more than the most typically examined three constructs.

One rough parcellation of alternative CC components has been achieved by anatomical localization in a large number of patients with frontal injuries [ 250 , 251 , 252 ]. The tasks included requirements to attend, switch, be vigilant, tap rhythmically, and respond quickly. Superior medial deficits were associated with “energization” (initiating and sustaining a response, problems with which were related to slower reaction times). Right lateral lesions were associated with “monitoring” (checking performance and adjusting behavior when necessary). Left lateral lesions were associated with “task setting” (setting up a stimulus-response relationship and organizing the processes necessary to complete a task), especially their acquisition and flexible use; and, for the inferior medial group, a problem of maintaining task set (possibly related to distractibility). These proposed CC components have only an approximate relationship with those discussed earlier (typically examined in latent variable models), and intriguingly do not appeal at all to any construct of “inhibition.” Rather, Stuss and Alexander [ 250 ] proposed that inhibition emerged as a combination of these three processes.

Fractionation and integration of CC within PFC

The main methodologies employed for examining how PFC mediates CC have been (i) the anatomical localization of specific aspects of CC/EF, based for example on evidence of lesions in conjunction with correlative neurophysiological or neuroimaging methods; and (ii) the analysis of task performance using the mapping of neural network methodology aimed at elucidating the sequencing and overall integration of CC processes. With respect to the latter, resting state and functional connectivity data suggest several distinct configurations (networks) of PFC and other brain regions, reviewed by Menon and D’Esposito (this issue) [ 16 ]: the lateral “fronto-parietal” (or “central executive”) network (FPN), anchored in the dorsolateral (dl) and dorsomedial PFC and posterior parietal cortex; the “cingulo-opercular” network (CON), which overlaps with a “salience” network and includes the ACC, the insula, and subcortical regions; the “ventral attention” network, which includes inferior frontal gyrus, regions of the insula, and the temporoparietal junction; the “dorsal attention” network, which includes the frontal eye fields and intraparietal sulcus; and the “default mode network” (DMN) comprising medial PFC regions interacting with certain posterior cortical regions (Haber et al., this issue; Menon & d’Esposito, this issue [ 15 , 16 ]). The DMN typically shows inverse levels of activity in relation to the other networks during external task performance, with the DMN being more active at rest, and consequently associated with “internal” control processes (Menon & D’Esposito, this issue [ 16 ]).

With respect to the neurobiological substrates of unity and diversity, one view would emphasize the participation of the PFC as a hub of an MD network that mediates all of the common facets of CC. Another would point to the cytoarchitectonic heterogeneity of the PFC (Fig.  2 ) and ask how the various components of CC were coordinated and integrated in different tasks by different PFC regions, and their specific roles as functional nodes within networks. The relevant circuitries include connectivity of the PFC to posterior cortical regions such as the parietal lobes and to subcortical regions, such as the striatum. Hybrid models of organization may incorporate MD characteristics in certain PFC regions, but also allow for specificity of neural connectivity, to mediate, for example, specific aspects of response inhibition, updating, or cognitive flexibility, as well as other forms of CC that putatively involve, for example, interactions with language systems and autobiographical episodic memory. An important consideration is the extent to which hierarchical CC processing maps onto a hierarchical lateral PFC system and how motivational processes interact with it to achieve integration of these dual forms of control [ 67 ]. The following sections selectively survey findings from the enormous literature on these issues (see also reviews by Tanji & Hoshi [ 68 ] and Badre [ 69 ]).

Fractionation of CC

Evidence from the double dissociation of component CC processes is relevant to the question of whether PFC’s role in CC is unitary. By the time of the classic edited book, The Frontal Granular Cortex and Behavior [ 70 ], lesion studies in non-human primates had already shown, via the double dissociation strategy, considerable apparent localization of function within the frontal lobes. For example, whereas impairments in working memory test paradigms such as spatial delayed response were produced by lesions of the sulcus principalis (dlPFC), damage to more ventrolateral and orbitofrontal regions produced impairments in tests apparently measuring inhibition and cognitive flexibility, such as reversal learning and Go/No-Go responding. These findings apparently provided strong evidence against a unitary system.

In the case of working memory , the lateral frontal cortex in primates, already implicated in the spatial delayed response task, was known to contain cells that exhibited activity in delay periods [ 24 ] in response to stimuli in a number of sensory modalities. Goldman-Rakic [ 71 ] in particular suggested that the dlPFC (BA-46 /sulcus principalis) mediated the maintenance of spatial information in memory in preparation for action. Subsequent work queried whether the maintenance of information per se was a critical PFC function. Thus, it was shown from other electrophysiological evidence as well as human functional imaging that the anterior inferotemporal/perirhinal and parietal cortex also exhibited maintenance operations [ 72 ], although the dlPFC did appear to have important roles in resisting interference (e.g., distraction) in working memory [ 73 ]. Moreover, other investigators (e.g., [ 74 ]) have interpreted the role of the “mid-dorsal” (BA-9/46) PFC to mediate CC processes, such as the monitoring or tagging of recently selected items, as in self-ordered or n -back tasks, rather than the passive maintenance of information (see also [ 75 ] for a meta-analysis). By contrast, damage to a different sector of more posterior dlPFC (BA-8) impaired selection of motor responses to particular stimuli (conditional learning of task sets) but such performance was not affected by BA-9/46 damage. This type of double dissociation further supports the hypothesis that distinct processes of CC in lateral PFC are mediated by different regions and is relevant to hierarchical theories of CC, considered below.

There are also distinct functions within nodes of the FPN, where some of the “manipulation” sub-processes of working memory are mediated by parietal cortex, for example, including representations and operations relevant for mental arithmetic [ 76 ] and mental rotation [ 77 ]. By combining fMRI methods with the measurement of evoked response potentials (ERP), it is possible to track the time-course of FPN control processes: Frontal dipoles contributed prior to parietal dipoles in a task involving updating working memory to bias processing of stimulus-response mappings mediated by the parietal cortex [ 78 ].

Theories relating basic processing of external stimulus features for immediate action to future planning functions involving working memory for sequential actions or branching rule contingencies have suggested a linear hierarchy of control operations in the FPN, with the most abstract levels represented by the most anterior PFC structures, i.e., in fronto-polar regions [ 67 , 79 ]. This hierarchical scheme, supported by evidence from functional neuroimaging and dynamic causal modeling, postulates the caudal lateral PFC to be responsive to external stimulus features, the mid-lateral PFC to contextual rules for attention, and the most rostral parts of lateral PFC to implement rules from working memory. However, causal analyses employing theta-burst transcranial magnetic stimulation (TMS) to reduce cortical excitability of infra-PFC connections have suggested that the “hub” or “apex” where these control influences over attention to the present, external world and the future, “internal” one, are integrated in mid-lateral, not rostral PFC [ 80 ].

With respect to cognitive flexibility , one of the classical tests in humans, the Wisconsin Card Sort Task (WCST; see Fig.  1 ), originally implicated both OFC and dlPFC [ 81 ], on the basis of lesion studies in humans and monkeys. A modern lesion study [ 82 ] in the marmoset showed that excitotoxic, cell body (i.e., fiber-sparing) lesions to the OFC (BA-13) and to the lateral PFC, BA-12/47) produced a clear double dissociation between two distinct tests commonly associated with cognitive flexibility (or inhibitory control): Reversal learning was robustly impaired by the OFC lesion (BA-11), and extra-dimensional set-shifting (as occurs in the WCST) by ventrolateral (vl)PFC (BA-12/47) lesions. (An apparently similar neural dissociation of these deficits has subsequently also been shown in both rats and mice, as well as in humans [ 83 ]). Moreover, other studies revealed that the set-shifting deficit occurred in the absence of any obvious “on-line” working memory impairments [ 84 ].

These studies suggest dissociation, not only between elements of CC, but also even within the domain of cognitive flexibility. The findings are also compatible with the hypothetically hierarchical organization of PFC function by which reversing contingencies for objects based on changes in value are at a lower level than flexibly attending to perceptual dimensions or categories. There is also evident task impurity in the former case; impairments in reversal learning could have resulted from deficits in the processing of negative feedback or the value of objects rather than cognitive flexibility per se.

Functional neuroimaging studies in humans confirm that these two tests of cognitive flexibility implicate different PFC regions, lateral PFC in the case of set-shifting (compared with intra-dimensional shifts) and OFC regions in the case of reversal learning occurring after negative feedback [ 85 ]. Additional analyses in that study suggested that the parietal activations occurred when previous stimulus-reward mappings needed to be overwritten and dlPFC activation at all phases of the task involving new solutions, thus providing a fractionation of the neural regions implicated in attentional control. Another, resting state, study of patients with OCD and healthy controls showed that the ability to perform the extra-dimensional shift task was related to functional connectivity between the ventrolateral PFC and the caudate nucleus, whereas performance of a visuospatial planning task implicated activity in a distinct fronto-striatal pathway [ 86 ]).

Whereas set-shifting and reversal learning may depend on learning from reinforcing feedback, switching rapidly between established stimulus-response mappings or task sets may produce switch costs. The latter are caused by the required reconfiguration of task sets and interference between them, and exaggerated by damage to PFC regions, especially to the right and left inferior frontal cortex [ 87 ]. Functional neuroimaging studies with several forms of task set switching that isolated perceptual versus response-related aspects of switching highlighted the right inferior junction and posterior parietal cortex as domain general zones for switching [ 88 , 89 ]. dlPFC was more implicated specifically in response switching, and posterior frontal regions (e.g., premotor cortex) in perceptual switching, with a familiar caudal-rostral PFC gradient of increasingly abstract switching rules [ 89 ]. Thus, mechanisms underlying cognitive flexibility overlap anatomically with those of updating working memory to some extent, but likely also depend on some specialized circuitry [ 90 ].

To examine response inhibition , Aron et al. [ 91 ] applied the lesion approach method to a single paradigm, the stop-signal task [ 92 ], in group of patients with variable volumes of damage to different sectors of the PFC. They showed that the only sector correlating with stop-signal reaction time was the right inferior frontal gyrus (RIFG; including BA-44 and BA-45, especially pars opercularis). Go reaction time, for example, was more related to damage to other PFC regions. This result was of theoretical significance, as the stop-signal task can readily be considered to measure response inhibition, although, like virtually all other tests of CC, it is impure and also incorporates attentional components. A good deal of evidence from a variety of methodologies, including fMRI and disruptive TMS, has supported this general correlation of RIFG dysfunction with impaired response inhibition [ 93 ], but the question has remained whether this region specifically mediates response inhibition or other aspects of performance (e.g., [ 94 , 95 , 96 , 97 ]). It is notable, for example, that patients with lesions in this area also exhibit deficits in spatial working memory performance [ 98 ], consistent with the MD model of lateral PFC function.

The issue of anatomical specificity matching cognitive specificity for stopping inhibition has been addressed from a variety of perspectives. A meta-analysis of the large number of relevant fMRI studies has shown two main peak BOLD activations, one within the insular cortex, apparently coincident with initial processing of the STOP signal, and a subsequent peak focused more in the RIFG, and plausibly associated with the production of the response. A possible solution of the issue may depend on sophisticated network analyses [ 99 ]. Early on, Aron and Poldrack [ 100 ] proposed a specific circuit for response braking that included the hyperdirect projection from RIFG to the subthalamic nucleus (STN), so it could perhaps be argued that among its various functions, via part of its extensive pattern of connections, the “hub” that is the RIFG has specific “spokes” that mediate relatively specific components of CC. An analysis of effective connectivity among PFC regions during stop-signal task performance revealed that the best selected Bayesian model allowed the RIFG to modulate an excitatory influence of the pre-SMA on the STN, thereby amplifying downstream polysynaptic inhibition from the STN to the motor cortex. Diffusion tensor imaging of the white fiber connectivity of these structures validated these conclusions and predicted individual differences in stopping efficiency [ 101 ]. To show CC specificity would require double dissociations of the dynamics of network action of this type, but this analysis clearly identifies highly specific interactions among frontal regions and an important modulatory role for the RIFG “hub.”

In the stop-signal task there is also a right frontal electrophysiological signature of increased beta power for successful versus stop trials, which is matched by a similar signal during a requirement to stop an unwanted thought coming to mind [ 102 ]. This match raises the possibility that the right lateral frontal cortex controls a general inhibitory mechanism that does not simply brake actions, but can also inhibit cognitive and emotional outputs [ 103 ]. The latter study [ 103 ] found that the right medial gyrus contained three nodes of activation that mediated cognitive and emotional inhibitory effects at the more anterior sites and motor inhibition more posteriorly, interacting with the RIFG. The emotional inhibition task engaged the OFC and amygdala, whereas a think-no-think task involved the hippocampus, congruent with the work of Anderson [ 104 , 105 ], who has consistently shown that memory retrieval can be inhibited by a dlPFC pathway to the hippocampus, via relays in the mid-temporal lobe or retrosplenial cortex (see Anderson & Floresco, this issue [ 106 ]). Overall, there is increasing evidence for parallel, top-down inhibitory systems over a range of behavioral and cognitive responses, somewhat lateralised to the right hemisphere. The reasons for this lateralization are not currently clear, but could relate to the lateralization of language to the left hemisphere, or possibly to lateralization of some emotional functions to the right. Existing evidence suggests that the complementary left inferior frontal cortex regions play a role in semantic memory retrieval [ 107 ], as well as constituting Broca’s area (BA-44/45).

In the case of motivational control (“hot” vs “cool” CC) , perhaps the most striking dissociations for human patients with frontal lobe injury were cases of everyday decision-making in the absence of obvious impairment in IQ or conventional neuropsychological testing of classical “frontal” deficits [ 108 ]. Subsequent work established that such deficits were caused by extensive damage to the orbitofrontal and ventromedial (vm)PFC, extending into the frontal pole (BA-10) [ 109 , 110 ]. Such patterns may indicate a clear division between “hot” and “cool” cognition, the latter comprising what could be termed as CC. By contrast, the vmPFC, an often ill-defined area potentially comprising several distinct cytoarchitectonic regions (Fig.  2 ), is commonly associated with a “goal-directed” system [ 7 ].

Although working memory components incorporate such notions as “goal maintenance,” it is important to consider how PFC circuits mediate the associative learning and monitoring of instrumental behavior, leading to such goals or response outcomes, including their valuation. Such motivational and evaluation functions are the province of PFC regions including the OFC (BA 11,12,13,14) and ACC (BA 24 and 32) ([ 7 ]; see also reviews by Monosov & Rushworth, this issue, and Rudebek & Izquierdo, this issue [ 111 , 112 ]), as well as the neuromodulatory influences of the ascending monoaminergic systems (Cools & Arnsten, this issue [ 113 ]).

Early notions of a role for the ACC in inhibition of prepotent dispositions and error monitoring in CC theory have more recently been supplanted by considerations of effort, choice difficulty, and adaptive coding of response outcomes, relevant to flexible foraging behavior and the exploration of alternative choices [ 114 ]. ACC activity is enhanced under conditions not only of conflict, but also cognitive effort and choice difficulty [ 115 ]. Moreover, studies using fMRI and electrophysiological (error-related negativity) methods in humans, as well as single-unit electrophysiology in experimental animals, have identified the ACC to be a site of computation of prediction errors encoding the difference between expected and obtained outcomes of responding [ 115 , 116 , 117 ].

It has proven to be a difficult task to accommodate all of these empirical phenomena in one computational model; how therefore can such a diversity of functions most parsimoniously be explained? Notable have been attempts to incorporate effortful and cost-benefit factors, as in the Expected Value of Control model [ 118 ] and the extension of the conflict monitoring notion to decision-making between options of similar value in the Choice Difficulty model [ 119 ] (see also Collins & Shenhav, this issue [ 120 ]). By contrast, the Predicted Response Outcome model [ 117 ] uses as its basis unsigned prediction errors of any type, whether appetitive or aversive outcomes, and whether negative (unexpected omission of expected outcome) or positive (unexpected occurrence of outcome) “surprise,” over multiple time-scales to directly affect actions (and not stimulus representations). This model thus accommodates observations of multiple signals in this region concerning outcomes of actions based on appetitive reward as well as aversive pain [ 114 ] and explains non-prepotent responses on the Stroop and errors as being less expected events. Further extensions to the model explain how this information can lead to behavioral adaptation during decision-making and to both proactive and reactive CC [ 121 ], in terms of anticipatory adjustments to responding on one hand, as in risk-avoidance and foraging behavior, and to error-induced slowing caused by negative surprise and the temporary invigoration of responding (“hot-hand”) of repeated positive surprise in association with rewarding feedback [ 122 ] on the other. The latter computational model has recently been suggested to capture the most important functions of the entire PFC [ 122 ].

A recent fMRI test of speeded value-based decision-making that could distinguish amongst the predictions made by the various models has favored the Predicted Response Outcome model over the Expected Value of Control and Choice Difficulty alternatives [ 123 ]. However, the Predicted Response Outcome model has a characteristic signature in several other brain regions, including the lateral PFC and parietal cortex, that pose questions about how signals from the ACC are relayed to other regions of the brain, including to the fronto-parietal axis, the striosomes of the striatum, and the noradrenergic locus coeruleus, with diverse applications. Moreover, it is possible that the differing strengths of the models may be reflected by anatomical differentiation within what is a large, quite heterogeneous anatomical region. Thus, choice difficulty has been related to a more dorsomedial region close to the pre-supplementary motor area and pain to more ventral regions of the ACC [ 114 , 122 ].

Neurochemical modulation of CC

CC has to occur in the context and history of different motivational states including those produced by prior stress and learning, mediated in part by phasic and tonic changes in the ascending monoaminergic and cholinergic neurotransmitter systems. For example, dopamine receptors in the PFC have been related hypothetically to three main elements of CC: gating; maintaining and relaying motor commands; and producing error learning signals [ 116 , 124 , 125 ].

An extensive literature in human and experimental animals has shown that CC is susceptible to pharmacological intervention and hence to neuromodulation by the ascending monoaminergic and cholinergic systems [ 126 , 127 ] (see also Cools & Arnsten, this issue [ 113 ]). For example, catecholaminergic drugs such as methylphenidate and atomoxetine, or manipulations affecting the dopaminergic and noradrenergic neurotransmitter systems, can affect several aspects of CC, including working memory, cognitive flexibility, and response inhibition. However, drug effects are generally dose-dependent and conform to a familiar inverted U-shaped function. Moreover, different tasks reflecting different components of CC may be affected in dissociable ways. For example, dopamine depletion in the marmoset PFC impaired spatial delayed response whilst enhancing extra-dimensional shifting [ 128 ]. Floresco [ 129 ] reviews data suggesting that dopamine D1 receptors are more implicated in working memory, whereas D2 receptors promote cognitive flexibility in rodents. In patients with Parkinson’s disease, therapeutic doses of L-Dopa appear to improve working memory and task-set switching but impair reversal learning and decision-making [ 130 ].

On the other hand, serotonin depletion of the OFC in marmosets impaired reversal learning without affecting extra-dimensional set-shifting [ 131 ]. In human studies, the noradrenergic reuptake inhibitor atomoxetine enhanced stop-signal performance but had no effect on probabilistic learning known to engage OFC mechanisms, whereas the selective serotonin reuptake inhibitor citalopram had the reverse pattern of effects [ 132 ]. These findings indicate a degree of specificity in how these ascending transmitter systems interact with PFC–striatal networks and also raise the possibility that the motivational states mediated by activity in these systems may differentially prime different aspects of CC. The possibility of “top-down” or local control of the cholinergic system [ 133 ] or the catecholamine system [ 134 ] by PFC circuitry may also provide mechanisms for the allocation of “cognitive effort”.

Integration of CC

Despite the evidence for fractionation (“diversity”) of CC, there is also considerable evidence for overlap (“unity”) across separable CC/EF processes. As described earlier, at the behavioral level, this unity can be observed in the correlations among EF latent factors, which enables estimation of a “Common” CC factor (Fig.  3 ). At the neurobiological level, this unity can be seen in the overall patterns of neural activations during CC tasks [ 135 , 136 , 137 ]: Neuroimaging studies suggest that individuals performing these kinds of tasks generally activate some of the same brain regions across multiple tasks, although they also activate some regions that are unique to a particular task.

Conjunction maps of activation during different tasks (e.g., during inhibiting, updating, and shifting tasks), either with data estimated within one study [ 135 , 138 , 139 ] or with meta-analytic techniques [ 137 , 140 , 141 ], suggest that both children and adults recruit a common set of regions within the FPN and CON during diverse EF tasks. The FPN and CON are functionally separable brain networks that enable flexible adaptive control and sustained task-set maintenance [ 142 ]; together, they form the MD network [ 143 ], a set of brain regions that is active during a range of tasks that require goal-directed behavior [ 139 ]. For example, Niendam et al.‘s [ 137 ] meta-analytic study of 193 neuroimaging fMRI studies of EF showed broad patterns of activation across the lateral and medial PFC, including regions BA-9 and BA-46 (dlPFC) and BA-32 (rostral ACC), as well as both superior and inferior regions of the parietal lobes for tasks with predominantly working memory, flexibility, or inhibitory components.

However, despite overall conjunctions of activation, there may be subtle differences among tasks that also account for diversity. In Niendam et al.‘s [ 137 ] meta-analysis, flexibility tasks alone activated BA 11 and a sub-class of tasks involving initiation did not activate parietal regions. A similar conclusion was arrived at by Fedorenko et al. [ 139 ] using a more refined design by which individual subjects were shown to exhibit overlapping activation in a variety of tasks including verbal and spatial working memory, arithmetic, Stroop, and attentional tasks. The concept of diversity as well as unity of PFC functions can be illustrated in a typical fMRI study [ 144 ] which had defined task components of response inhibition and cognitive flexibility, finding increased BOLD activity in the FPN including both the RIFG region and in the parietal cortex. However, whereas the response control-related activity was greatest in the RIFG, the opposite was the case for attentional shifting; hence the different nodes of the FPN as well as functioning as part of a network, evidently had different roles within that network. The FPN can be regarded in some sense as a domain-general system that flexibly connects with other networks depending on the nature of the task, but the various dissociations reviewed also highlight how CC can be deployed in specific ways, perhaps capturing diversity as well as unity of PFC function.

Although there are clearly common neural areas recruited by diverse CC tasks, an open question is whether these same areas are involved in individual differences in performance of these tasks. That is, most people activate the FPN during demanding tasks, but do performance differences across tasks systematically relate to how strongly individuals activate the FPN or its nodes during these tasks? Few studies have looked at conjunction maps of areas related to individual differences in performance, but one study that did so did not find areas of significant overlap across three CC tasks, despite significant areas of mean activation across those tasks [ 138 ].

Studies that have correlated individual differences in Common CC ability with a neural measure, such as functional activation of a particular region during a task, functional or structural connectivity, or gray matter volume, often find that areas within but also outside of the MD network contribute to performance [ 145 , 146 , 147 , 148 ]. A meta-analysis of healthy adults [ 149 ] found that better performance on individual CC tasks was associated with larger PFC volume and greater PFC thickness, particularly for lateral PFC, although there was significant heterogeneity in the strength of association that was related to the particular tasks examined as well as sample age variability. Examining a group of healthy college-aged young adults, Smolker et al. [ 147 ] found that a Common CC factor was related to changes in gray matter and cortical folding in the broad vmPFC area, typically associated with the goal-directed system, whereas the specific aspects of updating of working memory and cognitive shifting were related specifically to dlPFC and vlPFC, respectively. However, it is notable that using a latent factor analysis in concert with structural imaging, there was only limited evidence of a major involvement of fronto-parietal activity for the Common CC factor.

A later study [ 148 ] in a large group ( N  = 251) of healthy adults approximately a decade older, and employing a larger test battery, did not confirm all of these associations, possibly because of an important developmental factor. In this study, Common CC was related to greater volume of the right middle frontal gyrus/frontal pole and to fractional anisotropy of the right superior longitudinal fasciculus, connecting the frontal lobes to other regions of posterior neocortex, including the parietal lobes. Updating-specific ability was linked to gray matter changes in regions both within and outside the fronto-parietal axis. In contrast, Shifting-specific ability implicated widespread white matter changes, as measured by mean, radial and axial diffusivity measures. In a similarly aged sample (ages 22–35) from the Human Connectome Project, Lerman-Sinkoff et al. [ 150 ] examined correlates of a Common CC composite using a data-driven approach (independent components analysis) to reduce multimodal imaging data. They found that CC was related to two components, one of which included variation related to visual network activity and insular gray matter volume, and the other of which included activity of the FPN and gray matter thickness in the CON [ 150 ].

Taken together, these results suggest that individual differences in Common CC are linked to structural and functional characteristics of the brain that include the FPN and CON, but also networks related to lower-level processes such as the visual network. Stronger global or specific connectivity of the dlPFC to other regions throughout the brain has been associated with better performance on diverse CC tasks or factors [ 151 , 152 ]. As such, dlPFC seems to affect individual differences in performance through its influence on other areas within and outside of the FPN.

Finally, a number of recent studies that have examined variations in structural and functional connectivity also suggest that more large-scale properties (as opposed to region-specific or even network-specific properties) may relate to Common CC. A large study of participants aged 8–22 years [ 153 ] found that higher scores on a CC factor were related to higher modularity of white matter brain networks (stronger connections within networks and weaker connections between networks), particularly the FPN, and modularity mediated age-related increases in CC scores. More modular, segregated networks may allow for more specialization and less interference across brain networks. CC scores were also positively associated with global efficiency, a measure of how quickly information can flow across networks. These results suggest that higher Common CC is associated with brain structures characterized by both more specialization of networks but also greater coordination across networks. Moreover, the extent to which brain network connectivity changes in response to cognitive demands is associated with better performance on different CC tasks [ 154 , 155 ], suggesting that task-based neural flexibility may facilitate Common CC through adaptive control [ 156 ].

The robust evidence for shared variance and common patterns of neural activations across CC tasks begs the key question of what cognitive process(es) comprise the “unity” of CC. There are a number of proposed mechanisms for the Common CC factor, which have both informed and been informed by the neuropsychological literature.

One proposed mechanism for the Common CC factor is inhibition , a mechanism apparently consistent with a strong relationship between the Common CC factor and the response inhibition factor that has been observed in several studies: When a higher-order common factor is used to model the correlations among response inhibition, working memory updating, and mental set shifting (see Fig.  3b ), that common factor almost perfectly predicts the inhibition factor [ 37 , 38 , 157 , 158 ], but there is significant variance in the updating and shifting factors that is not related to the common factor. Similarly, when the correlated factors model is re-parameterized into a bifactor model (see Fig.  3c ), there are updating-specific and shifting-specific factors, but there is no evidence for a response inhibition-specific factor [ 37 , 38 , 157 ]. In other words, the variance that is shared across response inhibition tasks is the same variance shared across all tasks (response inhibition, working memory updating, and mental set shifting). Broadly speaking, this pattern suggests that, at the level of individual differences, unity (what is common to CC processes) may be isomorphic with response inhibition, whereas diversity is evident in additional processes associated with updating working memory and mental set shifting.

This pattern of little inhibition-specific variance could be interpreted as indicating that what is common to CC abilities is inhibition. That is, one could describe most if not all CC processes as requiring some sort of inhibition [ 159 ]. For example, updating working memory tasks could be characterized as requiring inhibition both to stop irrelevant information from entering working memory and to remove no-longer-relevant information from working memory when appropriate [ 160 ]. Similarly, mental set shifting tasks could be characterized as requiring inhibition to ignore information irrelevant to the current task set [ 161 ], as well as to suppress the no-longer-relevant task set when switching sets [ 162 ].

However, this characterization relies on the assumption that processes described with similar terms (such as “inhibition”) are in fact similar, an assumption that may not be valid [ 163 ]. Even though the same inhibition term is used to describe these requirements, those processes may be dissociable [ 161 , 164 ], and in some cases, may not involve inhibition (i.e., neural inhibition) at all [ 165 , 166 ].

An alternative proposal is that the unity component reflects individual differences in the ability to actively maintain goals and use those goals to bias ongoing processing [ 29 , 97 , 167 ]. To perform well in all CC tasks, participants must have accurate representations of the task goals that can be used to direct attention to task-relevant information, particularly when there is conflicting task-irrelevant information. In some cases, participants must also monitor the environment for conditions that signal that the goal is relevant. For example, in stop-signal tasks, the stop goal is only relevant on a subset of trials in which a signal occurs, so performance may partially depend on being able to quickly recognize the relevance of the signal and stop the response [ 95 ]. According to this proposal, individual differences in response inhibition tasks may be particularly related to this ability because if a goal is inactive or ineffective, then more automatic or prepotent responses will take over, leading to poor performance on these tasks. If response-inhibition-specific neural processes, such as global motor suppression [ 93 , 168 ], do not show large individual differences, then task performance may be more driven by whether those processes are triggered in the first place. To the extent that keeping goals highly active and proactively biasing ongoing processing influences stopping, performance may be more determined by these global processes rather than inhibition-specific ones [ 29 , 97 , 169 ].

This proposal that unity captures goal maintenance and biasing [ 29 ] is in fact a classic conception of CC and frontal lobe function [ 8 , 170 , 171 ]. In addition to proposing that CC involves the active maintenance of goals in the PFC that bias processing elsewhere in the brain, Miller and Cohen [ 8 ] argued that this key function of PFC is responsible for various aspects of CC, such as selective attention, response inhibition, and working memory. Seen from an individual differences perspective, they essentially proposed a “common” CC ability that depended on such goal maintenance and bias.

Specifically, building on Desimone and Duncan’s [ 170 ] model of such competitive dynamics during visual attention, Miller and Cohen argued that prefrontal goal representations enable weak stimulus-response mappings to out-compete more habitual ones when appropriate: Goal representations bias competition by boosting activation for task-relevant processing, which, by virtue of lateral inhibition, suppress activity of competing representations. In this sense, “inhibition” could be seen as fundamental to common CC ability. Yet, this goal maintenance and biasing account of common CC ability is conceptually different from accounts that invoke a broader inhibition mechanism, discussed earlier.

The goal maintenance/biasing perspective is incorporated into several other CC frameworks, such as the executive attention framework [ 172 ] and the dual mechanisms of CC framework [ 121 ]. Duncan and colleagues’ MD framework also prominently incorporates a goal-maintenance/biasing perspective. They characterized the unity of frontal lobe functions in terms of goal-related processes, specifically the ability to form and carry out goals at multiple levels of abstraction [ 32 ]. Failures in this ability can manifest as goal neglect, a phenomenon commonly observed with head injury, as also noted by Teuber [ 31 ]. Duncan et al. [ 32 ] found that goal neglect was more related to general brain atrophy than focal frontal lesions, and Duncan [ 11 ] later linked these general goal-related processes to the MD network a network of frontal and parietal regions that is commonly activated across tasks.

Duncan and colleagues [ 11 , 32 ] have also linked goal neglect and the activity of the MD network with general fluid intelligence, leading to the last interpretation of the unity component we consider here: that it recapitulates intelligence or Spearman’s g . Indeed, a large body of work has documented moderate to large correlations between intelligence measures, particularly measures of fluid intelligence (such as reasoning) with measures of CC. Perhaps most relevant, studies that have measured a common CC latent factor have reported correlations with intelligence ranging from r  = 0.53–0.91 [ 41 , 158 , 173 , 174 ]. Such correlations suggest that the unity of CC is related to intelligence or g . However, at least in adult samples, this correlation is only moderate, and is significantly lower than 1 ( r  = 0.53–0.68 [ 41 , 158 ]), indicating that these constructs cannot be considered identical, even when examined with latent variables. Moreover, CC and intelligence seem to show discriminant predictive validity of behavior, in that CC is associated with problems related to attention-deficit/hyperactivity disorder (e.g., [ 175 , 176 ]) or lack of self-restraint [ 177 ], even when controlling for intelligence.

In addition to correlating with common CC, intelligence also significantly correlates with the variance that is unique to working memory processes (working memory updating and/or capacity) [ 41 , 158 ]. Such results suggest that although the CC unity component may reflect some of the same processes tapped by intelligence measures, common CC is not equivalent to intelligence. Rather, intelligence may be related to both common CC and working memory-specific processes, consistent with earlier research showing that intelligence and particularly reasoning ability are strongly related to working memory capacity [ 178 , 179 ].

Clinical implications

Many psychiatric and neurological disorders are associated with specific symptoms that may be at least partly a product of impaired CC, or with more general cognitive deficits that accompany the specific symptoms. For example, Attention-Deficit Hyperactivity Disorder (ADHD) has major EF/CC impairments in attentional control, working memory, and response inhibition that contribute to DSM-5 symptoms of distractibility and impulsivity. Similarly, some symptoms of major depressive disorder include problems of decision-making and concentration, which appear to entail primary CC impairments. In schizophrenia, negative symptoms have been related to impaired goal-directed behavior [ 180 ] and positive symptoms such as delusions and hallucinations to deficits in reality monitoring [ 181 ], although there is an additional domain of symptoms in schizophrenia of cognitive impairment that includes major working memory deficits and impedes rehabilitation [ 182 ].

One hypothesis concerning addiction is that it results from a general impairment in goal-directed behavior, leading to more pronounced habitual tendencies, and exacerbated by a loss of top-down control, to contribute to compulsive drug-seeking [ 183 ]. An analogous mechanism has been proposed to account for other forms of compulsive behavior, such as checking or washing in obsessive-compulsive disorder [ 184 ]. However, it should also be recognized that hyperactivity in medial PFC regions in such disorders (Ahmari & Rauch, this issue [ 185 ]) could potentially be associated with specific compulsive behaviors that retain their goal-directness. Moreover, it is possible that a simple dichotomy between goal-directed and habitual behavior is too simple. A recent computational formulation [ 186 ] has suggested an intermediate type of control mode relying on model-based and model-free computations guided by “successor representations’“ that enable behavior to be both flexibly goal-directed but also efficiently model free.

Although the precise contribution of dysfunctional CC mechanisms to psychiatric and neurological symptoms, perhaps in combination with altered perceptual and motivational processes, remains to be determined, at a general level, it is clear that CC deficits are characteristic of a wide range of disorders. Indeed, meta-analyses suggest transdiagnostic associations of CC deficits with psychiatric disorders, including major depressive disorder, post-traumatic stress disorder [ 187 ], obsessive-compulsive disorder [ 188 ], bipolar disorder [ 189 ], schizophrenia [ 190 ], ADHD [ 191 , 192 ], conduct disorder and antisocial personality disorder [ 193 ], and substance use disorders [ 194 ].

Unity and diversity of psychopathology in relation to CC

Although such psychiatric and neurological disorders are often treated as distinct entities, a growing body of work has focused on the observation that these disorders share considerable variance [ 195 ]. That is, whether treated as dimensional or categorical constructs, different disorders are often comorbid, either concurrently or sequentially across the lifespan [ 20 , 195 ]. This common variance occurs at multiple levels of specificity. At one level, particular disorders can be clustered into internalizing (depression and anxiety), externalizing (antisocial behavior and substance use), and thought disorder (schizophrenia, bipolar disorder, obsessive-compulsive disorder) factors. At a higher-order level, these internalizing, externalizing, and thought disorder factors correlate with each other, and these correlations can be modeled with a general psychopathology factor [ 20 , 195 , 196 ]. This hierarchical general factor has been dubbed the “ p factor” in recognition of its parallel to the g factor for cognitive abilities [ 197 ]. The p factor has been modeled in a number of datasets, and shows longitudinal stability and criterion validity, in that it predicts a number of clinical outcomes [ 195 , 196 ].

However, like any statistical factor, it describes a pattern of correlations but not an explanation of those correlations. That is, its neurobiological underpinnings are not well understood and its psychological interpretation varies [ 20 , 195 ]. For example, the p factor has been proposed to reflect negative emotionality, disordered thinking, and/or poor CC, particularly impulse control (inhibitory control) over positive and negative emotions [ 2 , 20 , 195 , 198 ].

With respect to CC, the transdiagnostic associations of CC with psychopathology support the notion that the p factor may partly reflect CC deficits [ 1 , 2 , 197 ], although there may also be specific CC deficits associated with particular disorders or clusters of disorders (e.g., [ 197 , 199 ]). Moreover, within many disorders, it appears that multiple aspects of CC (inhibition, shifting or flexibility, and working memory processes) are impaired [ 1 ], though possibly to different extents. These patterns suggest that CC impairments associated with psychopathology may be general, reflecting variance that is shared across multiple CC constructs [ 1 ]. Indeed, several studies have examined this hypothesis directly, finding that a common CC factor is associated with a p factor ( r  = –0.16 to –0.56) [ 174 , 200 , 201 , 202 , 203 ].

Studies of neural correlates of psychopathology also suggest the importance of CC-related regions of interest and networks. In particular, multiple psychiatric disorders are associated with hypoactivation of the FPN and CON during CC tasks [ 2 , 204 ], alterations in functional connectivity of these networks at rest [ 3 , 205 ], and alterations in gray matter volume in nodes of these networks, including the dorsal ACC, insula, and dorsomedial and vmPFC [ 2 , 3 , 206 ]. When integrated with findings that these same patterns are associated with poorer performance on CC tasks [ 2 , 3 ], these results are consistent with the conclusion that CC and mental health share neural substrates, and that disruptions of these neural substrates may account for increased p and decreased common CC functioning [ 2 , 3 ].

Links between impulse control, CC, and psychopathology

Many PFC areas are particularly associated with control over emotionally relevant information (hot CC) [ 60 , 61 , 62 , 63 , 64 , 66 ], which, as discussed earlier, show some dissociations from cool CC. These associations are consistent with interpretations of the p factor that focus on emotional regulation, particularly impulse control in the context of high arousal (both positive and negative emotion) [ 198 ]. At the behavioral level, such emotional impulse control is often measured with self-report measures of impulsivity and emotional urgency, such as those assessed with the UPPS-P impulsivity scale [ 207 ].

Although such emotional impulse control is thought to be enabled by general processes and neural correlates of CC [ 198 ], and urgency measures are correlated with CC, these correlations are generally weak ( r  = ~0.10 to 0.20), as are correlations between laboratory CC tasks and more general self-control and EF questionnaires [ 157 , 208 , 209 , 210 , 211 , 212 ]. These weak relationships could indicate that there is no great dependency of impulse control on CC processes or the PFC, or indicate that subjective report of impulse control represents a domain of EF outside classical CC function, or it could simply reflect methodological differences. For example, the self-report questionnaires measure subjective aspects of performance whereas laboratory tests such as the Stroop measure objective aspects. However, some evidence suggests that task-based and self-report measures of CC may best be considered separable constructs that are both relevant to mental health [ 209 , 213 ], because they independently predict psychopathology in multiple regressions [ 157 , 214 , 215 ]. This conclusion is consistent with the possibility that these different aspects of CC may depend on different PFC regions: e.g., self-report measures have been correlated with medial PFC morphology, whereas CC tasks typically activate more lateral PFC [ 216 ].

Whether other-dimensional measures of performance, such as apathy (e.g., as measured by the Apathy Motivation Index [ 217 ]) or compulsivity (as measured by the Obsessive-Compulsive Inventory, OCI [ 218 ]) will be beset by similar issues is as yet unclear. One potentially important approach has been to combine computational paradigms such as the two-stage Markov decision-making task with latent factors including compulsivity from an analysis of multiple questionnaires used in impulsive-compulsive disorders [ 219 ]. This study showed that a factor of compulsivity was related to a bias to “model-free” responding, over “model-based” responding, which is commonly associated with goal-directed behavior. Participants in the “model-free” mode tend to respond according to the “win-stay/lose-shift” heuristics of Thorndike’s Law of Effect underlying reinforcement learning, whereas “model-based” responding entails developing a “mental model” of the task, which may involve higher-order processes of CC to optimize performance (e.g., switching away from win-stay when it is ultimately advantageous to do so).

Causal direction of associations between CC and psychopathology and substance use

Although it is clear that CC deficits are behaviorally associated with psychopathology, the causal origin of these relationships are often unclear. Are CC deficits a cause or consequence of emotional and behavioral problems, or perhaps both (i.e., is there a bidirectional relationship)? And if CC deficits are a consequence of the psychopathology, do they produce exacerbation of those symptoms, or other distinct problems that require rehabilitation? An obvious example is substance use disorders, where pre-existing deficits in CC may predispose to drug taking, but drug taking may also cause CC deficits by producing neuropathology, for example in the PFC and related circuitry. Cause-effect relationships regarding other forms of psychiatric morbidity can plausibly operate in a similar fashion. However, it is also possible that these relationships reflect common associations with other variables (e.g., correlated genetic or environmental risk factors).

Quasi-experimental observational designs such as family studies provide some evidence that these associations are at least partly due to correlated genetic risk. For example, stimulant drug abusers and their first degree relatives both have deficits in response inhibition on the Stop task, correlated with reductions in white matter in the RIFG [ 220 ]. Whilst this can be interpreted as showing that PFC-related response inhibition deficits promote vulnerability to stimulants, this influence of impaired response inhibition could theoretically arise from family-related environmental, as well as genetic, influences. Twin studies suggest that associations between psychopathology/substance use and CC are attributable largely to shared genetic influences [ 157 , 221 ]. However, there is some evidence for correlated environmental influences in addition to correlated genetic influences for a common CC factor with depression symptoms in a middle-aged male twin sample [ 222 ] and for a common CC factor with a p factor in children and adolescents [ 200 ].

Several co-twin control studies, which examine relationships controlling for shared familial risk factors, generally suggest that associations of lower cognitive ability with substance use, particularly cannabis, are not consistent with causal models in which substance use causes cognitive impairment. Specifically, the twin who used cannabis more often or began using earlier did not have lower cognitive ability or brain volume than their co-twins, which is inconsistent with a causal effect of the drug [ 223 , 224 , 225 , 226 ]. However, a recent co-twin-control study of young adults [ 227 ] found that the association of alcohol, but not cannabis, misuse with reduced cortical thickness of central executive and salience networks was consistent with causal effects of alcohol exposure as well as pre-existing genetic associations of cortical thickness with the propensity to misuse alcohol. Specifically, causal effects of alcohol misuse were present for lateral PFC, medial frontal and parietal areas, and the frontal operculum (BA 44). These results are thus consistent with a model in which reduced cortical thickness in areas that enable CC, particularly those related to response inhibition, may increase risk for alcohol misuse, and subsequent misuse further impacts those cortical areas (see also [ 228 ]).

Summary and future research directions

Lesion studies and psychometric models both suggest unity and diversity of CC. CC tasks that assess processes such as response inhibition, interference control, working memory maintenance and updating, and mental set shifting show unique variances, but also exhibit some overlap. This overlap (the “unity” of CC) can be characterized in multiple ways, but most characterizations include goal-related processes, such as active goal maintenance and the use of such goals to bias ongoing processing. It appears that there are CC processes that distinguish working memory updating, mental set shifting, and potentially other functions (dual-tasking ability and generativity, as in verbal fluency) from the common CC factor. It is also clear that hot CC can be distinguished from cool CC, and that CC as measured by laboratory tasks is quite different from constructs like impulsivity, which are typically measured with self-reports but can also be measured with laboratory paradigms. Given that different “objective” measures of impulsivity often fail to inter-correlate themselves [ 229 ], and there is also neural evidence of dissociation [ 230 ], it is likely that impulsivity, like CC and inhibition, is a multi-dimensional construct that includes a family of related but separable processes and underlying neural systems. Both CC and self-reported dimensions such as impulsivity may independently relate to psychiatric dysfunction, perhaps at different levels (i.e., at the level of individual disorders or factors that capture variance common across disorders).

Our understanding of the “unity and diversity” of PFC function at the neural level is necessarily incomplete, but suggests some congruency with the evidence at psychometric levels. There is evidence, for example, that networks involving the PFC, for example, the FPN, can mediate superficially different types of cognitive performance, suggesting the operation of an MD system of CC. Nevertheless, the existence of functional dissociations following different types of intervention is also compelling and may suggest that there is specialization of circuitry conferred by the flexible networking of its “hubs” with other neural circuitry. In particular, different PFC nodes within the network, as well their interactions with other neural circuitry, presumably have distinct contributions to information processing, and elucidation of such dynamic transactions in real time will be an important future focus of research. Finally, it appears likely that CC will have to be understood in the context of complementary motivational control networks, including subcortical influences of chemical neuromodulatory systems. Thus, the heterogeneous, but also overlapping, nature of psychiatric symptoms across different DSM5 categories presumably reflects the unity and diversity of CC.

The PFC and its associated networks will thus continue to be a major factor in understanding psychiatric and neurological disorders and developing new treatments. We can foresee future research priorities in several areas. The unity and diversity model of CC/EF needs to be developed further to explore other possible constructs related to PFC networks, perhaps especially hot CC, which may be of greatest significance to mental health disorders. It may also prove necessary to decompose some of its existing constructs, e.g., working memory updating and cognitive flexibility (e.g., set shifting), into their components in order to relate them to distinctive psychiatric symptoms and neural dysfunction.

There is also a need to compare different theoretical positions, such as the cognitive, learning theory, and computational modeling approaches, to optimize our descriptions of phenotypes for mapping onto PFC networks. Such refinements could perhaps enhance genetic studies, as well as improve new nosological systems such as United States’ National Institute of Mental Health’s Research Domain Criteria (RDoC) [ 231 ], a research framework that advocates examining mental disorders from the perspective of basic dimensions of functioning, each examined at multiple levels of analysis (genes to circuits to behavior) that may apply to multiple diagnostic categories. Most relevant to this review, the RDoC includes cognitive systems, with CC and working memory constructs, but their dysfunctioning in mental health disorders has ultimately to be related to their neural substrates and pathophysiology.

Understanding the unity and diversity of genetic influences on CC and how they map onto associated PFC development and structure is another priority for future research. Structurally, there is evidence of differential genetic regulation of different PFC regions; for example, development of the mouse dorsal (and not ventral) PFC is especially sensitive to the fibroblast growth factor family of genes [ 232 ]. Several independent twin studies [ 36 , 39 , 41 , 157 , 233 ] have yielded evidence that at the latent variable level, CC constructs are moderately to highly heritable and, importantly, that the separability of working memory updating and mental set shifting from the common CC factor is largely attributable to different genetic influences.

However, the specific genes that account for these patterns, presumably in part via expression in the PFC, have yet to be identified. Most genome-wide association studies (GWAS) to date have focused on intelligence or g [ 234 , 235 , 236 , 237 ], and suggest that hundreds to thousands of genes additively influence variation in intelligence, with the effect of any one gene being very small (typically in GWAS, a variant has r 2  < 0.05% [ 238 ]). The largest GWAS of CC to date [ 239 ] included individual CC tests such as the Stroop task with samples smaller than 11,000 individuals, and did not yield any significant associations. Clearly, more work is needed with sufficiently large samples to enable GWAS. However, acquiring detailed cognitive task data on such large samples ( N  = 10’s to 100’s of thousands) is no easy feat and will most certainly require harmonization across multiple samples and/or online testing. Though resulting measures are typically crude compared to the measures included in smaller studies [ 240 ], the trade-off between phenotype depth and sample size may be effective for gene discovery [ 241 ], as demonstrated by a recent preliminary report [ 242 ] of a GWAS for the Common CC factor.

Once such variants are identified, bioinformatic follow-up analyses can be used to identify genetic pathways that influence CC-related neural differences. For example, a recent GWAS [ 243 ] suggested that global measures of cortical surface area and thickness were related to distinct genetic influences associated with different developmental mechanisms (i.e., associated with regulatory elements present during fetal development and in adults, respectively); and total surface area was bidirectionally causally related to general cognitive ability and educational attainment. Similar analyses applied to more nuanced CC phenotypes could confirm hypothesized pathways and suggest new avenues of research for understanding behavioral and neural variation related to CC variation and associated clinical outcomes.

As developmental studies are also likely to be of increasing importance for determining the factors influencing the etiology of mental disorders, large scale longitudinal studies of CC/EF, combined with sensitive clinical scales, trait questionnaires, neuroimaging, and genotyping, as for example, in the National Institutes’ of Health Adolescent Brain Cognitive Development (ABCD) study [ 244 ], will be invaluable. Such an ambitious project may well have to involve increasingly sophisticated ways of obtaining this information via on-line testing.

Deficits in neural networks, including the PFC, are increasingly being used to determine the neural substrates of CC/EF. However, more analysis is required of the underlying pathophysiology of these networks (e.g., at the circuit and molecular levels), because a network abnormality could arise in many different ways that may have significance for diagnosis, drug discovery, and neuromodulation strategies. Finally, if experimental animals are to be used to model genetic and molecular deficits in the developing brain, new research needs to be done to test whether the “unity and diversity” approach applies across species and fits what is known of PFC homology (Preuss & Wise, this issue [ 245 ]).

Snyder HR, Miyake A, Hankin BL. Advancing understanding of executive function impairments and psychopathology: bridging the gap between clinical and cognitive approaches. Front Psychol. 2015;6:328.

PubMed   PubMed Central   Google Scholar  

McTeague LM, Goodkind MS, Etkin A. Transdiagnostic impairment of cognitive control in mental illness. J Psychiatr Res. 2016;83:37–46.

Sha Z, Wager TD, Mechelli A, He Y. Common dysfunction of large-scale neurocognitive networks across psychiatric disorders. Biol Psychiatry. 2019;85:379–88.

PubMed   Google Scholar  

Menon V. Large-scale brain networks and psychopathology: a unifying triple network model. Trends Cogn Sci. 2011;15:483–506.

Cohen JD. Cognitive control: core constructs and current considerations. In: Egner T, editor. The Wiley handbook of cognitive control, Chichester, West Sussex, UK: John Wiley & Sons Ltd.; 2017. p. 3–28.

Schneider W, Shiffrin RM. Controlled and automatic human information processing: I. detection, search, and attention. Psychol Rev. 1977;84:1–66.

Google Scholar  

Balleine BW, O’Doherty JP. Human and rodent homologies in action control: corticostriatal determinants of goal-directed and habitual action. Neuropsychopharmacology. 2010;35:48–69.

Miller EK, Cohen JD. An integrative theory of prefrontal cortex function. Annu Rev Neurosci. 2001;24:167–202.

CAS   PubMed   Google Scholar  

Carter CS, Braver TS, Barch DM, Botvinick MM, Noll D, Cohen JD. Anterior cingulate cortex, error detection, and the online monitoring of performance. Science. 1998;280:747–9.

Kerns JG, Cohen JD, MacDonald AW, Cho RY, Stenger VA, Carter CS. Anterior cingulate conflict monitoring and adjustments in control. Science. 2004;303:1023–6.

Duncan J. The multiple-demand (MD) system of the primate brain: mental programs for intelligent behaviour. Trends Cogn Sci. 2010;14:172–9.

Duncan J. An adaptive coding model of neural function in prefrontal cortex. Nat Rev Neurosci. 2001;2:820–9.

Duncan J, Miller EK. Adaptive neural coding in frontal and parietal cortex. In: Stuss DT, Knight RT, editors. Principles of frontal lobe function, 2nd ed., New York, NY: Oxford University Press; 2013. p. 292–301.

Miller EK, Wilson MA. All my circuits: using multiple electrodes to understand functioning neural networks. Neuron. 2008;60:483–8.

Haber SN, Liu H, Seidlitz J, Bullmore ET. Prefrontal connectomics: from anatomy to neuroimaging. Neuropsychopharmacol. 2022. In press.

Menon V, D’Esposito M. The role of PFC networks in cognitive control and executive function. Neuropsychopharmacol. 2022. In press.

Badre D. Brain networks for cognitive control. In: Kalivas PW, Paulus MP, editors. Intrusive thinking: from molecules to free will, Cambridge, MA: MIT Press; 2020. p. 203–28.

Eisenreich BR, Akaishi R, Hayden BY. Control without controllers: toward a distributed neuroscience of executive control. J Cogn Neurosci. 2017;29:1684–98.

Cools R, D’Esposito M. Inverted-U–shaped dopamine actions on human working memory and cognitive control. Biol Psychiatry. 2011;69:e113–e125.

CAS   PubMed   PubMed Central   Google Scholar  

Caspi A, Moffitt TE. All for one and one for all: mental disorders in one dimension. Am J Psychiatry. 2018;175:831–44.

Elston G, Benavides-Piccione R, Elston A, Manger P, Defelipe J. Pyramidal cells in prefrontal cortex of primates: marked differences in neuronal structure among species. Front Neuroanat. 2011;5:2.

Semendeferi K, Teffer K, Buxhoeveden DP, Park MS, Bludau S, Amunts K, et al. Spatial organization of neurons in the frontal pole sets humans apart from great apes. Cereb Cortex. 2011;21:1485–97.

Allman JM, Watson KK, Tetreault NA, Hakeem AY. Intuition and autism: a possible role for Von Economo neurons. Trends Cogn Sci. 2005;9:367–73.

Fuster JM. Unit activity in prefrontal cortex during delayed-response performance: neuronal correlates of transient memory. J Neurophysiol. 1973;36:61–78.

Funahashi S, Bruce CJ, Goldman-Rakic PS. Mnemonic coding of visual space in the monkey’s dorsolateral prefrontal cortex. J Neurophysiol. 1989;61:331–49.

Shinomoto S, Miyazaki Y, Tamura H, Fujita I. Regional and laminar differences in in vivo firing patterns of primate cortical neurons. J Neurophysiol. 2005;94:567–75.

Rodriguez G, Sarazin M, Clemente A, Holden S, Paz JT, Delord B. Conditional bistability, a generic cellular mnemonic mechanism for robust and flexible working memory computations. J Neurosci. 2018;38:5209–19.

Diamond A. Executive functions. Annu Rev Psychol. 2013;64:135–68.

Friedman NP, Miyake A. Unity and diversity of executive functions: individual differences as a window on cognitive structure. Cortex. 2017;86:186–204.

Jurado MB, Rosselli M. The elusive nature of executive functions: a review of our current understanding. Neuropsychol Rev. 2007;17:213–33.

Teuber H-L. Unity and diversity of frontal lobe functions. Acta Neurobiol Exp. 1972;32:615–56.

CAS   Google Scholar  

Duncan J, Johnson R, Swales M, Freer C. Frontal lobe deficits after head injury: unity and diversity of function. Cogn Neuropsychol. 1997;14:713–41.

Miyake A, Friedman NP, Emerson MJ, Witzki AH, Howerter A, Wager TD. The unity and diversity of executive functions and their contributions to complex “frontal lobe” tasks: a latent variable analysis. Cogn Psychol. 2000;41:49–100.

Cirino PT, Ahmed Y, Miciak J, Taylor WP, Gerst EH, Barnes MA. A framework for executive function in the late elementary years. Neuropsychology. 2018;32:176–89.

Xu F, Han Y, Sabbagh MA, Wang T, Ren X, Li C. Developmental differences in the structure of executive function in middle childhood and adolescence. PLoS One. 2013;8:e77770.

Friedman NP, Miyake A, Altamirano LJ, Corley RP, Young SE, Rhea SA, et al. Stability and change in executive function abilities from late adolescence to early adulthood: a longitudinal twin study. Dev Psychol. 2016;52:326–40.

Fleming KA, Heintzelman SJ, Bartholow BD. Specifying associations between conscientiousness and executive functioning: mental set shifting, not prepotent response inhibition or working memory updating. J Pers. 2016;84:348–60.

Ito TA, Friedman NP, Bartholow BD, Correll J, Loersch C, Altamirano LJ, et al. Toward a comprehensive understanding of executive cognitive function in implicit racial bias. J Pers Soc Psychol. 2015;108:187–218.

Engelhardt LE, Briley DA, Mann FD, Harden KP, Tucker-Drob EM. Genes unite executive functions in childhood. Psychol Sci. 2015;26:1151–63.

Rose SA, Feldman JF, Jankowski JJ. Implications of infant cognition for executive functions at age 11. Psychol Sci. 2012;23:1345–55.

Gustavson DE, Panizzon MS, Franz CE, Friedman NP, Reynolds CA, Jacobson KC, et al. Genetic and environmental architecture of executive functions in midlife. Neuropsychology. 2018;32:18–30.

Hartung J, Engelhardt LE, Thibodeaux ML, Harden KP, Tucker-Drob EM. Developmental transformations in the structure of executive functions. J Exp Child Psychol. 2020;189:104681.

Shing YL, Lindenberger U, Diamond A, Li S-C, Davidson MC. Memory maintenance and inhibitory control differentiate from early childhood to adolescence. Dev Neuropsychol. 2010;35:679–97.

Wiebe SA, Espy KA, Charak D. Using confirmatory factor analysis to understand executive control in preschool children: I. latent structure. Dev Psychol. 2008;44:575–87.

Ecker UKH, Lewandowsky S, Oberauer K. Removal of information from working memory: a specific updating process. J Mem Lang. 2014;74:77–90.

Monsell S. Task switching. Trends Cogn Sci. 2003;7:134–40.

Koechlin E. Motivation, control, and human prefrontal executive function. In: Stuss DT, Knight RT, editors. Principles of frontal lobe function, 2nd edition, New York, NY: Oxford University Press; 2013. p. 279–91.

Williams JMG, Mathews A, MacLeod CM. The emotional Stroop task and psychopathology. Psychol Bull. 1996;120:3–24.

Banich MT, Smolker HR, Snyder HR, Lewis-Peacock JA, Godinez DA, Wager TD, et al. Turning down the heat: neural mechanisms of cognitive control for inhibiting task-irrelevant emotional information during adolescence. Neuropsychologia. 2019;125:93–108.

Zelazo PD, Carlson SM. Hot and cool executive function in childhood and adolescence: development and plasticity. Child Dev Perspect. 2012;6:354–60.

Welsh M, Peterson E. Issues in the conceptualization and assessment of hot executive functions in childhood. J Int Neuropsychol Soc. 2014;20:152–6.

Poon K. Hot and cool executive functions in adolescence: development and contributions to important developmental outcomes. Front Psychol. 2018;8:2311.

Botdorf M, Rosenbaum GM, Patrianakos J, Steinberg L, Chein JM. Adolescent risk-taking is predicted by individual differences in cognitive control over emotional, but not non-emotional, response conflict. Cogn Emot. 2017;31:972–9.

Kim S, Nordling JK, Yoon JE, Boldt LJ, Kochanska G. Effortful control in “hot” and “cool” tasks differentially predicts children’s behavior problems and academic performance. J Abnorm Child Psychol. 2013;41:43–56.

Hongwanishkul D, Happaney KR, Lee WSC, Zelazo PD. Assessment of hot and cool executive function in young children: age-related changes and individual differences. Dev Neuropsychol. 2005;28:617–44.

Harden KP, Kretsch N, Mann FD, Herzhoff K, Tackett JL, Steinberg L, et al. Beyond dual systems: a genetically-informed, latent factor model of behavioral and self-report measures related to adolescent risk-taking. Dev Cogn Neurosci. 2017;25:221–34.

Willoughby M, Kupersmidt J, Voegler-Lee M, Bryant D. Contributions of hot and cool self-regulation to preschool disruptive behavior and academic achievement. Dev Neuropsychol. 2011;36:162–80.

Menon V, Uddin LQ. Saliency, switching, attention and control: a network model of insula function. Brain Struct Funct. 2010;214:655–67.

Seeley WW, Menon V, Schatzberg AF, Keller J, Glover GH, Kenna H, et al. Dissociable intrinsic connectivity networks for salience processing and executive control. J Neurosci. 2007;27:2349–56.

Hung Y, Gaillard SL, Yarmak P, Arsalidou M. Dissociations of cognitive inhibition, response inhibition, and emotional interference: voxelwise ALE meta-analyses of fMRI studies. Hum Brain Mapp. 2018;39:4065–82.

Ochsner KN, Hughes B, Robertson ER, Cooper JC, Gabrieli JDE. Neural systems supporting the control of affective and cognitive conflicts. J Cogn Neurosci. 2009;21:1841–54.

Cromheeke S, Mueller SC. Probing emotional influences on cognitive control: an ALE meta-analysis of cognition emotion interactions. Brain Struct Funct. 2014;219:995–1008.

Song S, Zilverstand A, Song H, d’Oleire Uquillas F, Wang Y, Xie C, et al. The influence of emotional interference on cognitive control: a meta-analysis of neuroimaging studies using the emotional Stroop task. Sci Rep. 2017;7:2088.

Etkin A, Egner T, Kalisch R. Emotional processing in anterior cingulate and medial prefrontal cortex. Trends Cogn Sci. 2011;15:85–93.

Etkin A, Egner T, Peraza DM, Kandel ER, Hirsch J. Resolving emotional conflict: a role for the rostral anterior cingulate cortex in modulating activity in the amygdala. Neuron. 2006;51:871–82.

Xu M, Xu G, Yang Y. Neural systems underlying emotional and non-emotional interference processing: an ALE meta-analysis of functional neuroimaging studies. Front Behav Neurosci. 2016;10:220.

Kouneiher F, Charron S, Koechlin E. Motivation and cognitive control in the human prefrontal cortex. Nat Neurosci. 2009;12:939–45.

Tanji J, Hoshi E. Role of the lateral prefrontal cortex in executive behavioral control. Physiol Rev. 2008;88:37–57.

Badre D. On task: how our brain gets things done. Princeton, NJ: Princeton University Press; 2020.

Warren JM, Akert K, editors. The frontal granular cortex and behavior. New York, NY, US: McGraw-Hill; 1964.

Goldman-Rakic PS. The prefrontal landscape: Implications of functional architecture for understanding human mentation and the central executive. Philos Trans R Soc Lond B Biol Sci. 1996;351:1445–53.

Petrides M. Dissociable roles of mid-dorsolateral prefrontal and anterior inferotemporal cortex in visual working memory. J Neurosci. 2000;20:7496–503.

Sakai K, Rowe JB, Passingham RE. Active maintenance in prefrontal area 46 creates distractor-resistant memory. Nat Neurosci. 2002;5:479–84.

Petrides M. The mid-dorsolateral prefronto-parietal network and the epoptic process. In: Stuss DT, Knight RT, editors. Principles of frontal lobe function, 2nd ed, New York, NY: Oxford University Press; 2013. p. 79–89.

Wager TD, Smith EE. Neuroimaging studies of working memory: a meta-analysis. Cogn Affect Behav Neurosci. 2003;3:255–74.

Chochon F, Cohen L, Moortele PF, van de, Dehaene S. Differential contributions of the left and right inferior parietal lobules to number processing. J Cogn Neurosci. 1999;11:617–30.

Georgopoulos AP, Pellizzer G. The mental and the neural: psychological and neural studies of mental rotation and memory scanning. Neuropsychologia. 1995;33:1531–47.

Brass M, Ullsperger M, Knoesche TR, Cramon DY, von, Phillips NA. Who comes first? The role of the prefrontal and parietal cortex in cognitive control. J Cogn Neurosci. 2005;17:1367–75.

Nee DE, D’Esposito M. Causal evidence for lateral prefrontal cortex dynamics supporting cognitive control. ELife. 2017;6:e28040.

Nee DE. Integrative frontal-parietal dynamics supporting cognitive control. ELife. 2021;10:e57244.

Milner B. Effects of different brain lesions on card sorting: the role of the frontal lobes. Arch Neurol. 1963;9:90–100.

Dias R, Robbins TW, Roberts AC. Dissociation in prefrontal cortex of affective and attentional shifts. Nature 1996;380:69–72.

Keeler JF, Robbins TW. Translating cognition from animals to humans. Biochem Pharm. 2011;81:1356–66.

Dias R, Robbins TW, Roberts AC. Dissociable forms of inhibitory control within prefrontal cortex with an analog of the Wisconsin Card Sort Test: restriction to novel situations and independence from “on-line” processing. J Neurosci. 1997;17:9285–97.

Hampshire A, Owen AM. Fractionating attentional control using event-related fMRI. Cereb Cortex. 2006;16:1679–89.

Vaghi MM, Vértes PE, Kitzbichler MG, Apergis-Schoute AM, van der Flier FE, Fineberg NA, et al. Specific frontostriatal circuits for impaired cognitive flexibility and goal-directed planning in obsessive-compulsive disorder: evidence from resting-state functional connectivity. Biol Psychiatry. 2017; 81:708–17.

Aron AR, Monsell S, Sahakian BJ, Robbins TW. A componential analysis of task-switching deficits associated with lesions of left and right frontal cortex. Brain. 2004;127:1561–73.

Esterman M, Chiu Y-C, Tamber-Rosenau BJ, Yantis S. Decoding cognitive control in human parietal cortex. Proc Natl Acad Sci USA. 2009;106:17974–9.

Kim C, Johnson NF, Cilles SE, Gold BT. Common and distinct mechanisms of cognitive flexibility in prefrontal cortex. J Neurosci. 2011;31:4771–9.

Uddin LQ. Cognitive and behavioural flexibility: neural mechanisms and clinical considerations. Nat Rev Neurosci. 2021;22:167–79.

Aron AR, Fletcher PC, Bullmore ET, Sahakian BJ, Robbins TW. Stop-signal inhibition disrupted by damage to right inferior frontal gyrus in humans. Nat Neurosci. 2003;6:115–6.

Logan GD, Cowan WB, Davis KA. On the ability to inhibit simple and choice reaction time responses: a model and a method. J Exp Psychol Hum Percept Perform. 1984;10:276–91.

Aron AR, Robbins TW, Poldrack RA. Inhibition and the right inferior frontal cortex: one decade on. Trends Cogn Sci. 2014;18:177–85.

Erika-Florence M, Leech R, Hampshire A. A functional network perspective on response inhibition and attentional control. Nat Commun. 2014;5:4073.

Chatham CH, Claus ED, Kim A, Curran T, Banich MT, Munakata Y. Cognitive control reflects context monitoring, not motoric stopping, in response inhibition. PloS One. 2012;7:13.

Sharp DJ, Bonnelle V, Boissezon XD, Beckmann CF, James SG, Patel MC, et al. Distinct frontal systems for response inhibition, attentional capture, and error processing. Proc Natl Acad Sci USA. 2010;107:6106–11.

Munakata Y, Herd SA, Chatham CH, Depue BE, Banich MT, O’Reilly RC. A unified framework for inhibitory control. Trends Cogn Sci. 2011;15:453–9.

Chase HW, Clark L, Sahakian BJ, Bullmore ET, Robbins TW. Dissociable roles of prefrontal subregions in self-ordered working memory performance. Neuropsychologia. 2008;46:2650–61.

Cai W, Ryali S, Chen T, Li C-SR, Menon V. Dissociable roles of right inferior frontal cortex and anterior insula in inhibitory control: evidence from intrinsic and task-related functional parcellation, connectivity, and response profile analyses across multiple datasets. J Neurosci. 2014;34:14652–67.

Aron AR, Poldrack RA. Cortical and subcortical contributions to stop signal response inhibition: role of the subthalamic nucleus. J Neurosci. 2006;26:2424–33.

Rae CL, Hughes LE, Anderson MC, Rowe JB. The prefrontal cortex achieves inhibitory control by facilitating subcortical motor pathway connectivity. J Neurosci. 2015;35:786–94.

Castiglione A, Wagner J, Anderson M, Aron AR. Preventing a thought from coming to mind elicits increased right frontal beta just as stopping action does. Cereb Cortex. 2019;29:2160–72.

Depue BE, Orr JM, Smolker HR, Naaz F, Banich MT. The organization of right prefrontal networks reveals common mechanisms of inhibitory regulation across cognitive, emotional, and motor processes. Cereb Cortex. 2016;26:1634–46.

Levy BJ, Anderson MC. Purging of memories from conscious awareness tracked in the human brain. J Neurosci. 2012;32:16785–94.

Schmitz TW, Correia MM, Ferreira CS, Prescot AP, Anderson MC. Hippocampal GABA enables inhibitory control over unwanted thoughts. Nat Commun. 2017;8:1311.

Anderson MC, Floresco SB. Prefrontal-hippocampal interactions supporting the extinction of emotion: the stopping retrieval model. Neuropsychopharmacol. 2022. In press.

Badre D, Wagner AD. Left ventrolateral prefrontal cortex and the cognitive control of memory. Neuropsychologia. 2007;45:2883–901.

Eslinger PJ, Damasio AR. Severe disturbance of higher cognition after bilateral frontal lobe ablation: patient EVR. Neurology. 1985;35:1731–1731.

Bechara A, Damasio AR, Damasio H, Anderson SW. Insensitivity to future consequences following damage to human prefrontal cortex. Cognition. 1994;50:7–15.

Burgess PW, Wu HC. Rostral prefrontal cortex (Brodman area 10): metacognition in the brain. In: Stuss DT, Knight RT, editors. Principles of frontal lobe function, 2nd edition, New York, NY: Oxford University Press; 2013. p. 524–44.

Monosov IE, Rushworth MRE. Interactions between ventrolateral prefrontal cortex and anterior cingulate cortex during learning and behavioral change. Neuropsychopharmacol. 2022; In press.

Rudebeck PH, Izquierdo A. Foraging with the frontal cortex: a cross-species evaluation of reward-guided behavior. Neuropsychopharmacol. 2022; In press.

Cools R, Arnsten AFT. Neuromodulation of prefrontal cortex cognitive function in primates: the powerful role of monoamines and acetylcholine. Neuropsychopharmacol. 2022; In press.

Kolling N, Behrens T, Wittmann M, Rushworth M. Multiple signals in anterior cingulate cortex. Curr Opin Neurobiol. 2016;37:36–43.

Vassena E, Holroyd CB, Alexander WH. Computational models of anterior cingulate cortex: at the crossroads between prediction and effort. Front Neurosci. 2017;11:316.

Holroyd C, Coles M. The neural basis of human error processing: reinforcement learning, dopamine, and the error-related negativity. Psychol Rev. 2002;109:679–709.

Alexander WH, Brown JW. Medial prefrontal cortex as an action-outcome predictor. Nat Neurosci. 2011;14:1338–44.

Shenhav A, Botvinick MM, Cohen JD. The expected value of control: an integrative theory of anterior cingulate cortex function. Neuron. 2013;79:217–40.

Shenhav A, Straccia MA, Cohen JD, Botvinick MM. Anterior cingulate engagement in a foraging context reflects choice difficulty, not foraging value. Nat Neurosci. 2014;17:1249–54.

Collins AGE, Shenhav A. Advances in modeling learning and decision-making in neuroscience. Neuropsychopharmacol. 2022. In press.

Braver TS. The variable nature of cognitive control: a dual mechanisms framework. Trends Cogn Sci. 2012;16:106–13.

Brown JW, Alexander WH. Foraging value, risk avoidance, and multiple control signals: how the anterior cingulate cortex controls value-based decision-making. J Cogn Neurosci. 2017;29:1656–73.

Vassena E, Deraeve J, Alexander WH. Surprise, value and control in anterior cingulate cortex during speeded decision-making. Nat Hum Behav. 2020;4:412–22.

Ott T, Nieder A. Dopamine and cognitive control in prefrontal cortex. Trends Cogn Sci. 2019;23:213–34.

Braver TS, Cohen JD. Dopamine, cognitive control, and schizophrenia: the gating model. In: Reggia JA, Ruppin E, Glanzman D, editors. Progress in brain research, vol. 121, Elsevier; 1999. p. 327–49.

Logue SF, Gould TJ. The neural and genetic basis of executive function: attention, cognitive flexibility, and response inhibition. Pharm Biochem Beh. 2014;123:45–54.

Robbins TW, Kehagia AA. The neurochemical modulation of prefrontal control processes. The Wiley handbook of cognitive control, John Wiley & Sons, Ltd; 2017. p. 334–54.

Roberts AC, Salvia MD, Wilkinson LS, Collins P, Muir JL, Everitt BJ, et al. 6-Hydroxydopamine lesions of the prefrontal cortex in monkeys enhance performance on an analog of the Wisconsin Card Sort Test: possible interactions with subcortical dopamine. J Neurosci. 1994;14:2531–44.

Floresco SB. Prefrontal dopamine and behavioral flexibility: shifting from an “inverted-U” toward a family of functions. Front Neurosci. 2013;7:62.

Cools R, Barker RA, Sahakian BJ, Robbins TW. Enhanced or impaired cognitive function in Parkinson’s disease as a function of dopaminergic medication and task demands. Cereb Cortex. 2001;11:1136–43.

Clarke HF, Walker SC, Crofts HS, Dalley JW, Robbins TW, Roberts AC. Prefrontal serotonin depletion affects reversal learning but not attentional set shifting. J Neurosci. 2005;25:532–8.

Chamberlain SR, Müller U, Blackwell AD, Clark L, Robbins TW, Sahakian BJ. Neurochemical modulation of response inhibition and probabilistic learning in humans. Science. 2006;311:861–3.

Sarter M, Lustig C, Howe WM, Gritton H, Berry AS. Deterministic functions of cortical acetylcholine. Eur J Neurosci. 2014;39:1912–20.

Westbrook A, van den Bosch R, Määttä JI, Hofmans L, Papadopetraki D, Cools R, et al. Dopamine promotes cognitive effort by biasing the benefits versus costs of cognitive work. Science. 2020;367:1362–6.

Collette F, Van der Linden M, Laureys S, Delfiore G, Degueldre C, Luxen A, et al. Exploring the unity and diversity of the neural substrates of executive functioning. Hum Brain Mapp. 2005;25:409–23.

Collette F, Hogge M, Salmon E, Van der Linden M. Exploration of the neural substrates of executive functioning by functional neuroimaging. Neuroscience. 2006;139:209–21.

Niendam TA, Laird AR, Ray KL, Dean YM, Glahn DC, Carter CS. Meta-analytic evidence for a superordinate cognitive control network subserving diverse executive functions. Cogn Affect Behav Neurosci. 2012;12:241–68.

Engelhardt LE, Harden KP, Tucker-Drob EM, Church JA. The neural architecture of executive functions is established by middle childhood. NeuroImage. 2019;185:479–89.

Fedorenko E, Duncan J, Kanwisher N. Broad domain generality in focal regions of frontal and parietal cortex. Proc Natl Acad Sci USA. 2013;110:16616–21.

McKenna R, Rushe T, Woodcock KA. Informing the structure of executive function in children: a meta-analysis of functional neuroimaging data. Front Hum Neurosci. 2017;11:154.

Nee DE, Brown JW, Askren MK, Berman MG, Demiralp E, Krawitz A, et al. A meta-analysis of executive components of working memory. Cereb Cortex. 2013;23:264–82.

Dosenbach NUF, Visscher KM, Palmer ED, Miezin FM, Wenger KK, Kang HC, et al. A core system for the implementation of task sets. Neuron. 2006;50:799–812.

Crittenden BM, Mitchell DJ, Duncan J. Task encoding across the multiple demand cortex is consistent with a frontoparietal and cingulo-opercular dual networks distinction. J Neurosci. 2016;36:6147–55.

Dodds CM, Morein-Zamir S, Robbins TW. Dissociating inhibition, attention, and response control in the frontoparietal network using functional magnetic resonance imaging. Cereb Cortex. 2011;21:1155–65.

Reineberg AE, Andrews-Hanna JR, Depue BE, Friedman NP, Banich MT. Resting-state networks predict individual differences in common and specific aspects of executive function. NeuroImage. 2015;104:69–78.

Reineberg AE, Banich MT. Functional connectivity at rest is sensitive to individual differences in executive function: a network analysis. Hum Brain Mapp. 2016;37:2959–75.

Smolker HR, Depue BE, Reineberg AE, Orr JM, Banich MT. Individual differences in regional prefrontal gray matter morphometry and fractional anisotropy are associated with different constructs of executive function. Brain Struct Funct. 2015;220:1291–306.

Smolker HR, Friedman NP, Hewitt JK, Banich MT. Neuroanatomical correlates of the unity and diversity model of executive function in young adults. Front Hum Neurosci. 2018;12:283.

Yuan P, Raz N. Prefrontal cortex and executive functions in healthy adults: a meta-analysis of structural neuroimaging studies. Neurosci Biobehav Rev. 2014;42:180–92.

Lerman-Sinkoff DB, Sui J, Rachakonda S, Kandala S, Calhoun VD, Barch DM. Multimodal neural correlates of cognitive control in the Human Connectome Project. NeuroImage. 2017;163:41–54.

Cole MW, Yarkoni T, Repovs G, Anticevic A, Braver TS. Global connectivity of prefrontal cortex predicts cognitive control and intelligence. J Neurosci. 2012;32:8988–99.

Panikratova YR, Vlasova RM, Akhutina TV, Korneev AA, Sinitsyn VE, Pechenkova EV. Functional connectivity of the dorsolateral prefrontal cortex contributes to different components of executive functions. Int J Psychophysiol. 2020;151:70–79.

Baum GL, Ciric R, Roalf DR, Betzel RF, Moore TM, Shinohara RT, et al. Modular segregation of structural brain networks supports the development of executive function in youth. Curr Biol. 2017;27:1561–72.e8.

Braun U, Schäfer A, Walter H, Erk S, Romanczuk-Seiferth N, Haddad L, et al. Dynamic reconfiguration of frontal brain networks during executive cognition in humans. Proc Natl Acad Sci USA. 2015;112:11678–83.

Cohen JR, D’Esposito M. The segregation and integration of distinct brain networks and their relationship to cognition. J Neurosci. 2016;36:12083–94.

Gratton C, Sun H, Petersen SE. Control networks and hubs. Psychophysiol. 2018;55:e13032.

Friedman NP, Hatoum AS, Gustavson DE, Corley RP, Hewitt JK, Young SE. Executive functions and impulsivity are genetically distinct and independently predict psychopathology: results from two adult twin studies. Clin Psychol Sci. 2020;8:519–38.

Friedman NP, Miyake A, Young SE, DeFries JC, Corley RP, Hewitt JK. Individual differences in executive functions are almost entirely genetic in origin. J Exp Psychol Gen. 2008;137:201–25.

Aron AR. The neural basis of inhibition in cognitive control. Neuroscientist. 2007;13:214–28.

Zacks RT, Hasher L. Directed ignoring: inhibitory regulation of working memory. Inhibitory processes in attention, memory, and language. San Diego, CA, US: Academic Press; 1994. p. 241–64.

Friedman NP, Miyake A. The relations among inhibition and interference control functions: a latent-variable analysis. J Exp Psychol Gen. 2004;133:101–35.

Mayr U, Keele SW. Changing internal constraints on action: the role of backward inhibition. J Exp Psychol Gen. 2000;129:4–26.

Nigg JT. On inhibition/disinhibition in developmental psychopathology: views from cognitive and personality psychology and a working inhibition taxonomy. Psychol Bull. 2000;126:220–46.

Hedden T, Yoon C. Individual differences in executive processing predict susceptibility to interference in verbal working memory. Neuropsychology. 2006;20:511–28.

MacLeod CM, Dodd MD, Sheard ED, Wilson DE, Bibi U. In opposition to inhibition. In: Ross BH, editor. Psychology of Learning and Motivation, vol. 43, Academic Press; 2003. p. 163–214.

Kimberg DY, Farah MJ. A unified account of cognitive impairments following frontal lobe damage: the role of working memory in complex, organized behavior. J Exp Psychol Gen. 1993;122:411–28.

Herd SA, O׳Reilly RC, Hazy TE, Chatham CH, Brant AM, Friedman NP. A neural network model of individual differences in task switching abilities. Neuropsychologia. 2014;62:375–89.

Aron AR, Robbins TW, Poldrack RA. Inhibition and the right inferior frontal cortex. Trends Cogn Sci. 2004;8:170–7.

Hampshire A, Sharp DJ. Contrasting network and modular perspectives on inhibitory control. Trends Cogn Sci. 2015;19:445–52.

Desimone R, Duncan J. Neural mechanisms of selective visual attention. Annu Rev Neurosci. 1995;18:193–222.

Roberts RJ Jr, Pennington BF. An interactive framework for examining prefrontal cognitive processes. Dev Neuropsychol. 1996;12:105–26.

Kane MJ, Bleckley MK, Conway ARA, Engle RW. A controlled-attention view of working-memory capacity. J Exp Psychol Gen. 2001;130:169–83.

Engelhardt LE, Mann FD, Briley DA, Church JA, Harden KP, Tucker-Drob EM. Strong genetic overlap between executive functions and intelligence. J Exp Psychol Gen. 2016;145:1141–59.

McGrath LM, Braaten EB, Doty ND, Willoughby BL, Wilson HK, O’Donnell EH, et al. Extending the ‘cross‐disorder’ relevance of executive functions to dimensional neuropsychiatric traits in youth. J Child Psychol Psychiatr. 2016;57:462–71.

Friedman NP, Haberstick BC, Willcutt EG, Miyake A, Young SE, Corley RP, et al. Greater attention problems during childhood predict poorer executive functioning in late adolescence. Psychol Sci. 2007;18:893–900.

Willcutt EG, Doyle AE, Nigg JT, Faraone SV, Pennington BF. Validity of the executive function theory of attention-deficit/hyperactivity disorder: a meta-analytic review. Biol Psychiatry. 2005;57:1336–46.

Friedman NP, Miyake A, Robinson JL, Hewitt JK. Developmental trajectories in toddlers’ self-restraint predict individual differences in executive functions 14 years later: a behavioral genetic analysis. Dev Psychol. 2011;47:1410–30.

Conway ARA, Kane MJ, Engle RW. Working memory capacity and its relation to general intelligence. Trends Cogn Sci. 2003;7:547–52.

Ackerman PL, Beier ME, Boyle MO. Working memory and intelligence: the same or different constructs? Psychol Bull. 2005;131:30–60.

Morris RW, Quail S, Griffiths KR, Green MJ, Balleine BW. Corticostriatal control of goal-directed action is impaired in schizophrenia. Biol Psychiatry. 2015;77:187–95.

Zmigrod L, Garrison JR, Carr J, Simons JS. The neural mechanisms of hallucinations: a quantitative meta-analysis of neuroimaging studies. Neurosci Biobehav Rev. 2016;69:113–23.

Green MF, Horan WP, Lee J. Nonsocial and social cognition in schizophrenia: current evidence and future directions. World Psychiatry. 2019;18:146–61.

Everitt BJ, Robbins TW. Drug addiction: updating actions to habits to compulsions ten years on. Annu Rev Psychol. 2016;67:23–50.

Gillan CM, Robbins TW. Goal-directed learning and obsessive–compulsive disorder. Philos Trans R Soc Lond B Biol Sci. 2014;369:20130475.

Ahmari SE, Rauch SL. The prefrontal cortex and OCD. Neuropsychopharmacology. 2022. In press.

Momennejad I, Russek EM, Cheong JH, Botvinick MM, Daw ND, Gershman SJ. The successor representation in human reinforcement learning. Nat Hum Behav. 2017;1:680–92.

Polak AR, Witteveen AB, Reitsma JB, Olff M. The role of executive function in posttraumatic stress disorder: a systematic review. J Affect Disord. 2012;141:11–21.

Snyder HR, Kaiser RH, Warren SL, Heller W. Obsessive-compulsive disorder is associated with broad impairments in executive function: a meta-analysis. Clin Psychol Sci. 2015;3:301–30.

Mann‐Wrobel MC, Carreno JT, Dickinson D. Meta-analysis of neuropsychological functioning in euthymic bipolar disorder: an update and investigation of moderator variables. Bipolar Disord. 2011;13:334–42.

Henry J, Crawford J. A meta-analytic review of verbal fluency deficits in schizophrenia relative to other neurocognitive deficits. Cogn Neuropsychiatry. 2005;10:1–33.

Walshaw PD, Alloy LB, Sabb FW. Executive function in pediatric bipolar disorder and attention-deficit hyperactivity disorder: in search of distinct phenotypic profiles. Neuropsychol Rev. 2010;20:103–20.

Frazier TW, Demaree HA, Youngstrom EA. Meta-analysis of intellectual and neuropsychological test performance in attention-deficit/hyperactivity disorder. Neuropsychology 2004;18:543–55.

Morgan AB, Lilienfeld SO. A meta-analytic review of the relation between antisocial behavior and neuropsychological measures of executive function. Clin Psychol Rev. 2000;20:113–36.

Smith JL, Mattick RP, Jamadar SD, Iredale JM. Deficits in behavioural inhibition in substance abuse and addiction: a meta-analysis. Drug Alcohol Depend. 2014;145:1–33.

Lahey BB, Moore TM, Kaczkurkin AN, Zald DH. Hierarchical models of psychopathology: empirical support, implications, and remaining issues. World Psychiatry. 2021;20:57–63.

Moore TM, Kaczkurkin AN, Durham EL, Jeong HJ, McDowell MG, Dupont RM, et al. Criterion validity and relationships between alternative hierarchical dimensional models of general and specific psychopathology. J Abnorm Psychol. 2020;129:677–88.

Caspi A, Houts RM, Belsky DW, Goldman-Mellor SJ, Harrington H, Israel S, et al. The p factor: one general psychopathology factor in the structure of psychiatric disorders? Clin Psychol Sci. 2014;2:119–37.

Johnson SL, Elliott MV, Carver CS. Impulsive responses to positive and negative emotions: parallel neurocognitive correlates and their implications. Biol Psychiatry. 2020;87:338–49.

Bloemen AJP, Oldehinkel AJ, Laceulle OM, Ormel J, Rommelse NNJ, Hartman CA. The association between executive functioning and psychopathology: general or specific? Psychol Med. 2018;48:1787–94.

Harden KP, Engelhardt LE, Mann FD, Patterson MW, Grotzinger AD, Savicki SL, et al. Genetic associations between executive functions and a general factor of psychopathology. J Am Acad Child Adolesc Psychiatry. 2020;59:749–58.

Snyder HR, Friedman NP, Hankin BL. Transdiagnostic mechanisms of psychopathology in youth: executive functions, dependent stress, and rumination. Cogn Ther Res. 2019;43:834–51.

Hatoum AS, Rhee SH, Corley RP, Hewitt JK, Friedman NP. Do executive functions explain the covariance between internalizing and externalizing behaviors? Dev Psychopathol. 2018;30:1371–87.

Shields AN, Reardon KW, Brandes CM, Tackett JL. The p factor in children: relationships with executive functions and effortful control. J Res Pers. 2019;82:103853.

Shanmugan S, Wolf DH, Calkins ME, Moore TM, Ruparel K, Hopson RD, et al. Common and dissociable mechanisms of executive system dysfunction across psychiatric disorders in youth. Am J Psychiatry. 2016;173:517–26.

Xia CH, Ma Z, Ciric R, Gu S, Betzel RF, Kaczkurkin AN, et al. Linked dimensions of psychopathology and connectivity in functional brain networks. Nat Commun. 2018;9:3003.

Goodkind M, Eickhoff SB, Oathes DJ, Jiang Y, Chang A, Jones-Hagata LB, et al. Identification of a common neurobiological substrate for mental illness. JAMA Psychiatry. 2015;72:305–15.

Cyders MA, Smith GT, Spillane NS, Fischer S, Annus AM, Peterson C. Integration of impulsivity and positive mood to predict risky behavior: development and validation of a measure of positive urgency. Psychol Assess. 2007;19:107–18.

Cyders MA, Coskunpinar A. The relationship between self-report and lab task conceptualizations of impulsivity. J Res Pers. 2012;46:121–4.

Sharma L, Markon KE, Clark LA. Toward a theory of distinct types of “impulsive” behaviors: a meta-analysis of self-report and behavioral measures. Psychol Bull. 2014;140:374–408.

Stahl C, Voss A, Schmitz F, Nuszbaum M, Tüscher O, Lieb K, et al. Behavioral components of impulsivity. J Exp Psychol Gen. 2014;143:850–86.

Duckworth AL, Kern ML. A meta-analysis of the convergent validity of self-control measures. J Res Pers. 2011;45:259–68.

Snyder HR, Friedman NP, Hankin BL. Associations between task performance and self-report measures of cognitive control: shared versus distinct abilities. Assessment. 2021;28:1080–96.

Friedman NP, Banich MT. Questionnaires and task-based measures assess different aspects of self-regulation: Both are needed. Proc Natl Acad Sci USA. 2019;116:24396–7.

Ellingson JM, Corley R, Hewitt JK, Friedman NP. A prospective study of alcohol involvement and the dual‐systems model of adolescent risk‐taking during late adolescence and emerging adulthood. Addiction. 2019;114:653–61.

Castellanos-Ryan N, Brière FN, O’Leary-Barrett M, Banaschewski T, Bokde A, Bromberg U, et al. The structure of psychopathology in adolescence and its common personality and cognitive correlates. J Abnorm Psychol. 2016;125:1039.

Korponay C, Dentico D, Kral T, Ly M, Kruis A, Goldman R, et al. Neurobiological correlates of impulsivity in healthy adults: lower prefrontal gray matter volume and spontaneous eye-blink rate but greater resting-state functional connectivity in basal ganglia-thalamo-cortical circuitry. NeuroImage. 2017;157:288–96.

Ang Y-S, Lockwood P, Apps MAJ, Muhammed K, Husain M. Distinct subtypes of apathy revealed by the Apathy Motivation Index. PLoS One. 2017;12:e0169938.

Foa EB, Huppert JD, Leiberg S, Langner R, Kichic R, Hajcak G, et al. The obsessive-compulsive inventory: development and validation of a short version. Psychol Assess. 2002;14:485–96.

Gillan CM, Kosinski M, Whelan R, Phelps EA, Daw ND. Characterizing a psychiatric symptom dimension related to deficits in goal-directed control. ELife. 2016;5:e11305.

Ersche KD, Jones PS, Williams GB, Turton AJ, Robbins TW, Bullmore ET. Abnormal brain structure implicated in stimulant drug addiction. Science. 2012;335:601–4.

Gustavson DE, Stallings MC, Corley RP, Miyake A, Hewitt JK, Friedman NP. Executive functions and substance use: relations in late adolescence and early adulthood. J Abnorm Psychol. 2017;126:257–70.

Gustavson DE, Franz CE, Panizzon MS, Reynolds CA, Xian H, Jacobson KC, et al. Genetic and environmental associations among executive functions, trait anxiety, and depression symptoms in middle age. Clin Psychol Sci. 2019;7:127–42.

Jackson NJ, Isen JD, Khoddam R, Irons D, Tuvblad C, Iacono WG, et al. Impact of adolescent marijuana use on intelligence: results from two longitudinal twin studies. Proc Natl Acad Sci USA. 2016;113:E500–E508.

Ross JM, Ellingson JM, Rhee SH, Hewitt JK, Corley RP, Lessem JM, et al. Investigating the causal effect of cannabis use on cognitive function with a quasi-experimental co-twin design. Drug Alcohol Depend. 2020;206:107712.

Meier MH, Caspi A, Danese A, Fisher HL, Houts R, Arseneault L, et al. Associations between adolescent cannabis use and neuropsychological decline: a longitudinal co-twin control study. Addiction. 2018;113:257–65.

Pagliaccio D, Barch DM, Bogdan R, Wood PK, Lynskey MT, Heath AC, et al. Shared predisposition in the association between cannabis use and subcortical brain structure. JAMA Psychiatry. 2015;72:994–1001.

Harper J, Malone SM, Wilson S, Hunt RH, Thomas KM, Iacono WG. The effects of alcohol and cannabis use on the cortical thickness of cognitive control and salience brain networks in emerging adulthood: a co-twin control study. Biol Psychiatry. 2021;89:1012–22.

Wilson S, Malone SM, Thomas KM, Iacono WG. Adolescent drinking and brain morphometry: a co-twin control analysis. Dev Cogn Neurosci. 2015;16:130–8.

Solanto MV, Abikoff H, Sonuga-Barke E, Schachar R, Logan GD, Wigal T, et al. The ecological validity of delay aversion and response inhibition as measures of impulsivity in AD/HD: a supplement to the NIMH multimodal treatment study of AD/HD. J Abnorm Child Psychol. 2001;29:215–28.

Dalley JW, Robbins TW. Fractionating impulsivity: neuropsychiatric implications. Nat Rev Neurosci. 2017;18:158–71.

Cuthbert BN. The RDoC framework: facilitating transition from ICD/DSM to dimensional approaches that integrate neuroscience and psychopathology. World Psychiatry. 2014;13:28–35.

Cholfin JA, Rubenstein JLR. Patterning of frontal cortex subdivisions by Fgf17. Proc Natl Acad Sci USA. 2007;104:7652–7.

Gustavson DE, Panizzon MS, Franz CE, Reynolds CA, Corley RP, Hewitt JK, et al. Integrating verbal fluency with executive functions: Evidence from twin studies in adolescence and middle age. J Exp Psychol Gen. 2019;148:2104–19.

Savage JE, Jansen PR, Stringer S, Watanabe K, Bryois J, de Leeuw CA, et al. Genome-wide association meta-analysis in 269,867 individuals identifies new genetic and functional links to intelligence. Nat Genet. 2018;50:912–9.

Sniekers S, Stringer S, Watanabe K, Jansen PR, Coleman JRI, Krapohl E, et al. Genome-wide association meta-analysis of 78,308 individuals identifies new loci and genes influencing human intelligence. Nat Genet. 2017;49:1107–12.

Davies G, Lam M, Harris SE, Trampush JW, Luciano M, Hill WD, et al. Study of 300,486 individuals identifies 148 independent genetic loci influencing general cognitive function. Nat Commun. 2018;9:2098.

Hill WD, Marioni RE, Maghzian O, Ritchie SJ, Hagenaars SP, McIntosh AM, et al. A combined analysis of genetically correlated traits identifies 187 loci and a role for neurogenesis and myelination in intelligence. Mol Psychiatry. 2019;24:169–81.

Buniello A, MacArthur JAL, Cerezo M, Harris LW, Hayhurst J, Malangone C, et al. The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Res. 2019;47:D1005–D1012.

Ibrahim-Verbaas CA, Bressler J, Debette S, Schuur M, Smith AV, Bis JC, et al. GWAS for executive function and processing speed suggests involvement of the CADM2 gene. Mol Psychiatry. 2016;21:189–97.

Friedman NP, Keller MC, Banich MT. Twin studies to GWAS: there and back again. Trends Cogn Sci. 2021; https://doi.org/10.1016/j.tics.2021.06.007 .

Article   PubMed   Google Scholar  

Border R, Johnson EC, Evans LM, Smolen A, Berley N, Sullivan PF, et al. No support for historical candidate gene or candidate gene-by-interaction hypotheses for major depression across multiple large samples. Am J Psychiatry. 2019;176:376–87.

Hatoum AS, Morrison CL, Mitchell EC, Lam M, Benca-Bachman CE, Reineberg AE, et al. Genome-wide association study of over 427,000 individuals establishes executive functioning as a neurocognitive basis of psychiatric disorders influenced by GABAergic processes. BioRxiv https://www.biorxiv.org/content/10.1101/674515v2 , 2020:674515.

Grasby KL, Jahanshad N, Painter JN, Colodro-Conde L, Bralten J, Hibar DP, et al. The genetic architecture of the human cerebral cortex. Science. 2020;367:1340.

Volkow ND, Koob GF, Croyle RT, Bianchi DW, Gordon JA, Koroshetz WJ, et al. The conception of the ABCD study: from substance use to a broad NIH collaboration. Dev Cogn Neurosci. 2018;32:4–7.

Preuss TM, Wise SP. Evolution of prefrontal cortex. Neuropsychopharmacol. 2022. In press.

Tottenham N, Tanaka JW, Leon AC, McCarry T, Nurse M, Hare TA, et al. The NimStim set of facial expressions: judgments from untrained research participants. Psychiatry Res. 2009;168:242–9.

Fournier-Vicente S, Larigauderie P, Gaonac’h D. More dissociations and interactions within central executive functioning: a comprehensive latent-variable analysis. Acta Psychol. 2008;129:32–48.

Fisk JE, Sharp CA. Age-related impairment in executive functioning: updating, inhibition, shifting, and access. J Clin Exp Neuropsychol. 2004;26:874–90.

Flavell JH. Metacognition and cognitive monitoring: a new area of cognitive–developmental inquiry. Am Psychol. 1979;34:906–11.

Stuss DT, Alexander MP. Is there a dysexecutive syndrome? Philos Trans R Soc Lond B Biol Sci. 2007;362:901–15.

Stuss DT. Functions of the frontal lobes: relation to executive functions. J Int Neuropsychol Soc. 2011;17:759–65.

Shallice T, Gillingham SM. On neuropsychological studies of prefrontal cortex: the Robbia approach. In: Stuss DT, Knight RT, editors. Principles of frontal lobe function, 2nd edn, New York, NY: Oxford University Press; 2013.

Download references

NPF is currently supported by NIH grants DA046064, DA046413, DA042742, DA051018, MH117131, AG046938, and HD078532. This research was funded in part, by the Wellcome Trust (Grant 104631/Z/14/Z to TWR). For the purpose of open access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. TWR also acknowledges a National Research Foundation (Singapore) CREATE Grant to the University of Cambridge and Nanyang Technological University for the Centre of Lifelong Learning and Individualised Cognition (CLIC). He discloses consultancy and royalties from Cambridge Cognition, consultancy with Arcadia, Greenfield Bioventures, Takeda, Merck Sharp & Dohne, Lundbeck, and research grants from Shionogi and GlaxoSmithKline.

Author information

Authors and affiliations.

Department of Psychology & Neuroscience and Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, USA

Naomi P. Friedman

Department of Psychology and Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK

Trevor W. Robbins

You can also search for this author in PubMed   Google Scholar

Contributions

NPF and TWR co-wrote the manuscript.

Corresponding authors

Correspondence to Naomi P. Friedman or Trevor W. Robbins .

Ethics declarations

Competing interests.

The author declares no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Friedman, N.P., Robbins, T.W. The role of prefrontal cortex in cognitive control and executive function. Neuropsychopharmacol. 47 , 72–89 (2022). https://doi.org/10.1038/s41386-021-01132-0

Download citation

Received : 11 February 2021

Revised : 20 July 2021

Accepted : 22 July 2021

Published : 18 August 2021

Issue Date : January 2022

DOI : https://doi.org/10.1038/s41386-021-01132-0

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

This article is cited by

Motivation and emotional distraction interact and affect executive functions.

  • Michael K. Yeung
  • Jaden Cheuk-Hei Wan
  • Winnie Wing-Yi Siu

BMC Psychology (2024)

Longitudinal microstructural changes in 18 amygdala nuclei resonate with cortical circuits and phenomics

  • Karam Ghanem
  • Karin Saltoun
  • Danilo Bzdok

Communications Biology (2024)

Orthogonal neural encoding of targets and distractors supports multivariate cognitive control

  • Harrison Ritz
  • Amitai Shenhav

Nature Human Behaviour (2024)

Immersion in nature enhances neural indices of executive attention

  • Amy S. McDonnell
  • David L. Strayer

Scientific Reports (2024)

Mapping Gilles de la Tourette syndrome through the distress and relief associated with tic-related behaviors: an fMRI study

  • Laura Zapparoli
  • Francantonio Devoto
  • Eraldo Paulesu

Translational Psychiatry (2024)

Quick links

  • Explore articles by subject
  • Guide to authors
  • Editorial policies

critical thinking and executive function

Sushiman/Shutterstock

Executive Function

Executive Control Network

Reviewed by Psychology Today Staff

Executive function describes a set of cognitive processes and mental skills that help an individual plan, monitor, and successfully execute their goals . The “executive functions,” as they’re known, include attentional control, working memory , inhibition, and problem-solving, many of which are thought to originate in the brain’s prefrontal cortex.

  • Understanding Executive Function
  • Executive Functioning Problems
  • Improving Executive Function

ANURAK PONGPATIMET/Shutterstock

Many behaviors in which humans engage, such as breathing or stepping out of the way of an oncoming car, occur without conscious thought. Most others, however, rely on executive function. Any process or goal pursuit that requires time management , decision-making , and storing information in one’s memory makes use of executive function to some degree. Since much of modern life is process-driven and demands that individuals set and meet goals, disruptions in executive function can make it challenging for someone to succeed in school, at work, or in the household.

Many experts believe that t he human mind contains seven different executive functions . These include self-awareness, inhibition, nonverbal working memory ( short-term memory related to sensory and spatial information), verbal working memory (short-term memory related to speech and language), emotional regulation , motivational regulation, and planning and problem-solving. 

Studies have found consistent overlap between executive functioning and general intelligence scores; some researchers have even proposed that executive functioning may better predict success than does IQ  across a wide array of disciplines. However, some high-IQ individuals struggle with executive functions; thus, there is clearly more to intelligence than executive functioning alone.

The executive functions start to appear in the first year of a child’s life and develop rapidly in the elementary school years. For most people, they will continue to develop into the mid-20s or even early 30s . Children and teens who lag behind their peers in executive functioning may find that they have fewer challenges once they enter adulthood.

critical thinking and executive function

Someone who struggles with executive functioning will likely have trouble starting or finishing tasks, executing multiple steps of a project in sequence, and keeping their belongings organized. They may struggle to make decisions or lose important items frequently.

Issues with impulse or emotional control are a less obvious sign of an executive functioning deficit. Someone with underdeveloped executive functioning may act without thinking and may appear overly emotional at times; this is because both behavioral and emotional inhibition are key aspects of executive functioning.

Executive dysfunction—sometimes called executive function disorder, or EFD—may appear similar to ADHD ; indeed, some experts posit that ADHD is itself a disorder of executive function. People with ADHD—especially children—usually struggle with one or more executive functions, in addition to other symptoms such as hyperactivity and distractibility.

The term “executive function disorder,” or EFD, describes a condition in which a child or adult struggles significantly with planning, problem-solving, or other aspects of executive function. EFD is not currently an official diagnosis in the DSM-5 , though executive function-related symptoms do appear in other DSM conditions.

The cause of poor executive functioning is not always clear. Like other developmental challenges such as ADHD, the cause is likely a combination of genetics , prenatal exposure to drugs or alcohol , early childhood trauma , or other factors. Sometimes, there is no discernible cause.

Someone with executive functioning challenges will find it more difficult than others in their age group to remember information, plan and execute tasks, keep items and information organized, and maintain motivation . They may also struggle with emotional, impulse, or attentional control.

No, though many experts believe the two are closely related. Though many with ADHD will struggle with one or more executive functions , the core symptoms of ADHD—hyperactivity, impulsivity, and distractibility—are not solely related to executive functioning. What’s more, executive function difficulties can co-occur with other developmental and mood disorders, including autism or depression .

Executive function disorder, or EFD, is not an official diagnosis. However, it is possible—and in fact, quite likely—for someone with ADHD to also have significant challenges with executive functioning.

Children can be disorganized because of ADHD, disobedience, or simply because they’re not interested in neatness. However, some children who wish to be organized but find it difficult may have poor executive functioning . These children may struggle with the motivation, problem-solving, and planning that are required for staying organized.

critical thinking and executive function

The ability to plan, problem-solve, organize, and execute can help children and adults in many domains in life. Thus, improving these skills is often a key interest for parents and adults. For some who struggle with executive function, accommodations at work or school can help fill the gaps; strategies aimed specifically at areas of weakness can also be of great help.

However, it’s important to remember that executive function is among the slowest mental processes to develop. Thus, many children who struggle with executive function may find that their skills naturally catch up over time and continue to improve well into adulthood.

Yes. Most children and teens who are behind their peers in executive function will continue to improve with time, particularly if offered specific strategies for doing so; many will catch up by the time they reach adulthood. Adults may find progress to be slower but can also improve executive functions using targeted strategies and accommodations. 

Strategies for improving executive function include: breaking a larger task into smaller chunks; externalizing information using to-do lists, notepads, or phone reminders; buddying up with a peer to foster accountability; blocking access to distractions (putting one’s phone in a drawer or blocking tempting websites); and using rewards to motivate periods of consistent effort.

Many children who struggle to keep track of tasks and responsibilities find the simple act of writing them down—and thus externalizing them—to be hugely helpful. Working with the teacher if necessary, parents can help their child establish a consistent routine for writing down tasks, planning the steps for completion, and rewarding themselves when successful.

Yes. Adults should identify which specific executive functions they wish to strengthen —whether planning, problem-solving, working memory, or emotional regulation—when deciding which strategy to use. For example, adults who struggle with motivation can devise a reward system for successfully completing a task, while those who struggle with impulse control can establish consistent routines to strengthen inhibition.

critical thinking and executive function

Useful strategies for helping your neurodivergent student achieve academic success during the end-of-the-school-year slump.

critical thinking and executive function

Looking at a well-devised to-do list can be overwhelming. These ten simple tips and tricks offer options to break the gridlock.

critical thinking and executive function

Would you like to improve recall, emotional control, and productivity? Learn how to think about working memory differently and apply four key strategies for improving it.

critical thinking and executive function

Sometimes when you can't concentrate it's because something else really needs your attention.

critical thinking and executive function

Readers love their reading lists, but do they cause stress? With the potential to cause stress, guilt, and overwhelm, it begs the question: Should readers ditch their book list?

critical thinking and executive function

Discover the powerful link between your heart, brain, and gut for better cognition and a sharper, more resilient mind.

critical thinking and executive function

Who do you want to be, and what kind of life do you want to lead? What steps can we take to find meaning, satisfaction, and joy in our lives?”

critical thinking and executive function

Changing habits can trigger resistance. Resistance to changing our eating and exercise habits can present unique challenges.

critical thinking and executive function

Technology has made our brains more machine-like. What are we losing when they act as human routers rather than for reading, contemplating, critiquing, synthesizing, and retaining?

critical thinking and executive function

New research suggests that moving to the beat for three minutes can boost brain power for some (but not all) people.

  • Find a Therapist
  • Find a Treatment Center
  • Find a Psychiatrist
  • Find a Support Group
  • Find Teletherapy
  • United States
  • Brooklyn, NY
  • Chicago, IL
  • Houston, TX
  • Los Angeles, CA
  • New York, NY
  • Portland, OR
  • San Diego, CA
  • San Francisco, CA
  • Seattle, WA
  • Washington, DC
  • Asperger's
  • Bipolar Disorder
  • Chronic Pain
  • Eating Disorders
  • Passive Aggression
  • Personality
  • Goal Setting
  • Positive Psychology
  • Stopping Smoking
  • Low Sexual Desire
  • Relationships
  • Child Development
  • Therapy Center NEW
  • Diagnosis Dictionary
  • Types of Therapy

March 2024 magazine cover

Understanding what emotional intelligence looks like and the steps needed to improve it could light a path to a more emotionally adept world.

  • Coronavirus Disease 2019
  • Affective Forecasting
  • Neuroscience

Book cover

Positive Neuropsychology pp 187–221 Cite as

Promoting the Executive Functions: Core Foundations, Assessment Considerations, and Practical Applications

  • John J. Randolph 2 , 3 &
  • Naomi S. Chaytor 4  
  • First Online: 05 November 2022

483 Accesses

The executive functions (EFs) comprise an interrelated set of higher-order cognitive abilities associated with goal-oriented behavior and emotional and social functioning. The present chapter considers a spectrum of issues related to EFs, beginning with theoretical foundations and concluding with practical strategies for promoting the EFs in daily life. In particular, we review theoretical, clinical, and empirical frameworks of EFs and examine EF assessment approaches and related dilemmas. We clarify EF assessment use patterns among practitioners in the context of ecological validity. We then discuss research on interventions designed to support or promote the EFs and consider evidence-based external and internal strategies useful in clinical practice. A final section considers future directions that may lead to more effective EF assessment and promotion.

This is a preview of subscription content, log in via an institution .

Buying options

  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
  • Available as EPUB and PDF
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
  • Durable hardcover edition

Tax calculation will be finalised at checkout

Purchases are for personal use only

Alt, M., Fox, A., Levy, R., Hogan, T. P., Cowan, N., & Gray, S. (2022). Phonological working memory and central executive function differ in children with typical development and dyslexia. Dyslexia, 28 (1), 20–39. https://doi.org/10.1002/dys.1699

Article   PubMed   Google Scholar  

Ambrosini, E., Arbula, S., Rossato, C., Pacella, V., & Vallesi, A. (2019). Neuro-cognitive architecture of executive functions: A latent variable analysis. Cortex, 119 , 441–456. https://doi.org/10.1016/j.cortex.2019.07.013

Amieva, H., Phillips, L., & Della Sala, S. (2003). Behavioral dysexecutive symptoms in normal aging. Brain and Cognition, 53 , 129–132. https://doi.org/10.1016/S0278-2626(03)00094-0

Anderson-Hanley, C., Arciero, P. J., Brickman, A. M., Nimon, J. P., Okuma, N., Westen, S. C., Merz, M. E., Pence, B. D., Woods, J. A., Kramer, A. F., & Zimmerman, E. A. (2012). Exergaming and older adult cognition: A cluster randomized clinical trial. American Journal of Preventive Medicine, 42 (2), 109–119. https://doi.org/10.1016/j.amepre.2011.10.016

Arnett, P. A. (Ed.). (2012). Secondary influences on neuropsychological test performance: Research findings and practical applications . Oxford University Press.

Google Scholar  

Arnett, P. A., Higginson, C., & Randolph, J. J. (2001). Depression in multiple sclerosis: Relationship to planning ability and other executive skills. Journal of the International Neuropsychological Society, 7 , 665–674. https://doi.org/10.1017/s1355617701766027

Baddeley, A. D. (1986). Working memory . Oxford University Press.

Baddeley, A. D. (2012). Working memory: Theories, models, and controversies. Annual Review of Psychology, 63 , 1–29. https://doi.org/10.1146/annurev-psych-120710-100422

Baddeley, A. D., & Hitch, G. J. (1994). Developments in the concept of working memory. Neuropsychology, 8 , 485–493. https://doi.org/10.1037/0894-4105.8.4.485

Article   Google Scholar  

Barkley, R. A. (1997). ADHD and the nature of self-control . Guilford Press.

Bell-McGinty, S., Podell, K., Franzen, M., Baird, A. D., & Williams, M. J. (2002). Standard measures of executive function in predicting instrumental activities of daily living in older adults. International Journal of Geriatric Psychiatry, 17 , 828–834. https://doi.org/10.1002/gps.646

Bennett-Levy, J., Klein-Boonschate, M. A., Batchelor, J., McCarter, R., & Walton, N. (1994). Encounters with Anna Thompson: The consumer’s experience of neuropsychological assessment. The Clinical Neuropsychologist, 8 (2), 219–238. https://doi.org/10.1080/13854049408401559

Benton, A. L., & deS Hamsher, K. (1989). Multilingual aphasia examination . AJA Associates.

Berberian, A. A., Gadelha, A., Dias, N. M., Mecca, T. P., Comfort, W. E., Bressan, R. A., & Lacerda, A. T. (2019). Component mechanisms of executive function in schizophrenia and their contribution to functional outcomes. Brazilian Journal of Psychiatry, 41 (1), 22–30. https://doi.org/10.1590/1516-4446-2018-0021

Bodenburg, S., & Dopslaff, N. (2008). The Dysexecutive Questionnaire: Advanced item and test score characteristics, 4-factor solution, and severity classification. Journal of Nervous and Mental Disease, 196 (1), 75–78. https://doi.org/10.1097/NMD.0b013e31815faa2b

Boone, K. B., Goruch, R. L., Gonzalez, J. J., & Miller, B. L. (1998). Factor analysis of four measures of prefrontal lobe functioning. Archives of Clinical Neuropsychology, 13 (7), 585–595.

Bouazzaoui, B., Isingrini, M., Fay, S., Angel, L., Vanneste, S., Clarys, D., & Taconnat, L. (2010). Aging and self-reported internal and external memory strategy uses: The role of executive functioning. Acta Psychologica, 135 , 59–66. https://doi.org/10.1016/j.actpsy.2010.05.007

Bowden, S. C., Fowler, K. S., Bell, R. C., Whelan, G., Clifford, C. C., Ritter, A. J., & Long, C. M. (1998). The reliability and internal validity of the Wisconsin Card Sorting Test. Neuropsychological Rehabilitation, 8 , 243–254.

Branco, L. D., Cotrena, C., Shansis, F. M., & Fonseca, R. P. (2021). Cognitive abilities underlying performance on the modified card sorting test: Novel and traditional scores. Applied Neuropsychology: Adult, 28 (5), 544–555. https://doi.org/10.1080/23279095.2019.1663522

Brasure, M., Desai, P., Davila, H., Nelson, V. A., Calvert, C., Jutkowitz, E., Butler, M., Fink, H. A., Ratner, E., Hemmy, L. S., McCarten, J. R., Barclay, T. R., & Kane, R. L. (2018). Physical activity interventions in preventing cognitive decline and Alzheimer-type dementia: A systematic review. Annals of Internal Medicine, 168 , 30–38. https://doi.org/10.7326/M17-1528

Braun, S. E., Lanoye, A., Aslanzadeh, F. J., & Loughan, A. R. (2021). Subjective executive dysfunction in patients with primary brain tumors and their informants: Relationships with neurocognitive, psychological, and daily functioning. Brain Injury, 35 (14), 1665–1673. https://doi.org/10.1080/02699052.2021.2008492

Burgess, P. W., Alderman, N., Wilson, B. A., Evans, J. J., & Emslie, H. (1996). The dysexecutive questionnaire. In B. A. Wilson, N. Alderman, P. W. Burgess, H. Emslie, & J. J. Evans (Eds.), Behavioural assessment of the dysexecutive syndrome . Thames Valley Test Company.

Burgess, P. W., Alderman, N., Evans, J., Emslie, H., & Wilson, B. A. (1998). The ecological validity of tests of executive function. Journal of the International Neuropsychological Society, 4 , 547–558. https://doi.org/10.1017/s1355617798466037

Burgess, P. W., Veitch, E., de Lacy Costello, A., & Shallice, T. (2000). The cognitive and neuroanatomical correlates of multitasking. Neuropsychologia, 38 , 848–863. https://doi.org/10.1016/s0028-3932(99)00134-7

Burgess, P. W., Alderman, N., Forbes, C., Costello, A., Coates, L. M.-A., Dawson, D. R., Anderson, N. D., Gilbert, S. J., Dumontheil, I., & Channon, S. (2006). The case for the development and use of “ecologically valid” measures of executive function in experimental and clinical neuropsychology. Journal of the International Neuropsychological Society, 12 , 194–209. https://doi.org/10.1017/S1355617706060310

Burke, J., Danick, J., Bemis, B., & Durgin, C. (1994). A process approach to memory book training for neurological patients. Brain Injury, 8 , 71–81. https://doi.org/10.3109/02699059409150960

Busch, R. M., McBride, A., Curtiss, G., & Vanderploeg, R. D. (2005). The components of executive functioning in traumatic brain injury. Journal of Clinical and Experimental Neuropsychology, 27 , 1022–1032. https://doi.org/10.1080/13803390490919263

Caballero, H. S., McFall, G. P., Wiebe, S. A., & Dixon, R. A. (2021). Integrating three characteristics of executive function in non-demented aging: Trajectories, classification, and biomarker predictors. Journal of the International Neuropsychological Society, 27 , 158–171. https://doi.org/10.1017/S1355617720000703

Cahn-Weiner, D. A., Malloy, P. F., Boyle, P. A., Marran, M., & Salloway, S. (2000). Prediction of functional status from neuropsychological tests in community-dwelling elderly individuals. The Clinical Neuropsychologist, 14 , 187–195. https://doi.org/10.1076/1385-4046(200005)14:2;1-Z;FT187

Callahan, C. D. (2001). The assessment and rehabilitation of executive function disorders. In B. Johnstone & H. H. Stonington (Eds.), Rehabilitation of neuropsychological disorders: A practical guide for rehabilitation professionals (pp. 75–106). Psychology Press.

Carlson, M. C., Saczynski, J. S., Rebok, G. W., Seeman, T., Glass, T. A., McGill, S., Tielsch, J., Frick, K. D., Hill, J., & Fried, L. P. (2008). Exploring the effects of an “everyday” activity program on executive function and memory in older adults: Experience Corps ® . The Gerontologist, 48 (6), 793–801. https://doi.org/10.1093/geront/48.6.793

Carpenter, R. W., Wycoff, A. M., & Trull, T. J. (2016). Ambulatory assessment: New adventures in characterizing dynamic processes. Assessment, 23 (4), 414–424. https://doi.org/10.1177/1073191116632341

Article   PubMed   PubMed Central   Google Scholar  

Carvalho, J. O., Ready, R. E., Malloy, P., & Grace, J. (2013). Confirmatory factor analysis of the Frontal Systems Behavior Scale (FrSBe). Assessment, 20 (5), 632–641. https://doi.org/10.1177/1073191113492845

Castellanos, F. X., Sonuga-Barke, E. J. S., Milham, M. P., & Tannock, R. (2006). Characterizing cognition in ADHD: Beyond executive dysfunction. Trends in Cognitive Sciences, 10 , 117–123. https://doi.org/10.1016/j.tics.2006.01.011

Chan, R. C. K. (2001). Dysexecutive symptoms among a non-clinical sample: A study with the use of the Dysexecutive Questionnaire. British Journal of Psychology, 92 , 551–565.

Chavez-Arana, C., Catroppa, C., Carranza-Escárcega, E., Godfrey, C., Yáñez-Téllez, G., Prieto-Corona, B., de León, M. A., & Anderson, V. (2018). A systematic review of interventions for hot and cold executive functions in children and adolescents with acquired brain injury. Journal of Pediatric Psychology, 43 (8), 928–942. https://doi.org/10.1093/jpepsy/jsy013

Chaytor, N. S., & Fonseca, L. M. (2022). Ambulatory assessment. In T. D. Marcotte, M. Schmitter-Edgecombe, & I. Grant (Eds.), Neuropsychology of everyday functioning (2nd ed., pp. 311–335). Guilford Press.

Chaytor, N., & Schmitter-Edgecombe, M. (2003). The ecological validity of neuropsychological tests: A review of the literature on everyday cognitive skills. Neuropsychology Review, 13 (4), 181–197. https://doi.org/10.1023/b:nerv.0000009483.91468.fb

Chaytor, N., & Schmitter-Edgecombe, M. (2007). Fractionation of the dysexecutive syndrome in a heterogeneous neurological sample: Comparing the Dysexecutive Questionnaire and the Brock Adaptive Functioning Questionnaire. Brain Injury, 21 (6), 615–621. https://doi.org/10.1080/02699050701426949

Chaytor, N., Schmitter-Edgecombe, M., & Burr, R. (2006). Improving the ecological validity of executive functioning assessment. Archives of Clinical Neuropsychology, 21 , 217–227. https://doi.org/10.1016/j.acn.2005.12.002

Chaytor, N., Temkin, N., Machamer, J., & Dikmen, S. (2007). The ecological validity of neuropsychological assessment and the role of depressive symptoms in moderate to severe traumatic brain injury. Journal of the International Neuropsychological Society, 13 , 377–385. https://doi.org/10.1017/S1355617707070592

Chaytor, N., Ciechanowski, P., Miller, J. W., Fraser, R., Russo, J., Unutzer, J., & Gilliam, F. (2011). Long-term outcomes from the PEARLS randomized trial for the treatment of depression in patients with epilepsy. Epilepsy and Behavior, 20 , 545–549. https://doi.org/10.1016/j.yebeh.2011.01.017

Chaytor, N. S., Riddlesworth, T. D., Bzdick, S., Odegard, P. S., Gray, S. L., Lock, J. P., DuBose, S. N., Beck, R. W., & T1D Exchange Severe Hypoglycemia in Older Adults with Type 1 Diabetes Study Group. (2017). The relationship between neuropsychological assessment, numeracy, and functional status in older adults with type 1 diabetes. Neuropsychological Rehabilitation, 27 (4), 507–521. https://doi.org/10.1080/09602011.2015.1116448

Chaytor, N. S., Barbosa-Leiker, C., Germine, L. T., Fonseca, L. M., McPherson, S. M., & Tuttle, K. R. (2021). Construct validity, ecological validity and acceptance of self-administered online neuropsychological assessment in adults. The Clinical Neuropsychologist, 35 (1), 148–164. https://doi.org/10.1080/13854046.2020.1811893

Chelune, G. J. (1985). Toward a neuropsychological model of everyday functioning. Psychotherapy in Private Practice, 3 , 39–44. https://doi.org/10.1300/J294v03n03_08

Chen, S. T., Sultzer, D. L., Hinkin, C. H., Mahler, M. E., & Cummings, J. L. (1998). Executive dysfunction in Alzheimer’s disease: Association with neuropsychiatric symptoms and functional impairment. Journal of Neuropsychiatry and Clinical Neurosciences, 10 (4), 426–432. https://doi.org/10.1176/jnp.10.4.426

Cicerone, K.D. (2005). Evidence-based practice and the limits of rational rehabilitation. Archives of Physical Medicine and Rehabilitation, 86 (6), 1073–1074. https://doi.org/10.1016/j.apmr.2005.01.003

Cicerone, K. D., & Giacino, J. T. (1992). Remediation of executive function deficits after traumatic brain injury. Neurorehabilitation, 2 , 73–83. https://doi.org/10.3233/NRE-1992-2304

Cicerone, K. D., Dahlberg, C., Kalmar, K., Langenbahn, D. M., Malec, J. F., Bergquist, T. F., Felicetti, T., Giacino, J. T., Harley, J. P., Harrington, D. E., Herzog, J., Kneipp, S., Laatsch, L., & Morse, P. A. (2000). Evidence-based cognitive rehabilitation: Recommendations for clinical practice. Archives of Physical Medicine and Rehabilitation, 81 (12), 1596–1615. https://doi.org/10.1053/apmr.2000.19240

Cicerone, K. D., Dahlberg, C., Malec, J. F., Langenbahn, D. M., Felicetti, T., Kneipp, S., Ellmo, W., Kalmar, K., Giancino, J. T., Harley, J. P., Laatsch, L., Morse, P. A., & Catanese, J. (2005). Evidence-based cognitive rehabilitation: Updated review of the literature from 1998 through 2002. Archives of Physical Medicine and Rehabilitation, 86 (8), 1681–1692. https://doi.org/10.1016/j.apmr.2005.03.024

Cicerone, K. D., Langenbahn, D. M., Braden, C., Malec, J. F., Kalmar, K., Fraas, M., … Ashman, T. (2011). Evidence-based cognitive rehabilitation: Updated review of the literature from 2003 through 2008. Archives of Physical Medicine and Rehabilitation, 92 , 519–530. https://doi.org/10.1016/j.apmr.2010.11.015

Cicerone, K. D., Goldin, Y., Ganci, K., Rosenbaum, A., Wethe, J. V., Langenbahn, D. M., Malec, J. F., Bergquist, T. F., Kingsley, K., Nagele, D., Trexler, L., Fraas, M., Bogdanova, Y., & Harley, J. P. (2019). Evidence-based cognitive rehabilitation: Systematic review of the literature from 2009 through 2014. Archives of Physical Medicine and Rehabilitation, 100 (8), 1515–1533. https://doi.org/10.1016/j.apmr.2019.02.011

Cirino, P. T., Ahmed, Y., Miciak, J., Taylor, W. P., Gerst, E. H., & Barnes, M. A. (2018). A framework for executive function in the late elementary years. Neuropsychology, 32 (2), 176–189. https://doi.org/10.1037/neu0000427

Cirulli, E. T., Attix, D. K., Smith, P. J., Chiba-Falek, O., Pennuto, T. O., Linney, K. N., & Goldstein, D. B. (2011). Contribution of pastimes and testing strategies to the performance of healthy volunteers on cognitive tests. The Clinical Neuropsychologist, 25 (5), 778–798. https://doi.org/10.1080/13854046.2011.578587

Colcombe, S., & Kramer, A. F. (2003). Fitness effects on the cognitive function of older adults: A meta-analytic study. Psychological Science, 14 , 125–130. https://doi.org/10.1111/1467-9280.t01-1-01430

Cormier, W. H., & Cormier, L. S. (1985). Interviewing strategies for helpers: Fundamental skills and cognitive-behavioral interventions (2nd ed.). Brooks/Cole Publishing.

Cox, E. P., O’Dwyer, N., Cook, R., Vetter, M., Cheng, H. L., Rooney, K., & O’Connor, H. (2016). Relationship between physical activity and cognitive function in apparently healthy young to middle-aged adults: A systematic review. Journal of Science and Medicine in Sport, 19 , 616–628. https://doi.org/10.1016/j.jsams.2015.09.003

D’Esposito, M., Detre, J. A., Alsop, D. C., Shin, R. K., Atlas, S., & Grossman, M. (1995). The neural basis of the central executive system of working memory. Nature, 378 , 279–281. https://doi.org/10.1038/378279a0

Davis, C. L., Tomporowski, P. D., McDowell, J. E., Austin, B. P., Miller, P. H., Yanasak, N. E., Allison, J. D., & Naglieri, J. A. (2011). Exercise improves executive function and achievement and alters brain activation in overweight children: A randomized, controlled trial. Health Psychology, 30 (1), 91–98. https://doi.org/10.1037/a0021766

Dawson, P. (2021). Helping children and teens strengthen executive skills to reach their full potential. Archives of Clinical Neuropsychology, 36 , 1279–1282. https://doi.org/10.1093/arclin/acab057

de Frias, C. M., & Dixon, R. A. (2014). Lifestyle engagement affects cognitive status differences and trajectories on executive functions in older adults. Archives of Clinical Neuropsychology, 29 , 16–25. https://doi.org/10.1093/arclin/act089

Delis, D. C., Kramer, J. H., Kaplan, E., Ober, B. A., & Fridlund, A. (1987). The California verbal learning test . Pearson.

Delis, D. C., Kaplan, E., & Kramer, J. (2001). Delis-Kaplan executive function system . Pearson.

Della Sala, S., Gray, C., Spinnler, H., & Trivelli, C. (1998). Frontal lobe functioning in man: The riddle revisited. Archives of Clinical Neuropsychology, 13 (8), 663–682.

PubMed   Google Scholar  

Diamond, A. (2013). Executive functions. Annual Review of Psychology, 64 , 135–168. https://doi.org/10.1146/annurev-psych-113011-143750

Dimitriadou, M., Michaelides, M. P., Bateman, A., & Constantinidou, F. (2020). Measurement of everyday dysexecutive symptoms in normal aging with the Greek version of the Dysexecutive Questionnaire-Revised. Neuropsychological Rehabilitation, 30 (6), 1024–1043. https://doi.org/10.1080/09602011.2018.1543127

Dobkin, R. D., Mann, S. L., Gara, M. A., Interian, A., Rodriguez, K. M., & Menza, M. (2020). Telephone-based cognitive behavioral therapy for depression in Parkinson disease: A randomized controlled trial. Neurology, 94 (16), e1764–e1773. https://doi.org/10.1212/WNL.0000000000009292

Dodrill, C. B. (1997). Myths of neuropsychology. The Clinical Neuropsychologist, 11 (1), 1–17. https://doi.org/10.1080/13854049708407025

Donders, J., & Strong, C.-A. (2016). Latent structure of the Behavior Rating Inventory of Executive Function-Adult version (BRIEF-A) after mild traumatic brain injury. Archives of Clinical Neuropsychology, 31 (1), 29–36. https://doi.org/10.1093/arclin/acv048

Donders, J., DenBraber, D., & Vos, L. (2010). Construct and criterion validity of the Behavior Rating Inventory of Executive Function (BRIEF) in children referred for neuropsychological assessment after paediatric traumatic brain injury. Journal of Neuropsychology, 4 (2), 197–209. https://doi.org/10.1348/174866409X478970

Dywan, J., & Segalowitz, S. (1996). Self and family ratings of adaptive behaviour after traumatic brain injury: Psychometric scores and frontally generated ERPs. The Journal of Head Trauma Rehabilitation, 11 , 79–95. https://doi.org/10.1097/00001199-199604000-00008

Egeland, J., & Fallmyr, O. (2010). Confirmatory factor analysis of the Behavior Rating Inventory of Executive Function (BRIEF): Support for a distinction between emotional and behavioral regulation. Child Neuropsychology, 16 (4), 326–337. https://doi.org/10.1080/09297041003601462

Emilsson, B., Gudjonsson, G., Sigurdsson, J. F., Baldursson, G., Einarsson, E., Olafsdottir, H., & Young, S. (2011). Cognitive behaviour therapy in medication-treated adults with ADHD and persistent symptoms: A randomized controlled trial. BMC Psychiatry, 11 , Article 116. https://doi.org/10.1186/1471-244X-11-116

Engel-Yeger, B., & Rosenblum, S. (2021). Executive dysfunctions mediate between altered sensory processing and daily activity performance in older adults. BMC Geriatrics, 21 , Article 132. https://doi.org/10.1186/s12877-021-02032-0

Erickson, K. I., Hillman, C., Stillman, C. M., Ballard, R. M., Bloodgood, B., Conroy, D. E., Macko, R., Marquez, D. X., Petruzzello, S. J., Powell, K. E., & 2018 Physical Activity Guidelines Advisory Committee. (2019). Physical activity, cognition, and brain outcomes: A review of the 2018 Physical Activity Guidelines. Medicine & Science in Sports & Exercise, 51 (6), 1242–1251. https://doi.org/10.1249/MSS.0000000000001936

Ettenhofer, M. L., Hambrick, D. Z., & Abeles, N. (2006). Reliability and stability of executive functioning in older adults. Neuropsychology, 20 (5), 607–613. https://doi.org/10.1037/0894-4105.20.5.607

Evans, J. J., Emslie, H., & Wilson, B. A. (1998). External cueing systems in the rehabilitation of executive impairments of action. Journal of the International Neuropsychological Society, 4 , 399–408.

Fagundo, A. B., Jiménez-Murcia, S., Giner-Bartolomé, C., Islam, M. A., de la Torre, R., Pastor, A., Casanueva, F. F., Crujeiras, A. B., Granero, R., Baños, R., Botella, C., Fernández-Real, J. M., Frühbeck, G., Gómez-Ambrosi, J., Menchón, J. M., Tinahones, F. J., & Aranda-Fernández, F. (2015). Modulation of higher-order olfaction components on executive functions in humans. PLoS ONE, 10 (6), Article e0130319. https://doi.org/10.1371/journal.pone.0130319

Farias, S. T., Schmitter-Edgecombe, M., Weakley, A., Harvey, D., Denny, K. G., Barba, C., Gravano, J. T., Giovannetti, T., & Willis, S. (2018). Compensation strategies in older adults: Association with cognition and everyday function. American Journal of Alzheimer’s Disease and Other Dementias, 33 (3), 184–191. https://doi.org/10.1177/1533317517753361

Ferguson, R. J., Randolph, J. J., & Wishart, H. A. (2004). Memory and Attention Adaptation Training: A brief cognitive-behavioral program for individuals with multiple sclerosis-associated memory and attention problems (Clinician and Patient Manuals).

Ferguson, R. J., Ahles, T. A., Saykin, A. J., McDonald, B. C., Furstenberg, C. T., Cole, B. F., & Mott, L. A. (2007). Cognitive-behavioral management of chemotherapy-related cognitive change. Psycho-oncology, 16 (8), 772–777. https://doi.org/10.1002/pon.1133

Fink, F., Rischkau, E., Butt, M., Klein, J., Eling, P., & Hildebrant, H. (2010). Efficacy of an executive function intervention programme in MS: A placebo-controlled and pseudo-randomized trial. Multiple Sclerosis, 16 (9), 1148–1151. https://doi.org/10.1177/1352458510375440

Fish, J., Manly, T., & Wilson, B. A. (2008). Long-term compensatory treatment of organizational deficits in a patient with bilateral frontal lobe damage. Journal of the International Neuropsychological Society, 14 , 154–163. https://doi.org/10.1017/S1355617708080120

Flavia, M., Stampatori, C., Zanotti, D., Parrinello, G., & Capra, R. (2010). Efficacy and specificity of intensive cognitive rehabilitation of attention and executive functions in multiple sclerosis. Journal of the Neurological Sciences, 288 , 101–105. https://doi.org/10.1016/j.jns.2009.09.024

Franzen, M. D., & Arnett, P. A. (1997). The validity of neuropsychological assessment procedures. In H. W. Reese & M. D. Franzen (Eds.), Biological and neuropsychological mechanisms: Life-span developmental psychology (pp. 51–69). Lawrence Erlbaum Associates.

Fuster, J. (2008). The prefrontal cortex . Academic.

Book   Google Scholar  

Gaertner, B., Buttery, A. K., Finger, J. D., Wolfsgruber, S., Wagner, M., & Busch, M. A. (2018). Physical exercise and cognitive function across the life span: Results of a nationwide population-based study. Journal of Science and Medicine in Sport, 21 , 489–494. https://doi.org/10.1016/j.jsams.2017.08.022

Garcia-Molina, A., Tormos, J. M., Bernabeu, M., Junque, C., & Roig-Rovira, T. (2012). Do traditional executive measures tell us anything about daily-life functioning after traumatic brain injury in Spanish-speaking individuals? Brain Injury, 26 (6), 864–874. https://doi.org/10.3109/02699052.2012.655362

Gates, N. J., Vernooij, R. W. M., Di Nisio, M., Karim, S., March, E., Martínez, G., & Rutjes, A. W. S. (2019). Computerised cognitive training for preventing dementia in people with mild cognitive impairment. Cochrane Database of Systematic Reviews, 2019 (3), Article CD012279. https://doi.org/10.1002/14651858.CD012279.pub2

Germine, L., Reinecke, K., & Chaytor, N. S. (2019). Digital neuropsychology: Challenges and opportunities at the intersection of science and software. The Clinical Neuropsychologist, 33 (2), 271–286. https://doi.org/10.1080/13854046.2018.1535662

Gioia, G. A., Isquith, P. K., Guy, S. C., & Kenworthy, L. (2000). The behavior rating inventory of executive function . Psychological Assessment Resources.

Gioia, G. A., Isquith, P. K., Retzlaff, P. D., & Espy, K. A. (2002). Confirmatory factor analysis of the Behavior Rating Inventory of Executive Function (BRIEF). Child Neuropsychology, 8 (4), 249–257. https://doi.org/10.1076/chin.8.4.249.13513

Glen, T., Hostetter, G., Roebuck-Spencer, T. M., Garmoe, W. S., Scott, J. G., Hilsabeck, R. C., Arnett, P., & Espe-Pfeifer, P. (2020). Return on investment and value research in neuropsychology: A call to arms. Archives of Clinical Neuropsychology, 35 (5), 459–468. https://doi.org/10.1093/arclin/acaa010

Goldman-Rakic, A. R., Cools, A. R., & Srivastava, K. (1996). The prefrontal landscape: Implications of functional architecture for understanding human mentation and the central executive. Philosophical Transactions: Biological Sciences, 351 , 1445–1453. https://doi.org/10.1098/rstb.1996.0129

Goldstein, G. (1996). Functional considerations in neuropsychology. In R. J. Sbordone & C. J. Long (Eds.), Ecological validity of neuropsychological testing (pp. 75–89). GR Press/St. Lucie Press.

Goldstein, G., & McCue, M. (1995). Differences between patient and informant functional outcome ratings in head-injured individuals. International Journal of Rehabilitation and Health, 1 , 25–35.

Gomes-Osman, J., Cabral, D. F., Morris, T. P., McInerney, K., Cahalin, L. P., Rundek, T., Oliveira, A., & Pascual-Leone, A. (2018). Exercise for cognitive brain health in aging: A systematic review for an evaluation of dose. Neurology: Clinical Practice, 8 (3), 1–9. https://doi.org/10.1212/CPJ.0000000000000460

Gorske, T. T., & Smith, S. R. (2009). Collaborative therapeutic neuropsychological assessment . Springer Science+Business Media.

Goverover, Y., Chiaravalloti, N., & DeLuca, J. (2013). The influence of executive functions and memory on self-generation benefit in persons with multiple sclerosis. Journal of Clinical and Experimental Neuropsychology, 35 (7), 775–783. https://doi.org/10.1080/13803395.2013.824553

Goverover, Y., Chiaravalloti, N. D., O’Brien, A. R., & DeLuca, J. (2018). Evidence-based cognitive rehabilitation for persons with multiple sclerosis: An updated review of the literature from 2007 to 2016. Archives of Physical Medicine and Rehabilitation, 99 , 390–407. https://doi.org/10.1016/j.apmr.2017.07.021

Grace, J., & Malloy, P. F. (2001). Frontal Systems Behavior Scale professional manual . Psychological Assessment Resources, Inc.

Grant, I., Olshen, R. A., Atkinson, J. H., Heaton, R. K., Nelson, J., McCutchan, J. A., & Weinrich, J. D. (1993). Depressed mood does not explain neuropsychological deficits in HIV-infected persons. Neuropsychology, 7 (1), 53–61. https://doi.org/10.1037/0894-4105.7.1.53

Gruters, A. A., Ramakers, I. H., Verhey, F. R., Kessels, R. P., & de Vugt, M. E. (2021). A scoping review of communicating neuropsychological test results to patients and family members. Neuropsychology Review . Advance online access. https://doi.org/10.1007/s11065-021-09507-2

Hall, K. E., Isaac, C. L., & Harris, P. (2009). Memory complaints in epilepsy: An accurate reflection of memory impairment or an indicator of poor adjustment? A review of the literature. Clinical Psychology Review, 29 , 354–367. https://doi.org/10.1016/j.cpr.2009.03.001

Halstead, W. C. (1947). Brain and intelligence . University of Chicago Press.

Halvorsen, M., Mathiassen, B., Amundsen, T., Ellingsen, J., Brøndbo, P. H., Sundby, J., Steinsvik, O. O., & Martinussen, M. (2019). Confirmatory factor analysis of the behavior rating inventory of executive function in a neuro-pediatric sample and its application to mental disorders. Child Neuropsychology, 25 (5), 599–616. https://doi.org/10.1080/09297049.2018.1508564

Hanna-Pladdy, B., & MacKay, A. (2011). The relation between instrumental musical activity and cognitive aging. Neuropsychology, 25 (3), 378–386. https://doi.org/10.1037/a0021895

Heaton, R. K., Chelune, G. J., Talley, J. L., Kay, C. G., & Curtiss, G. (1993). Wisconsin Card Sorting Test manual . Psychological Assessment Resources.

Heaton, R. K., Marcotte, T. D., Mindt, M. R., Sadek, J., Moore, D. J., Bentley, H., McCutchan, J. A., Reicks, C., Grant, I., & The HNRC Group. (2004). The impact of HIV-associated neuropsychological impairment on everyday functioning. Journal of the International Neuropsychological Society, 10 , 317–331. https://doi.org/10.1017/S1355617704102130

Heinrichs, R. W. (1990). Current and emergent applications of neuropsychological assessment: Problems with validity and utility. Professional Psychology: Research and Practice, 21 , 171–176. https://doi.org/10.1037/0735-7028.21.3.171

Hill, N. T. M., Mowszowski, L., Naismith, S. L., Chadwick, V. L., Valenzuela, M., & Lampit, A. (2017). Computerized cognitive training in older adults with mild cognitive impairment or dementia: A systematic review and meta-analysis. American Journal of Psychiatry, 174 (4), 329–340. https://doi.org/10.1176/appi.ajp.2016.16030360

Hørlyck, L. D., Obenhausen, K., Jansari, A., Ullum, H., & Miskowiak, K. W. (2021). Virtual reality assessment of daily life executive functions in mood disorders: Associations with neuropsychological and functional measures. Journal of Affective Disorders, 280 , 478–487. https://doi.org/10.1016/j.jad.2020.11.084

Irwin, L. N., Kofler, M. J., Soto, E. F., & Groves, N. B. (2019). Do children with attention-deficit/hyperactivity disorder (ADHD) have set shifting deficits? Neuropsychology, 33 (4), 470–481. https://doi.org/10.1037/neu0000546

Isquith, P. K., Roth, R. M., & Gioia, G. (2013). Contribution of rating scales to the assessment of executive functions. Applied Neuropsychology: Child, 2 (2), 125–132. https://doi.org/10.1080/21622965.2013.748389

Jensen, A. R., & Rohwer, W. D. (1966). The Stroop Color-Word Test: A review. Acta Psychologica, 25 , 36–93.

Johnson, J. K., Lui, L., & Yaffe, K. (2007). Executive function, more than global cognition, predicts functional decline and mortality in elderly women. Journals of Gerontology: Series A, 62 (10), 1134–1141. https://doi.org/10.1093/gerona/62.10.1134

Jones, C. D., Motl, R., & Sandroff, B. M. (2021). Depression in multiple sclerosis: Is one approach for its management enough? Multiple Sclerosis and Related Disorders, 51 , Article 102904. https://doi.org/10.1016/j.msard.2021.102904

Kaitaro, T., Koskinen, S., & Kaipio, M. (1995). Neuropsychological problems in everyday life: A 5-year follow-up study of young severely closed-head-injured patients. Brain Injury, 9 , 713–727. https://doi.org/10.3109/02699059509008227

Kanauss, K., Schatz, P., & Puente, A. E. (2005). Current trends in the reimbursement of professional neuropsychological services. Archives of Clinical Neuropsychology, 20 , 341–353. https://doi.org/10.1016/j.acn.2004.09.002

Kibby, M. Y., Schmitter-Edgecombe, M., & Long, C. J. (1998). Ecological validity of neuropsychological tests: Focus on the California Verbal Learning Test and the Wisconsin Card Sorting Test. Archives of Clinical Neuropsychology, 13 , 523–534.

Kinsinger, S. W., Lattie, E., & Mohr, D. C. (2010). Relationship between depression, fatigue, subjective cognitive impairment, and objective neuropsychological functioning in patients with multiple sclerosis. Neuropsychology, 24 (5), 573–580. https://doi.org/10.1037/a0019222

Knight, C., Alderman, N., & Burgess, P. W. (2002). Development of a simplified version of the Multiple Errands Test for use in hospital settings. Neuropsychological Rehabilitation, 12 , 231–255. https://doi.org/10.1080/09602010244000039

Lambez, B., Harwood-Gross, A., Golumbic, E. Z., & Rassovsky, Y. (2020). Non-pharmacological interventions for cognitive difficulties in ADHD: A systematic review and meta-analysis. Journal of Psychiatry Research, 120 , 40–55. https://doi.org/10.1016/j.jpsychires.2019.10.007

Latzman, R. D., & Markon, K. E. (2010). The factor structure and age-related factorial invariance of the Delis-Kaplan Executive Function System (D-KEFS). Assessment, 17 (2), 172–184. https://doi.org/10.1177/1073191109356254

Levine, B., Robertson, I. H., Clare, L., Carter, G., Hong, J., Wilson, B. A., Duncan, J., & Stuss, D. T. (2000). Rehabilitation of executive functioning: An experimental-clinical validation of goal management training. Journal of the International Neuropsychological Society, 6 , 299–312. https://doi.org/10.1017/s1355617700633052

Lezak, M. D. (1982). The problem of assessing executive functions. International Journal of Psychology, 17 , 281–297. https://doi.org/10.1080/00207598208247445

Lezak, M. D., Howieson, D. B., Bigler, E. D., & Tranel, D. (2012). Neuropsychological assessment (5th ed.). Oxford University Press.

Li, H., Li, J., Li, N., Li, B., Wang, P., & Zhou, T. (2011). Cognitive intervention for persons with mild cognitive impairment: A meta-analysis. Ageing Research Reviews, 10 (2), 285–296. https://doi.org/10.1016/j.arr.2010.11.003

Liao, Y.-Y., Chen, I.-H., Lin, Y.-J., Chen, Y., & Hsu, W.-C. (2019). Effects of virtual reality-based physical and cognitive training on executive function and dual-task gait performance in older adults with mild cognitive impairment: A randomized control trial. Frontiers in Aging Neuroscience, 11 , Article 162. https://doi.org/10.3389/fnagi.2019.00162

Loewenstein, D. A., & Bates, B. C. (1989). The Direct Assessment of Functional Status (DAFS): Manual for administration and scoring . Unpublished manuscript.

Long, C. J., & Kibby, M. Y. (1995). Ecological validity of neuropsychological tests: A look at neuropsychology’s past and the impact that ecological issues may have on its future. Advances in Medical Psychotherapy, 8 , 59–78.

Longley, W. A., Tate, R. L., & Brown, R. F. (2022). The psychological benefits of neuropsychological assessment feedback as a psycho-educational therapeutic intervention: A randomized-controlled trial with cross-over in multiple sclerosis. Neuropsychological Rehabilitation . Advanced online access. https://doi.org/10.1080/09602011.2022.2047734

Maggio, M. G., Russo, M., Cuzzola, M. F., Destro, M., La Rosa, G., Molonia, F., Bramanti, P., Lombardo, G., De Luca, R., & Calabrò, R. S. (2019). Virtual reality in multiple sclerosis rehabilitation: A review on cognitive and motor outcomes. Journal of Clinical Neuroscience, 65 , 106–111. https://doi.org/10.1016/j.jocn.2019.03.017

Manchester, D., Priestley, N., & Jackson, H. (2004). The assessment of executive functions: Coming out of the office. Brain Injury, 18 (11), 1067–1081. https://doi.org/10.1080/02699050410001672387

Manly, T., Hawkins, K., Evans, J., Woldt, K., & Robertson, I. H. (2002). Rehabilitation of executive function: Facilitation of effective goal management on complex tasks using periodic auditory alerts. Neuropsychologia, 40 (3), 271–281. https://doi.org/10.1016/s0028-3932(01)00094-x

Mateer, C. A. (1999). The rehabilitation of executive disorders. In D. T. Stuss, G. Winocur, & I. Robertson (Eds.), Cognitive neurorehabilitation (pp. 314–332). Cambridge University Press.

Mateer, C. A., & Sira, C. S. (2008). Practical rehabilitation strategies in the context of clinical neuropsychology feedback. In J. E. Morgan & J. H. Ricker (Eds.), Textbook of clinical neuropsychology (pp. 996–1007). Taylor and Francis.

McAlister, C., Schmitter-Edgecombe, M., & Lamb, R. (2016). Examination of variables that may affect the relationship between cognition and functional status in individuals with mild cognitive impairment: A meta-analysis. Archives of Clinical Neuropsychology, 31 , 123–147. https://doi.org/10.1093/arclin/acv089

McCabe, J. A., Redick, T. S., & Engle, R. W. (2016). Brain-training pessimism, but applied memory optimism. Psychological Science in the Public Interest, 17 (3), 187–191. https://doi.org/10.1177/1529100616664716

McCue, M., & Pramuka, M. (1998). Functional assessment. In G. Goldstein & S. Beers (Eds.), Rehabilitation . Plenum Press.

McFarland, D. J. (2020). Factor-analytic evidence for the complexity of the Delis-Kaplan Executive Function System (D-KEFS). Assessment, 27 (7), 1645–1656. https://doi.org/10.1177/1073191119843584

McKinney, T. L., Euler, M. J., & Butner, J. E. (2020). It’s about time: The role of temporal variability in improving assessment of executive functioning. The Clinical Neuropsychologist, 34 (4), 619–642. https://doi.org/10.1080/13854046.2019.1704434

Meichenbaum, D. H., & Goodman, J. (1971). Training impulsive children to talk to themselves: A means of developing self-control. Journal of Abnormal Psychology, 77 , 115–126. https://doi.org/10.1037/h0030773

Mitchell, M., & Miller, L. S. (2008). Prediction of functional status in older adults: The ecological validity of four Delis–Kaplan Executive Function System tests. Journal of Clinical and Experimental Neuropsychology, 30 , 683–690. https://doi.org/10.1080/13803390701679893

Miyake, A., Emerson, M. J., & Friedman, N. P. (2000a). Assessment of executive functions in clinical settings: Problems and recommendations. Seminars in Speech and Language, 21 (2), 169–183. https://doi.org/10.1055/s-2000-7563

Miyake, A., Friedman, N. P., Emerson, M. J., Witzki, A. H., Howerter, A., & Wager, T. D. (2000b). The unity and diversity of executive functions and their contribution to complex “frontal lobe” tasks: A latent variable analysis. Cognitive Psychology, 41 , 49–100. https://doi.org/10.1006/cogp.1999.0734

Monette, S., Betrand, J.-A., Perreau-Linck, E., Ramos-Usuga, D., Rivera, D., & Arango-Lasprilla J. C. (2021). The profession of neuropsychologist in Canada: Findings of a National survey. The Clinical Neuropsychologist . Advance online access. https://doi.org/10.1080/13854046.2021.2002934

Mooney, B., Walmsley, C., & McFarland, K. (2006). Factor analysis of the self-report Dysexecutive Questionnaire. Applied Neuropsychology, 13 (1), 12–18. https://doi.org/10.1207/s15324826an1301_2

Muñoz, M. P., & Filippetti, V. A. (2021). Confirmatory factor analysis of the BRIEF-2 parent and teacher form: Relationship to performance-based measures of executive functions and academic achievement. Applied Neuropsychology: Child, 10 (3), 219–233. https://doi.org/10.1080/21622965.2019.1660984

Nguyen, L., Murphy, K., & Andrews, G. (2019). Immediate and long-term efficacy of executive functions cognitive training in older adults: A systematic review and meta-analysis. Psychological Bulletin, 145 (7), 698–733. https://doi.org/10.1037/bul0000196

Norman, D. A., & Shallice, T. (1986). Attention to action: Willed and automatic control of behavior. In R. J. Davidson, G. E. Schwarts, & D. Shapiro (Eds.), Consciousness and self-regulation: Advances in research and therapy (pp. 1–18). Plenum Press.

Nyhus, E., & Barceló, F. (2009). The Wisconsin Card Sorting Test and the cognitive assessment of prefrontal executive functions: A critical update. Brain and Cognition, 71 (3), 437–451. https://doi.org/10.1016/j.bandc.2009.03.005

Ophir, E., Nass, C., & Wagner, A. D. (2009). Cognitive control in media multitaskers. Proceedings of the National Academy of Sciences, 106 , 15583–15587. https://doi.org/10.1073/pnas.0903620106

Pan, M.-R., Huang, F., Zhao, M.-J., Wang, Y.-F., Wang, Y.-F., & Qian, Q.-J. (2019). A comparison of efficacy between cognitive behavioral therapy (CBT) and CBT combined with medication in adults with attention-deficit/hyperactivity Disorder (ADHD). Psychiatry Research, 279 , 23–33. https://doi.org/10.1016/j.psychres.2019.06.040

Poissant, H., Mendrek, A., Talbot, N., Khoury, B., & Nolan, J. (2019). Behavioral and cognitive impacts of mindfulness-based interventions on adults with attention-deficit hyperactivity disorder: A systematic review. Behavioural Neurology, 2019 , Article ID 5682050. https://doi.org/10.1155/2019/5682050

Prigatano, G. P., Zigler, L. Y., & Rosenstein, L. D. (2003). The clinical neuropsychological examination: Scope, cost, and health-care value. In G. P. Prigatano & N. H. Pliskin (Eds.), Clinical neuropsychology and cost outcome research (pp. 15–36). Psychology Press.

Rabbitt, P., Lowe, C., & Shilling, V. (2001). Frontal tests and models for cognitive ageing. European Journal of Cognitive Psychology, 13 (1/2), 5–28. https://doi.org/10.1080/09541440125722

Rabin, L. A., Barr, W. B., & Burton, L. A. (2005). Assessment practices of clinical neuropsychologists in the United States and Canada: A survey of INS, NAN, and APA Division 40 members. Archives of Clinical Neuropsychology, 20 , 33–65. https://doi.org/10.1016/j.acn.2004.02.005

Rabin, L. A., Burton, L. A., & Barr, W. B. (2007). Utilization rates of ecologically oriented instruments among clinical neuropsychologists. The Clinical Neuropsychologist, 21 , 727–743. https://doi.org/10.1080/13854040600888776

Rabin, L. A., Paolillo, E. P., & Barr, W. B. (2016). Stability in test-usage practices of clinical neuropsychologists in the United States and Canada over a 10-year period: A follow-up survey of INS and NAN members. Archives of Clinical Neuropsychology, 31 , 206–230. https://doi.org/10.1093/arclin/acw007

Randolph, J. J., Arnett, P. A., & Higginson, C. I. (2001). Metamemory and tested cognitive functioning in multiple sclerosis. The Clinical Neuropsychologist, 15 (3), 357–368. https://doi.org/10.1076/clin.15.3.357.10278

Randolph, J. J., Arnett, P. A., & Freske, P. (2004a). Metamemory in multiple sclerosis: Exploring affective and executive contributors. Archives of Clinical Neuropsychology, 19 , 259–279. https://doi.org/10.1016/S0887-6177(03)00026-X

Randolph, J. J., Ferguson, R. J., & Wishart, H. A. (2004b). Cognitive-behavioral rehabilitation of cognitive dysfunction and depression in MS: A case study (presentation). Archives of Clinical Neuropsychology, 19 , 987.

Randolph, J. J., Randolph, J. S., & Wishart, H. A. (2012). Correlates of real world compensatory cognitive strategy use in MS (presentation). International Neuropsychological Society meeting, Montreal, Canada.

Randolph, J. J., Randolph, J. S., & Wishart, H. A. (2017). Association between cognitive complaints and vulnerability to environmental distraction in multiple sclerosis. Archives of Clinical Neuropsychology, 32 , 21–28. https://doi.org/10.1093/arclin/acw096

Rath, J. F., Simon, D., Langenbahn, D. M., Sherr, R. L., & Diller, L. (2003). Group treatment of problem-solving deficits in outpatients with traumatic brain injury: A randomized outcome study. Neuropsychological Rehabilitation, 13 , 461–488. https://doi.org/10.1080/09602010343000039

Razani, J., Casas, R., Wong, J. T., Lu, P., Alessi, C., & Josephson, K. (2007). Relationship between executive functioning and activities of daily living in patients with relatively mild dementia. Applied Neuropsychology, 14 (3), 208–214. https://doi.org/10.1080/09084280701509125

Reger, M. A., Welsh, R. K., Watson, G. S., Cholerton, B., Baker, L. D., & Craft, S. (2004). The relationship between neuropsychological functioning and driving ability in dementia: A meta-analysis. Neuropsychology, 18 (1), 85–93. https://doi.org/10.1037/0894-4105.18.1.85

Reid-Arndt, S. A., Nehl, C., & Hinkebein, J. (2007). The Frontal Systems Behaviour Scale (FrSBe) as a predictor of community integration following a traumatic brain injury. Brain Injury, 21 (13-14), 1361–1369. https://doi.org/10.1080/02699050701785062

Reitan, R. M. (1958). Validity of the Trail making Test as an indication of organic brain damage. Perceptual and Motor Skills, 8 , 271–276. https://doi.org/10.2466/pms.1958.8.3.271

Rey, A. (1941). Psychological examination of traumatic encephalopathy. Archives de Psychologie, 28 , 286–340.

Reynolds, G. O., Willment, K., & Gale, S. A. (2021). Mindfulness and cognitive training interventions in mild cognitive impairment: Impact on cognition and mood. American Journal of Medicine, 134 (4), 444–455. https://doi.org/10.1016/j.amjmed.2020.10.041

Rilo, O., Peña, J., Ojeda, N., Rodríguez-Antigüedad, A., Mendibe-Bilbao, M., Gómez-Gastiasoro, A., DeLuca, J., Chiaravalloti, N., & Ibarretxe-Bilbao, N. (2018). Integrative group-based cognitive rehabilitation efficacy in multiple sclerosis: A randomized clinical trial. Disability and Rehabilitation, 40 (2), 208–216. https://doi.org/10.1080/09638288.2016.1250168

Rodriguez-Aranda, C., & Sundet, K. (2006). The frontal hypothesis of cognitive aging: Factor structure and age effects on four frontal tests among healthy individuals. Journal of Genetic Psychology, 167 (3), 269–287. https://doi.org/10.3200/GNTP.167.3.269-287

Rohling, M. L., Faust, M. E., Beverly, B., & Demakis, G. (2009). Effectiveness of cognitive rehabilitation following acquired brain injury: A meta-analytic re-examination of Cicerone et al.’s (2000, 2005) systematic reviews. Neuropsychology, 23 (1), 20–39. https://doi.org/10.1037/a0013659

Rosado, D. L., Buehler, S., Botobol-Berman, E., Feigon, M., León, A., Luu, H., Carrión, C., Gonzalez, M., Rao, J., Greif, T., Seidenberg, M., & Pliskin, N. H. (2018). Neuropsychological feedback services improve quality of life and social adjustment. The Clinical Neuropsychologist, 32 (3), 422–435. https://doi.org/10.1080/13854046.2017.1400105

Rosenblum, S., Frisch, C., Deutsh-Castel, T., & Josman, N. (2015). Daily functioning profile of children with attention deficit hyperactive disorder: A pilot study using an ecological assessment. Neuropsychological Rehabilitation, 25 (3), 402–418. https://doi.org/10.1080/09602011.2014.940980

Roth, R. M., Isquith, P. K., & Gioia, G. A. (2005). Behavior Rating Inventory of Executive Function-Adult Version professional manual . Psychological Assessment Resources, Inc.

Roth, R. M., Lance, C. E., Isquith, P. K., Fischer, A. S., & Giancola, P. R. (2013). Confirmatory factor analysis of the Behavior Rating Inventory of Executive Function-Adult version in healthy adults and application to Attention-Deficit/Hyperactivity Disorder. Archives of Clinical Neuropsychology, 28 , 425–434. https://doi.org/10.1093/arclin/act031

Rubinstein, J. S., Meyer, D. E., & Evans, J. E. (2001). Executive control of cognitive processes in task switching. Journal of Experimental Psychology: Human Perception and Performance, 27 (4), 763–797. https://doi.org/10.1037//0096-1523.27.4.763

Safren, S. A., Otto, M. W., Sprich, S., Winett, C. L., Wilens, T. E., & Biederman, J. (2005). Cognitive-behavioral therapy for ADHD in medication-treated adults with continued symptoms. Behavior Research and Therapy, 43 (7), 831–842. https://doi.org/10.1016/j.brat.2004.07.001

Safren, S. A., Sprich, S., Mimiaga, M. J., Surman, C., Knouse, L., Groves, M., & Otto, M. W. (2010). Cognitive-behavioral therapy vs. relaxation with educational support for medication-treated adults with ADHD and persistent symptoms: A randomized and controlled trial. Journal of the American Medical Association, 304 (8), 875–880. https://doi.org/10.1001/jama.2010.1192

Salehinejad, M. A., Ghanavati, E., Rashid, M. H. A., & Nitsche, M. A. (2021). Hot and cold executive functions in the brain: A prefrontal-cingular network. Brain and Neuroscience Advances, 5 , 1–19. https://doi.org/10.1177/23982128211007769

Salva, G. N., Twamley, E. W., Delis, D. C., Roesch, S. C., Jeste, D. V., & Palmer, D. W. (2012). Dimensions of executive functioning in schizophrenia and their relationship with processing speed. Schizophrenia Bulletin, 38 (4), 760–768. https://doi.org/10.1093/schbul/sbq149

Sbordone, R. J. (1997). The ecological validity of neuropsychological testing. In A. M. Horton, D. Wedding, & J. Webster (Eds.), The neuropsychology handbook, volume 1: Foundations and assessment (2nd ed., pp. 365–392). Springer.

Sbordone, R. J., & Guilmette, T. J. (1999). Ecological validity: Prediction of everyday and vocational functioning from neuropsychological test data. In J. J. Sweet (Ed.), Forensic neuropsychology: Fundamentals and practice (pp. 227–254). Swets and Zeitlinger.

Schmitter-Edgecombe, M., Fahy, J. F., Whelan, J. P., & Long, C. J. (1995). Memory remediation after severe closed head injury: Notebook training versus supportive therapy. Journal of Consulting and Clinical Psychology, 63 (3), 484–489. https://doi.org/10.1037//0022-006x.63.3.484

Seeman, T. E., Miller-Martinez, D. M., Merkin, S. S., Lachman, M. E., Tun, P. A., & Karlamangla, A. S. (2011). Histories of social engagement and adult cognition: Midlife in the U.S. study. Journals of Gerontology: Series B, 66B , i141–i152. https://doi.org/10.1093/geronb/gbq091

Shandera-Ochsner, A. L., Chandler, M. J., Locke, D. E., Ball, C. T., Crook, J. E., Phatak, V. S., & Smith, G. E. (2021). Comparative effects of physical exercise and other behavioral interventions on functional status outcomes in mild cognitive impairment. Journal of the International Neuropsychological Society, 27 (8), 805–812. https://doi.org/10.1017/S1355617721000485

Shaw, S., Oei, T. P. S., & Sawang, S. (2015). Psychometric validation of the Dysexecutive Questionnaire (DEX). Psychological Assessment, 27 (1), 138–147. https://doi.org/10.1037/a0038195

Sherman, D. S., Mauser, J., Nuno, M., & Sherzai, D. (2017). The efficacy of cognitive intervention in mild cognitive impairment (MCI): A meta-analysis of outcomes on neuropsychological measures. Neuropsychology Review, 27 , 440–484. https://doi.org/10.1007/s11065-017-9363-3

Silva, M. T., Laks, J., & Engelhardt, E. (2009). Neuropsychological tests and driving in dementia: A review of the recent literature. Revisita da Associacao Medica Brasileira, 55 (4), 484–488. https://doi.org/10.1590/s0104-42302009000400027

Slick, D. J., Lautzenhiser, A., Sherman, E. M., & Eyrl, K. (2006). Frequency of scale elevations and factor structure of the Behavior Rating Inventory of Executive Function (BRIEF) in children and adolescents with intractable epilepsy. Child Neuropsychology, 12 (3), 181–189. https://doi.org/10.1080/09297040600611320

Smith, M. M., & Arnett, P. A. (2010). Awareness of executive functioning deficits in multiple sclerosis: Self versus informant ratings of impairment. Journal of Clinical and Experimental Neuropsychology, 32 (7), 780–787. https://doi.org/10.1080/13803390903540307

Smith, S., Wiggins, C., & Gorske, T. (2007). A survey of psychological assessment feedback practices. Assessment, 14 (3), 310–319. https://doi.org/10.1177/1073191107302842

Sohlberg, M. M., & Mateer, C. A. (1989). Training use of compensatory memory books: A three stage behavioral approach. Journal of Clinical and Experimental Neuropsychology, 11 , 871–891. https://doi.org/10.1080/01688638908400941

Sohlberg, M. M., & Mateer, C. A. (2001). Cognitive rehabilitation: An integrative neuropsychological approach . Guilford Press.

Stamenova, V., & Levine, B. (2019). Effectiveness of goal management training in improving executive functions: A meta-analysis. Neuropsychological Rehabilitation, 29 (10), 1569–1599. https://doi.org/10.1080/09602011.2018.1438294

Stout, J. C., Ready, R. E., Grace, J., Malloy, P. F., & Paulsen, J. S. (2003). Factor analysis of the frontal systems behavior scale (FrSBe). Assessment, 10 (1), 79–85. https://doi.org/10.1177/1073191102250339

Strauss, E., Sherman, E. M. S., & Spreen, O. (2006). A compendium of neuropsychological tests . Oxford University Press.

Stuss, D. T. (2011). Functions of the frontal lobes: Relation to executive functions. Journal of the International Neuropsychological Society, 17 , 759–765. https://doi.org/10.1017/S1355617711000695

Stuss, D. T., & Alexander, M. P. (2007). Is there a dysexecutive syndrome? Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences, 362 , 901–915. https://doi.org/10.1098/rstb.2007.2096

Stuss, D. T., & Benson, F. B. (1986). The frontal lobes . Raven Press.

Suchy, Y., Ziemnik, R. E., Niermeyer, M. A., & Brothers, S. L. (2020). Executive functioning interacts with complexity of daily life in predicting daily medication management among older adults. The Clinical Neuropsychologist, 34 (4), 797–825. https://doi.org/10.1080/13854046.2019.1694702

Sweet, J. J., Klipfel, K. M., Nelson, N. W., & Moberg, P. J. (2021). Professional practices, beliefs, and incomes of U.S. neuropsychologists: The AACN, NAN, SCN 2020 practice and “salary survey”. The Clinical Neuropsychologist, 35 (1), 7–80. https://doi.org/10.1080/13854046.2020.1849803

Tan, C. C., & Lumeng, J. C. (2018). Associations between cool and hot executive functions and children’s eating behavior. Current Nutrition Reports, 7 (2), 21–28. https://doi.org/10.1007/s13668-018-0224-3

Testa, R., Bennett, P., & Ponsford, J. (2012). Factor analysis of nineteen executive function tests in a healthy adult population. Archives of Clinical Neuropsychology, 27 , 213–224. https://doi.org/10.1093/arclin/acr112

Tomaszewski, B., Fidler, D., Talapatra, D., & Riley, K. (2018). Adaptive behaviour, executive function and employment in adults with Down syndrome. Journal of Intellectual Disability Research, 62 (1), 41–52. https://doi.org/10.1111/jir.12450

Tyburski, E., Mak, M., Sokolowski, A., Starkowska, A., Karabanowicz, E., Kerestey, M., Lebiecka, Z., Preś, J., Sagan, L., Samochowiec, J., & Jansari, A. S. (2021). Executive dysfunctions in schizophrenia: A critical review of traditional, ecological, and virtual reality assessments. Journal of Clinical Medicine, 10 , 2782. https://doi.org/10.3390/jcm10132782

Velligan, D. I., Ritch, J. L., Sui, D., DiCocco, M., & Huntzinger, C. D. (2002). Frontal Systems Behavior Scale in schizophrenia: Relationships with psychiatric symptomatology, cognition and adaptive function. Psychiatry Research, 113 , 227–236. https://doi.org/10.1016/s0165-1781(02)00264-0

Verdejo-García, A., & Pérez-García, M. (2007). Ecological assessment of executive functions in substance dependent individuals. Drug and Alcohol Dependence, 90 (1), 48–55. https://doi.org/10.1016/j.drugalcdep.2007.02.010

Vriezen, E. R., & Pigott, S. E. (2002). The relationship between parental report on the BRIEF and performance-based measures of executive functioning in children with moderate to severe traumatic brain injury. Child Neuropsychology, 8 (4), 296–303. https://doi.org/10.1076/chin.8.4.296.13505

Wakely, H., Radakovic, R., Bateman, A., Simblett, S., Fish, J., & Gracey, F. (2022). Psychometric properties of the revised Dysexecutive Questionnaire in a non-clinical population. Frontiers in Human Neuroscience, 16 , Article 767367. https://doi.org/10.3389/fnhum.2022.767367

Wang, X. T., Wang, Z. T., Hu, H. Y., Qu, Y., Wang, M., Shen, X. N., Xu, W., Dong, Q., Tan, L., & Yu, J. T. (2021). Association of subjective cognitive decline with risk of cognitive impairment and dementia: A systematic review and meta-analysis of prospective longitudinal studies. The Journal of Prevention of Alzheimer’s Disease, 8 (3), 277–285. https://doi.org/10.14283/jpad.2021.27

Weber, E., Chiaravalloti, N. D., DeLuca, J., & Goverover, Y. (2019). Time-based prospective memory is associated with functional performance in persons with MS. Journal of the International Neuropsychological Society, 25 (10), 1035–1043. https://doi.org/10.1017/S135561771900095X

Webster, J. S., & Scott, R. R. (1983). The effects of self-instructional training on attentional deficits following head injury. Clinical Neuropsychology, 5 , 69–74.

Wilson, B. A., Alderman, N., Burgess, P., Emslie, H., & Evans, J. J. (1996). Behavioural assessment of the dysexecutive syndrome (BADS) . Thames Valley Test Company.

Yates, L. A., Ziser, S., Spector, A., & Orrell, M. (2016). Cognitive leisure activities and cognitive impairment and dementia: Systematic review and meta-analysis. International Psychogeriatrics, 28 (11), 1791–1806. https://doi.org/10.1017/S1041610216001137

Young, J., Angevaren, M., Rusted, J., & Tabet, N. (2015). Aerobic exercise to improve cognitive function in older people without known cognitive impairment. Cochrane Database of Systematic Reviews, 2015 (4), Article CD005381. https://doi.org/10.1002/14651858.CD005381.pub4

Zahodne, L. B., Nowinski, C. J., Gershon, R. C., & Manly, J. J. (2014). Which psychosocial factors best predict cognitive performance in older adults? Journal of the International Neuropsychological Society, 20 , 487–495. https://doi.org/10.1017/S1355617714000186

Zelazo, P. D., & Müller, U. (2002). Executive function in typical and atypical development. In U. Goswami (Ed.), Handbook of childhood cognitive development (pp. 445–469). Blackwell.

Zelzao, P. D., & Carlson, S. M. (2012). Hot and cool executive function in childhood and adolescence: Development and plasticity. Child Development Perspectives, 6 (4), 354–360. https://doi.org/10.1111/j.1750-8606.2012.00246.x

Zencius, A., Wesolowski, M. D., & Burke, W. H. (1990). A comparison of four memory strategies with traumatically brain-injured clients. Brain Injury, 4 , 33–38. https://doi.org/10.3109/02699059009026146

Ziemnik, R. E., & Suchy, Y. (2019). Ecological validity of performance-based measures of executive functions: Is face validity necessary for prediction of daily functioning? Psychological Assessment, 31 (11), 1307–1318. https://doi.org/10.1037/pas0000751

Download references

Author information

Authors and affiliations.

Department of Psychiatry, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA

John J. Randolph

Randolph Neuropsychology Associates, PLLC, Lebanon, NH, USA

Department of Community and Behavioral Health, Elson S. Floyd College of Medicine, Washington State University, Spokane, WA, USA

Naomi S. Chaytor

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to John J. Randolph .

Editor information

Editors and affiliations, rights and permissions.

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Cite this chapter.

Randolph, J.J., Chaytor, N.S. (2022). Promoting the Executive Functions: Core Foundations, Assessment Considerations, and Practical Applications. In: Randolph, J.J. (eds) Positive Neuropsychology. Springer, Cham. https://doi.org/10.1007/978-3-031-11389-5_7

Download citation

DOI : https://doi.org/10.1007/978-3-031-11389-5_7

Published : 05 November 2022

Publisher Name : Springer, Cham

Print ISBN : 978-3-031-11388-8

Online ISBN : 978-3-031-11389-5

eBook Packages : Behavioral Science and Psychology Behavioral Science and Psychology (R0)

Share this chapter

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

  • Publish with us

Policies and ethics

  • Find a journal
  • Track your research

U.S. flag

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

  • Publications
  • Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

  • Advanced Search
  • Journal List
  • HHS Author Manuscripts

Logo of nihpa

Educating Executive Function

Executive functions are thinking skills that assist with reasoning, planning, problem solving, and managing one’s life. The brain areas that underlie these skills are interconnected with and influenced by activity in many different brain areas, some of which are associated with emotion and stress. One consequence of the stress-specific connections is that executive functions, which help us to organize our thinking, tend to be disrupted when stimulation is too high and we are stressed out, or too low when we are bored and lethargic. Given their central role in reasoning and also in managing stress and emotion, scientists have conducted studies, primarily with adults, to determine whether executive functions can be improved by training. By and large, results have shown that they can be, in part through computer-based videogame-like activities. Evidence of wider, more general benefits from such computer-based training, however, is mixed. Accordingly, scientists have reasoned that training will have wider benefits if it is implemented early, with very young children as the neural circuitry of executive functions is developing, and that it will be most effective if embedded in children’s everyday activities. Evidence produced by this research, however, is also mixed. In sum, much remains to be learned about executive function training. Without question, however, continued research on this important topic will yield valuable information about cognitive development.

INTRODUCTION

The term executive function encompasses cognitive abilities that enable us to hold information in mind in working memory, to inhibit highly automatic responses to stimulation, and to shift the focus of attention between related but distinct aspects of a given task or problem. Executive, or cognitive control abilities, allow us to inhibit ingrained behaviors, to focus attention strategically, and to organize our thoughts in the face of distraction, complexity, and stress. Like many aspects of cognitive ability, executive functions are useful in many different situations. Unfortunately, however, they also degrade rapidly under stress and pressure. If only we should all be so lucky to be as cool, calm, and collected as James Bond as he decides which wires to clip to defuse a nuclear bomb threatening to destroy civilization as its timer rapidly counts down to zero. If we could hang on to our thinking skills in the face of complexity and stress, in the face of pressure at work and in relationships, seemingly the world would be a better place and our lives would be more fulfilling (or at least we wouldn’t get blown to bits). More realistically, we might inhibit impulses that get us into trouble, that lead us down rocky paths and into bad decisions that bring problems, sickness, and even death. On the more positive side, we might do better in school and in work, routinely avoid bad situations and encounters, and feel generally good about the choices we make. Of course, there are situations in life in which it is far better to respond quickly without thinking about things, such as when hitting the brakes when the car in front of us stops suddenly in traffic. It is also possible to ‘over think’ or ruminate on a given situation or topic in ways that can impair our wellbeing and our ability to take action. For most aspects of daily life, however, executive functions are more help than hindrance.

Fortunately, like many aspects of cognitive ability, executive functions tend to get better the more we practice them. The big questions are how much better and what kind of practice? Although seemingly the stuff of fiction, the possibility that we can develop executive function skills through training is a tantalizing and scientifically realistic possibility; but how can we do this in a way that will lead to some lasting and broadly applicable benefits? The answer is complex and, for the most part, currently unknown. There are, however, some good indicators of what an answer might look like and the types of benefits this might bring to ourselves and our society.

NEUROSCIENCE

To begin, it is useful to consider some basic neurobiology. The brain areas associated with the thinking skills that make up executive functions – working memory, inhibitory control, flexible shifting of attention – are primarily ‘located’ in what is known as prefrontal cortex (PFC). The neural circuits that support these skills, however, involve numerous brain regions, including areas of cingulate cortex and parietal cortex as well as subcortical structures, primarily the basal ganglia, amygdala, and hippocampus. In brief, specific networks of brain areas are active when we engage executive functions. These ares work together in an interconnected network to solve complex problems and help us reason about things. Most importantly for present purposes, some of the brain areas that are interconnected with PFC and make up the neural networks that support executive functions are part of the brain’s limbic system; particularly the amygdala. The limbic brain registers the emotional and motivational significance of things—events, persons, places, tasks, etc. Relatively rapid and automatic signaling from the limbic brain to the PFC helps to direct our attention and thinking skills to things that are meaningful to us and away from things that have less meaning. In a word, the interconnected limbic-to-PFC circuitry (or corticolimbic connectivity if you want to impress your friends) underlies goal-directed purposeful actions, from rare actions (as in, defuse the bomb before it blows up) to daily activities (plan your day, solve a crossword puzzle, stop yourself from absent mindedly crossing the street against the light).

The interconnected limbic-to-PFC circuitry functions as a feedback loop. As the PFC receives input from the limbic brain, it feeds back on the limbic brain to modulate its input. In fact, the system is thought to work over short time periods to maintain a moderate level of input. That is, signaling from the limbic brain to the higher brain is in the form of neurotransmitters—dopamine, norepinephrine, the glucocorticoid hormone cortisol— that stimulate or more precisely modulate neural activity in the PFC brain areas that underlie executive functions 1 . When the increase in neurotransmitters/modulators is in a moderate range, PFC neural activity is strong and executive functions are working well. When the increase is too great, indicating that the person is under stress or over stimulated, or too low, indicating boredom and lethargy, then activity in PFC, and consequently the valuable thinking skills that this activity supports, is reduced.

BRAIN DEVELOPMENT AND EXECUTIVE FUNCTION SKILLS TRAINING

Given the general principle of brain function and development, namely that “cells that fire together, wire together” (the idea that much of the brain’s circuitry and strength of connectivity is shaped over time by experience; what is referred to as use-dependent activity), it is reasonable to expect that an individual might strengthen his or her executive function skills by strengthening the neural circuitry that underlies these skills. By repeatedly practicing executive function types of tasks, one would repeatedly activate the PFC-to-limbic brain neural circuitry (as well as PFC connectivity throughout the brain related to the task at hand). Presumably such repeated practice, as long as it did not become too boring and repetitive and remained moderately challenging, would strengthen the top down control from PFC to limbic brain, as well as other brain areas, leading to better control of emotional responses to situations and to a cooler, calmer, more collected self. Such a scenario is plausible and some evidence suggests that this might be the case. Individuals practicing a specific type of executive function skill, namely, working memory, over 6-8 weeks about 45mins per day get better at holding information in working memory and demonstrate changes in brain activity and even brain structure in relevant brain areas consistent with the increase in performance 2 . Primarily these activities are videogame-like activities that include things like remembering a series of locations, altering one’s actions in response to specific contextual cues, and overriding automatic responses. There is now a good body of evidence that repeated practice of executive skills (attention skills also) can be enhanced by practice in specific types of videogames and related types of activities 3 - 5 .

An important question, however, is whether the improvements in these skills will translate into gains that generalize across a variety of situations and behaviors. This generalizability question is a complicated one and looms large in this area of research. The question is whether training merely leads the individual to become better on the trained task and on similar types of tasks, something known as near transfer. The alternative is far transfer, where training leads to more widespread benefits, that is, gains that matter in the real world like becoming better at controlling one’s emotions or getting better at some higher-level skill such as math. Evidence pertaining to this key question is mixed. Some studies indicate that training improves the control of behavior and attention for children with ADHD 6 . Some evidence also exists for other disorders (see Morrison & Chein 5 ). Other studies, however, suggest that at best, near transfer is most frequently seen. One particularly interesting study with young adults demonstrated that working memory training led to improvements on a specific type of intelligence test that requires reasoning about matrices 7 . The idea here was that if intelligence and reasoning improved with training then perhaps many other things with which intelligence and reasoning are associated would also improve. Although the authors of that study did not look at broader underlying mental abilities beyond matrix reasoning, other similar studies with younger children have and demonstrate some benefits to broader abilities such as reading comprehension and mathematics that would be associated with general reasoning ability 8 , 9 . Findings are far from definitive, however, and much remains to be done in this important area of research 10 . Methodologically speaking, there are many ways in which current studies could be improved. Some of these improvements are relatively straightforward, such as increasing sample size, while others are more complex, such as developing experimental manipulations to control for alternative, competing explanations for effects, such as social interaction 11 , 12 .

EXPANDING THE PLAYING FIELD

Given pressing issues of generalizability, two overarching principles of psychological development would seem to readily come into play. One is the “earlier is better” principle and the other is the ecological validity principle. The earlier is better principle suggests that efforts at training and building executive function skills will be most effective when PFC and other brain areas, including limbic brain areas, are developing rapidly in childhood as executive functions are first emerging. Given that neural networks are less specialized and differentiated in infancy and early childhood, the idea is that they will be more amenable to the effects of training. The second, and related, principle, ecological validity, suggests that in addition to starting early, incorporating the training exercises into things that we want kids to become better at, such as learning things in school, should be most effective. This principle reflects the common sense idea that if we want training to help people in their everyday lives, then training should take place in the contexts in which individuals are typically situated, such as school and work. Realistically, what good is such training if it only improves performance on specific executive function assessments?

With respect to the earlier is better principle, there is some evidence to suggest that earlier actually is better. Starting as young as infancy, some enterprising researchers have shown that training attention skills through structured computerized presentations of information can support the development of attention and provide a foundation for later executive functions 13 , 14 . There are longitudinal data indicating that attention in infancy is a great predictor of later mental ability 15 , 16 , so training attention early to improve executive function makes good sense. As with the findings of training effects on the measure of intelligence in young adults, however, it may be that any observed positive effects would only be on superficial aspects of a given executive function task rather than broader underlying mental abilities. Longitudinal data are needed to determine whether starting early in life to train executive function skills through computer-based training of attention might lead to meaningful longer-term gains.

Related to the second principle, the ecological validity principle, why not start training early but do it in the context of preschool and kindergarten? Given that executive function skills support reasoning and problem solving, they are very useful in the classroom. Executive function training would assist students in learning new information in a context that would likely lead to high generalizability (or at least be very useful). But how to do this? Despite its appeal, playing videogames for part of the day in the classroom, on its own disconnected from other activities, would not seem to be a good idea (sorry kids.) Some insightful curriculum specialists, however, have been way ahead of the curve in this regard. Several years ago, these specialists recognized the power of structured play to encourage the development of complex thinking skills in young children. This is thought to occur primarily through the ability of structured play to encourage children to plan ahead and to reflect on their behavior, in a word, to be self-directed and, hence, self-regulated. Many experts consider the development of self-regulation skills, of which executive functions are the crown jewel, to be the most important objective of high quality preschool — to help children focus attention, be emotionally expressive, not be impulsive, and to engage in purposeful and meaningful interactions with caregivers and other children.

The use of child-directed, structured play and classroom activities are characteristic of Montessori education and of a program known as Tools of the Mind. Tools of the Mind and Montessori are thought to promote executive functions by providing children with opportunities for self-direction in choosing and completing learning activities. The programs are also thought to promote executive function skills by having children work collaboratively in ways that require each child to take the perspective of his or her partner. In addition, Tools of the Mind uses structured play and most importantly the planning of play in advance to encourage reflection and thereby the development of executive function skills.

Evaluations of Tools of the Mind and Montessori and similar educational approaches have been generally favorable, suggesting that they do indeed help young children build their executive function skills and to learn more quickly. For example, a ‘randomized’ (lottery system) evaluation of Montessori education indicated benefits to children’s executive functions as well as to academic achievement and play behavior 17 . Similarly, a recent randomized controlled trial evaluation of the Tools of the Mind program with children in kindergarten demonstrated benefits to executive functions and academic abilities as well as the ability to control attention in the face of emotionally arousing images 18 . These results followed RCT evaluations of the program with preschoolers, one of which found benefits to executive function skills 19 with some evidence of academic benefits (Barnett et al., 2008) while another found no effects of Tools of the Mind on any aspect of child ability 20 . A third evaluation of the preschool program is now underway and is showing effects of the program on executive functions and academic abilities. In theory, by fostering the development of self-regulation, high quality preschool and early elementary education is understood to assist children in making sense of and building on the academic types of information that they will increasingly be exposed to throughout their school careers. Too much of an academic focus in early education without sufficient support for a strong foundation in self-regulation is ultimately self-defeating and likely to lead to worse, not better, school outcomes for children.

In addition to structured play, another approach to fostering executive functions is mindfulness meditation. Although not specific to school, this approach can and often is practiced by school children. Research on mindfulness meditation in adults indicates that mediation for even a short time improves attention and executive functions 21 . Educators and researchers are developing interesting techniques for assisting young children in meditating with some promising findings suggesting that the techniques are working 22 . Some of the best evidence for the effects of mindfulness meditation on executive functions comes from research with young adults. That research has shown not only effects on behavioral measures of executive functions but also on brain activity and connectivity in the limbic brain to higher brain PFC circuitry 23 . These effects suggest that although earlier may be better, it is perhaps never too late (or at least better late than never.) Research findings suggesting effects of training on executive functions in adults hold the very interesting possibility that we all might improve our well being and our job performance, interpersonal relationships, and so on through executive function training of one type or another, be it mindfulness meditation or videogame playing, or something else entirely. Perhaps recognition of the potential for change and the power to control one’s thinking skills – that effort matters more than ability – is enough to lead people to give it a try. It’s certainly something worth thinking about.

A number of studies with children and adults have shown that executive function skills -- particularly working memory — can be improved. The idea that we can increase our executive function abilities in ways that matter in our daily lives is less well established but would seem to be a realistic possibility. There have been very rapid advances in a relatively short period of time in the scientific understanding of executive functions and of how the brain works in general. It would seem likely that issues related to near and far transfer of executive function training will be resolved in the not too distant future and that executive function training will begin to make its way into a variety of activities, including educational and vocational training. An important aspect of progress in this research and its application, however, will be to pay close attention to how and under what circumstances training is actually effective and resulting in lasting gains. It is perhaps very easy to sell the idea that playing some sort of videogame, just like taking some sort of pill, might actually make one smarter and improve one’s life. If history and prior scientific evidence are any guide, however, genuine increases in ability are only likely to come through sustained and ongoing practice and persistence in things that matter to us most.

ACKNOWLEDGMENTS

Preparation of this manuscript was supported by National Institute of Child Health and Human Development grants R01HD51502 and P01HD39667 and Institute of Education Sciences grants R305A100058 and R324A120033.

FURTHER READING

Bodrova, E. & Leong, D.J. (2007). Tools of the Mind: The Vygotskian Approach to Early Childhood Education. New York: Merrill/Prentice Hall.

Fuster, J. M. (2008). The prefrontal cortex. 4th Edition. Burlington MA: Academic Press.

Shipstead, Z., Redick, T. S., & Engle, R. W. (2012). Is working memory training effective? Psychological Bulletin, 138, 628-654.

Key Concepts

Executive Function & Self-Regulation

Content in this guide, step 1: executive function 101.

  • You Are Here: Executive Function & Self-Regulation
  • : Executive Function: Skills for Life and Learning

Step 2: The Science of Executive Function

  • : Building the Brain’s “Air Traffic Control” System
  • : Video: How to Build Core Capabilities for Life

Step 3: Building Executive Function Skills

  • : Activities Guides: Practicing Executive Function Skills
  • : Building the Core Skills Youth Need for Life
  • : Building the Skills Adults Need for Life

Executive function and self-regulation skills are the mental processes that enable us to plan, focus attention, remember instructions, and juggle multiple tasks successfully. Just as an air traffic control system at a busy airport safely manages the arrivals and departures of many aircraft on multiple runways, the brain needs this skill set to filter distractions, prioritize tasks, set and achieve goals, and control impulses.

When children have opportunities to develop executive function and self-regulation skills, individuals and society experience lifelong benefits. These skills are crucial for learning and development. They also enable positive behavior and allow us to make healthy choices for ourselves and our families.

Executive function and self-regulation skills depend on three types of brain function: working memory, mental flexibility, and self-control. These functions are highly interrelated, and the successful application of executive function skills requires them to operate in coordination with each other.

The successful application of executive function skills requires them to operate in coordination with each other.

  • Working memory governs our ability to retain and manipulate distinct pieces of information over short periods of time.
  • Mental flexibility helps us to sustain or shift attention in response to different demands or to apply different rules in different settings.
  • Self-control enables us to set priorities and resist impulsive actions or responses.

Children aren’t born with these skills—they are born with the potential to develop them. Some children may need more support than others to develop these skills. In other situations, if children do not get what they need from their relationships with adults and the conditions in their environments—or (worse) if those influences are sources of toxic stress —their skill development can be seriously delayed or impaired. Adverse environments resulting from neglect , abuse, and/or violence may expose children to toxic stress, which can disrupt brain architecture and impair the development of executive function.

Ready4Routines

Providing the support that children need to build these skills at home, in early care and education programs, and in other settings they experience regularly is one of society’s most important responsibilities. Growth-promoting environments provide children with “scaffolding” that helps them practice necessary skills before they must perform them alone. Adults can facilitate the development of a child’s executive function skills by establishing routines, modeling social behavior, and creating and maintaining supportive, reliable relationships. It is also important for children to exercise their developing skills through activities that foster creative play and social connection, teach them how to cope with stress, involve vigorous exercise, and over time, provide opportunities for directing their own actions with decreasing adult supervision.

Explore Related Resources

  • Reports & Working Papers
  • Tools & Guides
  • Presentations
  • Infographics

critical thinking and executive function

Tools & Guides : Activities Guide: Enhancing and Practicing Executive Function Skills with Children from Infancy to Adolescence

Working Paper 11 cover

Reports & Working Papers : Building the Brain’s “Air Traffic Control” System: How Early Experiences Shape the Development of Executive Function

A cover image from the Best Practices to Breakthrough Impacts paper, showing the title and an image of two parents kissing their baby

Reports & Working Papers : From Best Practices to Breakthrough Impacts

Executive Function InBrief

Briefs : InBrief: Executive Function

Executive Function video still

Videos : InBrief: Executive Function: Skills for Life and Learning

children with caregiver/teacher

Briefs : 8 Things to Remember about Child Development

Resource

Tools & Guides : Brain-Building Through Play: Activities for Infants, Toddlers, and Children

cover of guide

Tools & Guides : Building the Core Skills Youth Need for Life: A Guide for Education and Social Service Practitioners

Adult capabilities handout thumbnail

Tools & Guides : Building the Skills Adults Need for Life: A Guide for Practitioners

critical thinking and executive function

Videos : Intergenerational Mobility Project: Building Adult Capabilities for Family Success

Tools & guides : mejora y práctica de las habilidades de función ejecutiva con niños desde la infancia hasta la adolescencia.

critical thinking and executive function

Videos : Ready4Routines: Building the Skills for Mindful Parenting

Motivation Brain

Infographics : The Brain Circuits Underlying Motivation: An Interactive Graphic

Working Paper 14 cover

Reports & Working Papers : Understanding Motivation: Building the Brain Architecture That Supports Learning, Health, and Community Participation

critical thinking and executive function

Presentations : Using Brain Science to Build a New 2Gen Intervention

Building Core Capabilities for Life brain graphic with airplanes

Videos : How Children and Adults Can Build Core Capabilities for Life

Executive Function infographic thumbnail

Infographics : What Is Executive Function? And How Does It Relate to Child Development?

critical thinking and executive function

Presentations : Why Do Some Children Respond to an Intervention and Others Don’t?

A screenshot of the training module showing the various parts of the course you can take

Partner Resources , Tools & Guides : Training Module: Health Care Practitioner Module and Resources

Beth Babcock

Presentations , Partner Resources : Using Brain Science to Create New Pathways out of Poverty

  • Our Mission

Improving Executive Function: Teaching Challenges and Opportunities

The high cost of over-packed curriculum standards.

For 21st century success, students will need skill sets far beyond those that are mandated in the densely packed standards -- and that's evaluated on bubble tests. In the near future, success will depend on accelerated rates of information acquisition. And we need to help students develop the skill sets to analyze new information as it becomes available, to flexibly adapt when facts are revised, and to be technologically fluent (as new technology becomes available). Success will also depend upon one's ability to collaborate and communicate with others on a global playing field -- with a balance of open-mindedness, foundational knowledge, and critical analysis skills so they can make complex decisions using new and changing information.

We are painfully aware that the educational model has not changed to accommodate the exponentially increasing amount of information pertinent to students. In every country I've given presentations and workshops, the problem is the same: overstuffed curriculum. In response to more information, educators are mandated to teach more rote facts and procedures and students are given bigger books and more to memorize.

The factory model of education still in place was designed for producing assembly line workers to do assigned, repetitive tasks correctly. These workers did not need to analyze, create, or question. Packing students' brains with unreasonable quantities of facts fails to prepare them for much beyond assembly line work -- and that line of thinking is outdated. Automation and computerization have surpassed the human ability for doing most repetitive tasks and information collection.

The human brain does have the equipment for the new critical skill sets needed in the future, but it cannot activate these tools without guided experiences. These tools, the neural networks that control executive functions, develop in the prefrontal cortex and do so most profoundly during the school years. Unlike other parts of the brain and body that develop automatically over time, the circuits that direct executive function require appropriate stimuli to develop appropriate response capabilities. As educators, it is our challenge to provide the stimuli that ignites and develops these dormant brain circuits so students can best select and succeed in the career and life paths they choose.

Executive Functions for Current and Future Opportunities

What my field of neurology has called "executive functions" for over 100 years, are the highest cognitive processes -- they are sometimes called higher order thinking or critical thinking. Executive functions can be thought of as the skills that would make a corporate executive successful -- the ability to be flexible, interpretive, creative, and have multidimensional thinking. Examples include planning, risk assessment, informed decision-making, deductive and inductive reasoning, critical analysis, and delay of immediate gratification to achieve long-term goals. These executive functions provide the tools the brain then uses for organizing, connecting, and prioritizing of information and tasks, attention focusing, self-monitoring, self-correcting, accurate prediction, abstraction, and creative problem-solving.

The Prefrontal Cortex: The Most Valuable Brain Real Estate

As the executive function control centers are activated in the prefrontal cortex (PFC) children are more able to consider and voluntarily control their thinking, emotional responses, and behavior. The PFC can be thought of as the "reflective" higher brain compared to the "reactive" lower brain. The prime real estate of the PFC comprises roughly 20-percent of our brains -- the highest percentage of brain volume of all animals.

Other animals are more dependent on their reactive lower brains to survive in their unpredictable environments. As man developed more control of his environment, the luxury of a larger reflective brain correlated with the evolution of the PFC to its current proportions.

The prefrontal cortex is the last part of the brain to mature. This maturation is a process of neuroplasticity that prunes away unused cells to better provide for the metabolic needs of the most frequently activated neurons. The other aspect of neuroplasticity is the growth of stronger and increased numbers of connections among frequently activated neurons. Single neurons hold only a tiny bit of any memory. It is only when multiple neurons connect, through the branches and synapses they grow, that a memory is stored for retrieval or a voluntary action can be carried out.

This prefrontal cortex maturation continues well into the twenties, with the most rapid changes happening from ages 8 through 16. It is the flow of electricity from neuron to neuron, through connections called axons and dendrites, that activates the neuroplastic growth of more efficient connections. Each time a network is stimulated -- its information recalled or used -- the connections become stronger and faster. The stimulation during the ages of their rapid development strongly influences social-emotional control and the highest thinking skill sets that today's students will carry with them as they leave school and become adults.

Preparing Students for the Challenges and Opportunities of the 21st Century

We have the obligation to provide our students with "activating" experiences that stimulate judgment, pattern recognition, induction, deduction; and activate prior knowledge, analysis, and prediction. Experiences that promote executive function activation include evaluating and doing something with information while they learn, such as discovering relationships between what they learn and what they already know, or transforming new learning into another form, such as writing about math or symbolically transforming a story into a drawing.

Unless new rote memories are incorporated into larger, relational networks, they remain isolated bits of data in small, unconnected circuits likely to be pruned away. It is through "doing something" -- active mental manipulation -- that new information becomes available for usable retrieval. If there is memorization without mental manipulation the isolated rote fact memories can only be retrieved by the specific cues through which they are practiced in repetitive drills. Mental manipulation promotes the neuroplasticity that constructs new connections (dendrites, synapses, myelinated axons) between formerly separate memory circuits each time they are activated together. This is the physical manifestation of the idea that "neurons that fire together, wire together."

Learning For A Purpose

25+ Executive Function Games to Boost Brain Skills

Executive function skills are vital cognitive abilities that allow both children and adults to plan, focus attention, remember instructions, and juggle multiple tasks successfully. These functions are fundamental for success in school, work, and day-to-day life. Training these skills can be a rich and rewarding experience, and one effective method of doing so is through the use of specially designed games. These games are not only enjoyable but also serve a dual purpose: they engage players in fun activities while concurrently strengthening various aspects of executive function.

Introducing games into the development process for executive function can be especially beneficial for young learners. Games that aim to improve skills like memory, flexible thinking, and self-control can provide a solid foundation for young adults and teens as they navigate complex educational and social landscapes. For adults, games designed to bolster executive function can also provide an edge in professional environments or assist in maintaining cognitive flexibility.

Key Takeaways

  • Games offer an enjoyable approach to enhance executive function skills for all ages.
  • Consistent play can improve cognitive abilities integral to academic and personal success.
  • Selection and integration of appropriate games into everyday routine optimize executive function training.

** This post contains affiliate links. This means if you click a link and make a purchase we will earn a comission at no extra cost to you.

Understanding Executive Functions

Executive functions (EF) are like the CEO of your brain, directing your thoughts, actions, and emotions to help you succeed in everyday tasks and challenges. Below, you'll find a more detailed look at what these functions entail and how particular types of games can foster their development.

What Are Executive Functions?

Executive functions are a set of cognitive skills essential for controlling and managing your thoughts, actions, and emotions. They enable you to:

  • Plan and organize your day-to-day tasks
  • Prioritize what's most important
  • Focus your attention and avoid distractions
  • Remember instructions and information ( working memory )
  • Regulate your emotions and control impulses ( impulse control and self-control )
  • Adapt to new situations ( flexibility )

These skills are fundamental for academic success, social interaction, and overall independent living .

Get the FREE Executive Functions Skills List

Get the free executive function skills list to help you better identify and understand different executive function skills. You can use this list to help you identify different skill areas to work on or ones that are a strength for you or the person you are working with.

In the form below just enter your best email address so I can send it to you.

The Role of Games in EF Development

Games, especially ones designed with your EF skills in mind, can be powerful tools for strengthening these capabilities. Through gameplay, you can practice and enhance various components of executive functions, such as:

  • Attention: Games often require concentration and the ability to filter out distractions.
  • Memory: Remembering rules, strategies, and previous turns exercises your working memory.
  • Flexibility: Adapting to new rules or unexpected game developments helps you practice flexibility.
  • Impulse Control: Waiting for your turn and thinking before acting cultivates self-control and impulse regulation.

Incorporating game-based learning into your routine can support EF development in an engaging and enjoyable way. Whether you're a child or an adult, games can offer a structured environment to practice and improve these vital skills.

Types of Executive Function Games

Discovering a variety of games that challenge and enhance your executive function skills can be both fun and beneficial. Whether you prefer a tactile experience with physical pieces, the convenience of digital platforms, or the classic feel of cards and boards, there's something tailored for everyone's taste and developmental goals.

Board Games

  • Strategy Games: Engage in games like chess which require you to think ahead and plan your moves, effectively bolstering your problem-solving and planning abilities.
  • Memory Chess Game: Enhance working memory and attention to detail by recalling moves and positions in Memory Chess.
  • No Stress Chess: Learn chess without the pressure, which supports task initiation and flexibility in thinking as you become familiar with different pieces and their moves.
  • Sudoku and Pictionary: Sharpen your working memory and improve impulse control as you match numbers in Sudoku or sketch rapidly in a pressure-driven round of Pictionary.
  • Quiddler: This card game challenges you to create words from the letters you're dealt with, boosting your linguistic processing and strategic planning skills.

Physical Games

  • Jenga: Carefully removing blocks to maintain the tower's integrity in Jenga can significantly enhance your hand-eye coordination and impulse control.
  • Sports: Participating in team sports or activities requires the rapid execution of complex physical and mental tasks, improving your overall executive function.

Digital Games

  • Brain Training Apps: Utilize the convenience of your smartphone or tablet with apps designed to train different aspects of executive functioning, like inhibitory control and cognitive flexibility.
  • Online Puzzles: Completing digital puzzles, solving online riddles, or playing interactive strategy games can bolster your executive function skills in an entertaining and engaging way.

Games for Different Age Groups

When you're looking to boost executive functions across different age groups, choosing age-appropriate and engaging activities is key. Here’s a roundup of games that cater to various developmental stages, ensuring fun while enhancing cognitive skills.

Games for Kids

For younger children, games should focus on multipurpose activities combining fun with cognitive development. A classic choice is Simon Says , which promotes impulse control and attention. For kids who enjoy a digital approach, memory games based on a computer or tablet offer a dynamic way to improve working memory and concentration.

  • Hand clapping games : improve coordination and memory
  • Board games like Chutes and Ladders : assist in learning about consequences and encourage turn-taking

Younger Kids (Ages 3-6)

  • Simon Says  – Focuses on attention and self-control.
  • Hopscotch  – Improves planning and sequencing skills.
  • Happy Salmon  – Enhances attention, speed, and the ability to switch tasks.
  • Feed the Woozle  – Develops fine motor skills, counting, and turn-taking.
  • Snug as a Bug in a Rug  – Encourages teamwork, problem-solving, and using strategies.
  • Outfoxed! – A cooperative game that develops logic and deduction.

School-Aged Kids (Ages 6-10)

  • Zingo  – A bingo-style game that boosts language and matching skills.
  • Dragonwood  – Enhances strategic thinking and probability evaluation.
  • Rush Hour –
  • Qwirkle  – Improves strategic planning and pattern recognition.
  • Magic Maze  – Enhances spatial awareness and cooperative problem-solving.

Executive Function Toys for Kids

  • Tangram Sets  – Develop spatial awareness and problem-solving skills.
  • Bo p It!   – Improves memory, motor skills, and auditory processing.
  • Balance Beans  – Introduces logic, physics, and math in a fun way.
  • Color Code  – Enhances visual perception, planning, and problem-solving.
  • Kanoodle  – Builds upon spatial reasoning and critical thinking through puzzles.

Games for Teens

Teenagers will benefit from games that require strategy and planning. Consider introducing card games like spades or bridge, which necessitate foresight and adaptability. Also, video games that are more complex, involving mission completions and strategy, can help enhance their problem-solving and organizational skills.

  • Strategy-based video games : boost planning and problem-solving abilities
  • Sudoku or crossword puzzles : sharpen focus and working memory

Middle Schoolers (Ages 11-13)

  • Forbidden Island  – Develops strategic planning and cooperative problem-solving.
  • Cortex Challenge  – Tests memory, speed, and tactile challenges.
  • Suspend  – A balancing game that improves strategic thinking and hand-eye coordination.
  • Rory's Story Cubes  – Stimulates imagination and narrative skills, enhancing creative and critical thinking.
  • Labyrinth  – Enhances planning and problem-solving abilities.
  • Head Rush – Helps improve social interactions and problem solving skills.

Teens (Ages 14+)

  • Pandemic  – Encourages teamwork, strategic planning, and problem-solving.
  • Ultimate Werewolf  – Enhances critical thinking, social cues interpretation, and strategic planning.
  • Spyfall  – Develops questioning, critical thinking, and deductive reasoning skills.
  • 7 Wonders  – Builds upon strategic planning, resource management, and decision-making skills.
  • Codenames  – Enhances language skills, teamwork, and strategic thinking.

Games for Young Adults

Young adults often enjoy games that challenge their intellect and social skills. Escape rooms , whether virtual or physical, encourage teamwork, strategic planning, and time management. Additionally, more complex tabletop games like Settlers of Catan can offer an enticing mix of social interaction, strategy, and critical thinking.

  • Escape rooms : real-life problem-solving with a fun narrative
  • Tabletop games : enjoy socializing while engaging in complex strategic gameplay

Games for Adults

For adults looking to enhance their executive functions, the focus may shift to activities that can be integrated into their routine. Chess , with its intricate strategy and foresight, remains a timeless choice. Online brain training platforms offer a variety of games that target specific cognitive skills and can be conveniently played during breaks or downtime.

  • Chess : a timeless, strategic game to enhance foresight and problem-solving
  • Brain training apps : personalized games to keep your cognitive skills sharp

Improving Specific Executive Functions

These games and activities are designed to sharpen critical aspects of executive functioning. By incorporating fun and challenging elements, you'll hardly notice you're giving your brain a major workout.

Enhancing Working Memory

Your working memory is like a mental sticky note, holding onto information temporarily for processing. To give it a boost, try engaging in card games like Uno or Memory. Board games such as chess also challenge you to keep track of various pieces and potential moves, thereby enhancing your memory retention.

Boosting Cognitive Flexibility

Cognitive flexibility allows you to switch between different concepts or adapt to new rules. Games that require you to alter strategies, like Settlers of Catan, can really exercise this skill. Puzzles that change patterns or require multistep problem-solving also push the limits of your mental agility.

Strengthening Inhibitory Control

Inhibitory control is your ability to suppress impulses and resist distractions. To strengthen this aspect of executive function, activities that require patience and a steady pace, such as the classic game Simon Says , are beneficial. Practicing meditation or mindfulness exercises can also enhance your ability to maintain focus and attention without yielding to interruptions.

Games Encouraging Social Interaction

A group of diverse individuals engage in lively conversation while playing executive function games, fostering social interaction and cognitive development

Playing games with others can significantly bolster your social interaction skills . You'll enjoy the camaraderie that comes with team-based activities and the creativity unleashed through role-playing games, both of which are fantastic for developing language and empathy .

Team-Based Games

Scrabble : This classic word game is more than just a test of your vocabulary. When played in teams, Scrabble encourages you to strategize and communicate with your partner to come up with the most point-scoring word combinations.

Snake Oil : In this imaginative party game, you work with teammates to create and pitch wacky products to different customers. It's a delightful way to practice persuasion and quick thinking while interacting and laughing with others.

Role-Playing Games

Role-playing games (RPGs) are a unique blend of storytelling and strategy, inviting you to step into a character's shoes and explore different personalities and outcomes. These games are not only engaging, but they're also a playground for social and language skills growth.

When you engage in RPGs, you navigate complex social scenarios that require understanding and empathy. Negotiating, problem-solving, and character development are all part of the process, making every session a rich experience in human interaction.

Executive Functioning Through Play

When you integrate play into learning, it can transform the process into a more engaging and effective experience. Let's dive into how fun and games can enhance your executive functioning skills without feeling like work.

Learning Through Fun

Games are a fantastic way to strengthen executive functions (EF) such as flexible thinking and impulse control—key components that allow you to manage and direct your cognitive processes. By participating in carefully chosen activities, you get the chance to practice these skills in a low-stress environment. Consider games that challenge your working memory or require complex problem-solving, as they can be particularly beneficial. For example, playing a board game like chess encourages you to plan and strategize, thereby training your brain to focus and think ahead.

The Relationship Between Play and EF

The connection between play and executive function is rooted in the idea that your brain learns best when you're engaged and free from distraction. Engaging, play-based activities encourage the development of EF skills by offering a safe space for you to experiment and learn from trial and error. Whether it's during infancy or adolescence, the practice of these skills through play can lead to improvements in task initiation, organization, and emotional control. By playing, you're not just having fun; you're carving out neural pathways that facilitate lifelong learning and adaptability.

Games and Academic Skills

When you think of sharpening your academic skills, games might not be the first tool that comes to mind. However, strategic games can enhance your math, reasoning, and language abilities in an engaging and entertaining way.

Math and Logic Games

Math and logic games are a fun route to boost your problem-solving skills. These games often require quick thinking and a strategic approach to patterns and numbers. Sudoku is a classic example that challenges you to fill a 9×9 grid with digits so that each column, row, and section contain the numbers between 1 to 9. Playing Sudoku can improve your number comprehension and logic skills.

Another engaging game that combines math skills with critical thinking is KenKen , which pushes the envelope a bit further by including basic arithmetic operations within a similar grid-based structure. This not only hones your numerical skills but also reinforces your operational understanding.

  • Tower of Hanoi
  • Minesweeper

Language and Word Games

Language and word games not only improve your vocabulary but also strengthen your reasoning and problem-solving skills. Scrabble is a timeless game where your ability to spot language patterns and use strategic tile placements can lead to victory. This game demands you to make the most out of limited resources (your letter tiles) to score points and outmaneuver your opponents.

If you're looking to boost your word recognition and spelling skills, give Boggle a try. It's a fast-paced game where you search for words in a jumble of letters and get points based on word length. The clock is ticking, so your brain is constantly engaged, looking for the next word.

  • Crossword Puzzles
  • Bananagrams
  • Word Search Puzzles

Incorporating Games into Routine

Integrating games into your daily routine can significantly enhance executive function skills. These structured playtimes serve not only as practice for cognitive growth but also as joyful moments for students, families, and therapy participants.

Games in the Classroom

In your classroom, starting the day with a morning meeting that includes short games can boost attention and set a positive tone. For example, you can use board games that require strategic thinking to foster skills like planning and impulse control. Teachers can pick games that align with the day's lessons, turning potentially monotonous reviews into engaging exercises. Consider designating a “Game Friday” where your class can look forward to practicing executive functions through collaborative play.

Family Game Night

At home, parents can reinforce executive function skills by organizing a family game night . Make it a routine event—perhaps every Wednesday—to play games that require memory or quick decision-making. This not only strengthens family bonds but also continuously and playfully engages skills like emotional control and flexibility in thinking. Choose games that would appeal to all family members to ensure everyone's participation and practice .

Executive Function Family Games

  • Dixit  – Stimulates creativity and abstract thinking.
  • Karuba  – Enhances strategic planning and decision-making in a fun, family setting.
  • Beat The Parents  – A family trivia game that encourages knowledge sharing and bonding.
  • Telestrations  – Improves communication, creativity, and laughter in a family setting.
  • Cooperative Board Games  (e.g., Forbidden Desert) – Encourages teamwork, strategic planning, and problem-solving.

Therapy Through Games

Using games in therapy sessions is both effective and enjoyable. If you're a therapist, incorporating tabletop games can help clients work on tasks like emotional regulation and problem-solving. Create a regular book of games that target different executive skills, allowing your clients to choose and empowering them to take charge of their growth. This personal involvement maximizes the therapeutic benefits and keeps the routine fresh and engaging.

Occupational Therapy Executive Function Games

  • Traffic Jam  – Improves problem-solving, sequencing, and collaborative skills.
  • Memory and Sequencing Games  – Such as “Simon” to enhance auditory processing and working memory.
  • Dexterity Games  (e.g., Operation) – Develops fine motor skills, focus, and patience.

Selecting the Right Games

A hand reaches for a game labeled "Executive Function Games" among a selection of other games on a shelf

Choosing the right games to enhance executive function is key to effective skill building. Your selections should align with the skill areas you want to improve, such as focus, flexibility, planning, memory, and abstract thinking.

Considerations for Game Selection

When you're picking out games to improve executive functions, think about the specific skills you are aiming to support. For instance, a strategy game might boost planning and flexibility, while a memory chess game could enhance your working memory and strategic planning abilities.

  • Skill relevance : Ensure the game challenges the executive function skills you want to develop.
  • Age appropriateness : Select games that are suitable for the age and maturity level of the players.
  • Engagement factor : A game should be enjoyable to keep you engaged and willing to play repeatedly.

Look for games that require a combination of skills. A game that needs planning , focus , and abstract thinking will offer a comprehensive brain workout as opposed to a game that targets a single skill.

Adapting Games for Different Needs

Every individual's needs and abilities are unique, and games may require some adaptation to be most effective.

  • Adjust rules : Simplify or modify game rules to make them more accessible or challenging.
  • Use support tools : Implementing tools like timers or visual aids can make it easier for players to keep track of the game and stay engaged.

Remember, the goal is to provide a supportive environment where you can develop executive function skills without getting discouraged. The flexibility of a game to adapt to your needs is crucial for maintaining motivation and progress.

Strategies for Optimizing EF Games

To effectively enhance executive function (EF) skills through games, it's crucial to have targeted strategies and regularly measure progress. These approaches enable you to maximize the benefits of practice through careful planning and prioritizing, fostering problem-solving abilities, and strengthening self-monitoring skills.

Creating a Game Plan

Creating a game plan involves identifying the specific EF skills you aim to improve, such as planning, task initiation, and perseverance. Begin by selecting games known to foster these skills. For example, collaborative games can effectively build skills like critical thinking and working memory .

Next, integrate these games into a routine to encourage consistent practice . Consider the following steps:

  • Identify which EF skills you or your students need to develop.
  • Choose games based on their potential to improve these targeted skills. Collaborative games , for example, can aid in critical thinking.
  • Set clear goals for each gaming session, aligning them with the desired outcomes.
  • Allocate specific time periods for playing these games to ensure regular engagement.

Measuring Progress

After establishing a game plan, measuring progress is key to understanding the effectiveness of the games on EF skills. Self-monitoring and metacognition are vital components of this process.

Consider using a progress chart or journal to track:

  • Dates and durations of game sessions.
  • EF skills practiced in each session.
  • Observable improvements, such as enhanced problem-solving abilities or increased perseverance in tasks.

Through these tracking methods, you will see patterns emerge that could inform future planning and prioritizing . By assessing progress and making adjustments to the game plan as necessary, you can ensure that the chosen games are serving their intended purpose and adjust your approaches to optimize cognitive skill development.

Enhancing Cognitive Skills Through Engaging Game Play

In the quest for enriching activities that not only entertain but also stimulate the mind, brain games stand out as a premier choice. These games, designed to challenge and develop various cognitive abilities, offer a fun game experience that's both rewarding and intellectually stimulating. Whether you're facilitating game play in small groups or planning a solo challenge, the benefits of engaging in brain games extend far beyond mere entertainment.

The Power of Problem Solving in Game Play

One of the core advantages of incorporating brain games into your routine is the emphasis on problem solving. These games often present scenarios that require critical thinking, logic, and creativity to navigate. Through game play, participants are not just having fun; they're actively enhancing their problem-solving skills, applying logic to overcome obstacles, and learning to think several steps ahead.

Brain Games for Small Groups: A Pathway to Collaborative Thinking

For small groups, brain games offer a unique opportunity to foster teamwork and collaborative problem-solving skills. In a group setting, individuals must communicate effectively, share strategies, and sometimes negotiate to advance in the game. This type of game play promotes social skills and emotional intelligence, as participants learn to work together towards a common goal, all while enjoying a fun game that challenges their intellect.

Selecting the Right Brain Games for Your Group

When choosing brain games for small groups, consider the interests and age ranges of the participants to ensure the game is engaging and appropriately challenging for everyone involved. Look for games that encourage strategic thinking, memory enhancement, and flexibility in thinking. The goal is to find a fun game that not only captivates the players but also promotes meaningful cognitive engagement.

The Benefits of Regular Game Play

Incorporating brain games into your regular routine can have lasting benefits on cognitive health. Regular game play can improve attention to detail, enhance memory, and even slow down cognitive decline. Moreover, in the context of small groups, these games can strengthen relationships, build communication skills, and create a sense of community among players.

In conclusion, brain games offer a dynamic and engaging way to enhance cognitive skills through fun game play. Whether in small groups or individually, these games challenge the mind, promote problem solving, and provide a meaningful, enjoyable experience for all ages. By integrating these games into your educational or leisure activities, you're not just providing entertainment; you're fostering an environment where learning and fun go hand in hand.

Frequently Asked Questions

In this section, you'll find targeted questions and answers to help you understand how games can be used to enhance executive functioning skills for different ages and needs.

What board games are beneficial for enhancing executive functioning skills?

Board games like chess and checkers require strategic planning, while games like Settlers of Catan demand resource management and flexibility, all of which can improve your executive functions. The OT Toolbox offers suggestions on various executive function games that can aid in developing these crucial skills.

Can adults improve their executive functioning with specific activities, and which are most effective?

Yes, adults can enhance their executive functions through activities such as brain teasers, memory games, and strategy-based games. Puzzles such as Sudoku or engaging in a new hobby that requires learning skills can also be effective.

How do video games impact the development of executive functions?

Certain video games that demand quick decision-making, problem-solving, and strategic planning can have a positive impact on executive function development. However, it is important to choose games that are challenging yet age-appropriate to ensure progress in these skills.

Which online resources offer free games designed to improve executive functioning?

Websites like Edutopia provide insights on how games can help students develop executive function skills. They often list free games and activities that are both educational and engaging.

What are some engaging executive function tasks that can appeal to high school students?

High school students often enjoy tasks that involve real-world scenarios and collaborative problem-solving. Project-based learning and simulation games which can be found on websites like Edutopia can significantly appeal to this age group.

Are there any executive function exercises tailored for the developmental needs of 7- to 12-year-olds?

Yes, there are many games designed for this age group that target executive functions, such as memory matching games, Simon Says, or obstacle courses that combine physical activity with mental challenges. The Pathway 2 Success suggests enjoyable games, including BLURT , which can improve self-control and cognitive flexibility.

Submit a Comment Cancel reply

Your email address will not be published. Required fields are marked *

This site uses Akismet to reduce spam. Learn how your comment data is processed .

sara blog image

Hi, I'm Sara!

I’m a former occupational therapist with a doctorate, now embracing the rewarding journey of homeschooling my children and raising a neurodivergent child. My younger autistic brother was my inspiration to dive into occupational therapy and dedicate my life to teaching life skills and finding meaningful activities for individuals.

This passion now fuels my mission to use neurodiversity-affirming strategies to support others in homeschooling and life skills development.

At Learning for a Purpose, I strive to create a space where we can explore the beauty of neurodiversity together. Through my blog, I share insights, resources, and strategies aimed at helping neurodivergent individuals thrive in their own unique ways. Whether it’s homeschooling tips, life skills tools, or just a place to find understanding and support, I hope to make a difference in your journey.

I’m also excited to invite you to join the Neurodivergent Life Skills Toolbox Membership . This is a special community designed to empower you and your loved ones with practical tools and strategies for everyday life. Here, we embrace each individual's strengths, celebrate our differences, and learn from each other.

neurodivergent life skills toolbox membership

Thank you for being here. Together, we can create a world where every person feels valued, understood, and equipped to pursue their passions. Let’s embark on this journey together.

Recent Posts

  • Executive Functioning Skills Checklist: Enhance Productivity & Success
  • Executive Function Activities: Boosting Brainpower with Fun Games
  • Executive Function Coaching: Enhance Your Skills Today
  • Executive Function Skills by Age: A Comprehensive Guide for Milestones
  • Uncovering the Powerful Connection Between Executive Function Skills and Emotional Regulation Abilities

Privacy Overview

  • Study Guides
  • Homework Questions

JohnsonThomasModule3Assignment

IMAGES

  1. Integrate Critical Thinking Skills and Executive Functioning

    critical thinking and executive function

  2. Executive Function

    critical thinking and executive function

  3. Executive Functioning Skills Explained

    critical thinking and executive function

  4. Executive Functioning Skills

    critical thinking and executive function

  5. Love this FREE executive functioning poster to help highlight these

    critical thinking and executive function

  6. 10 Executive Functioning Skills: The Ultimate Guide

    critical thinking and executive function

VIDEO

  1. How Important It Is To Control Your Emotions

  2. Webinar: The Correlation between Executive Function and Achievement: Implications for ECE Policy

  3. Step up and answer the call

  4. MAKU DIGRESSES: Trying to Understand Unsolicited Advice

  5. MAKU DIGRESSES: Dialogue about Emotional Intelligence with Youth

  6. MAKU DIGRESSES: Critical Thinking and Problem Solving in Competitive Games

COMMENTS

  1. Critical thinking, executive functions and their potential relationship

    The cognitive components of critical thinking and executive functions mostly belong to higher cognition, and their activation requires the participation of both routine and controlled processing. Using Evans's proposal (2008), both constructs would require the intervention of thought type 1 (automatic, unconscious, etc.) and thought type 2 ...

  2. Executive functions as predictors of critical thinking: Behavioral and

    To further clarify the shared and unique contributions of executive functions, fluid intelligence, and thinking dispositions to critical thinking, we applied the variance partitioning method (cf. Unsworth & Spillers, 2010) which enables the subdivision of the overall R 2 of a particular criterion variable (here critical thinking) into portions that are shared and unique to a set of predictors ...

  3. Using the Mind's "Executive Functions"

    Critical thinking is an ongoing activity; executive functioning is optimal, deep, and intermittent—focused precision at particular times. This sharpening includes self-monitoring and future ...

  4. The role of prefrontal cortex in cognitive control and executive function

    Abstract. Concepts of cognitive control (CC) and executive function (EF) are defined in terms of their relationships with goal-directed behavior versus habits and controlled versus automatic ...

  5. Executive Function: What It Is, How To Improve & Types

    Executive function refers to skills that you use to manage everyday tasks like making plans, solving problems and adapting to new situations. The three main skills are working memory, cognitive flexibility and inhibition control. These skills develop during your lifetime, often declining as you get older. But there are ways to keep and improve ...

  6. Critical thinking, executive functions and their potential relationship

    The central issue of this paper is to review the possible relationships between the constructs of critical thinking and executive functions. To do this, we first analyse the essential components of critical thinking from a psychological and neurological point of view. Second, we examine the scope of the cognitive and neurological nature of executive functions. Third, we propose a model for ...

  7. Critical thinking, executive functions and their potential relationship

    Abstract. The central issue of this paper is to review the possible relationships between the constructs of critical thinking and executive functions. To do this, we first analyse the essential ...

  8. Executive Function

    Executive function describes a set of cognitive processes and mental skills that help an individual plan, monitor, and successfully execute their goals. The "executive functions," as they're ...

  9. Promoting the Executive Functions: Core Foundations, Assessment

    The executive functions (EFs) comprise an interrelated set of higher-order cognitive abilities associated with goal-oriented behavior and emotional and social functioning. ... task persistence, organization of actions and thoughts, generative thinking and cognitive flexibility, and awareness of and ability to modify one's behavior ...

  10. Executive Functions and the Improvement of Thinking Abilities: The

    It includes: (a) four core on-line WM executive functions that are involved in every novel and complex cognitive task; (b) two higher order off-line executive functions, planning and revision, that are required to resolving the most complex intellectual abilities; and (c) emotional control that is involved in any complex, novel and difficult task.

  11. Educating Executive Function

    The brain areas associated with the thinking skills that make up executive functions - working memory, inhibitory control, flexible shifting of attention - are primarily 'located' in what is known as prefrontal cortex (PFC). The neural circuits that support these skills, however, involve numerous brain regions, including areas of ...

  12. Critical thinking, executive functions and their potential relationship

    Critical thinking as a self-regulatory process component in teaching and learning. It is argued that critical thinking acts as another cognitive strategy of self-regulation that learners use in their learning, and critical thinking may be a product of various antecedents such as different self-regulatory strategies. Expand.

  13. Executive Dysfunction: What It Is, Symptoms & Treatment

    Working memory, cognitive flexibility and inhibition control are the foundation of executive function. There are also higher-level processes that can happen, including: Planning. This is when you mentally map out a series of actions that'll help you reach a goal. Reasoning. This is the ability to apply critical thinking.

  14. Creative thinking and executive functions: Associations and training

    Previous research on the association between executive functions and creativity has revealed mixed results. Here, we examined which of three components of executive functioning, working memory (WM) updating, inhibition, and shifting ability, is most strongly associated with which aspect of creative thinking in a group of young adolescents. Moreover, we assessed the effects of specifically ...

  15. Critical thinking, executive functions and their potential relationship

    The central issue of this paper is to review the possible relationships between the constructs of critical thinking and executive functions. To do this, we first analyse the essential components of critical thinking from a psychological and neurological point of view. Second, we examine the scope of the cognitive and neurological nature of ...

  16. What is executive function?

    Español. Executive function is a set of mental skills that include working memory, flexible thinking, and self-control. We use these skills every day to learn, work, and manage daily life. Trouble with executive function can make it hard to focus, follow directions, and handle emotions, among other things.

  17. Executive functions as predictors of critical thinking: Behavioral and

    DOI: 10.1016/j.learninstruc.2020.101376 Corpus ID: 224870499; Executive functions as predictors of critical thinking: Behavioral and neural evidence @article{Li2021ExecutiveFA, title={Executive functions as predictors of critical thinking: Behavioral and neural evidence}, author={Shuangshuang Li and Xuezhu Ren and Karl Schweizer and Thomas M. Brinthaupt and Tengfei Wang}, journal={Learning and ...

  18. ERIC

    The central issue of this paper is to review the possible relationships between the constructs of critical thinking and executive functions. To do this, we first analyse the essential components of critical thinking from a psychological and neurological point of view. Second, we examine the scope of the cognitive and neurological nature of executive functions.

  19. Executive functions as predictors of critical thinking: Behavioral and

    Theories of critical thinking suggest that executive functions play crucial roles in students' critical thinking performance. However, very little empirical research has examined the potential confounding factors of fluid intelligence and thinking dispositions on the relationship between executive functions and critical thinking. Study 1, based on a large sample of university students ...

  20. Executive Function & Self-Regulation

    Executive function and self-regulation skills are the mental processes that enable us to plan, focus attention, remember instructions, and juggle multiple tasks successfully. Just as an air traffic control system at a busy airport safely manages the arrivals and departures of many aircraft on multiple runways, the brain needs this skill set to filter distractions, prioritize tasks, set and ...

  21. Creative Thinking and Executive Functions: Associations and Training

    Despite the importance of Executive functions, Divergent thinking, and intelligence in 21stcentury society, few studies have analyzed these variables in childhood and adolescence.

  22. Improving Executive Function: Teaching Challenges and ...

    Executive functions can be thought of as the skills that would make a corporate executive successful -- the ability to be flexible, interpretive, creative, and have multidimensional thinking. Examples include planning, risk assessment, informed decision-making, deductive and inductive reasoning, critical analysis, and delay of immediate ...

  23. 25+ Executive Function Games to Boost Brain Skills

    Occupational Therapy Executive Function Games. Traffic Jam - Improves problem-solving, sequencing, and collaborative skills. Memory and Sequencing Games - Such as "Simon" to enhance auditory processing and working memory. Dexterity Games (e.g., Operation) - Develops fine motor skills, focus, and patience.

  24. JohnsonThomasModule3Assignment (docx)

    Philosophy. Metacognition, Critical Thinking, and Bloom's Taxonomy Assignment: You explored six topics concepts (Learning, Memory, Metacognition, Critical Thinking, Bloom's Taxonomy and Executive Function) that will contribute to a successful learning in college. 1. Complete the Metacognitive Awareness Inventory in the Learning Activities ...