dissertations on executive functioning

SYSTEMATIC REVIEW article

The relationship between executive functions and academic performance in primary education: review and meta-analysis.

• 1 Department of Education Science, Faculty of Education, University of Zaragoza, Aragon, Spain
• 2 Department of Psychology and Sociology, Faculty of Humanities and Education, University of Zaragoza, Aragon, Spain
• 3 Department of Education, University of Zaragoza, Aragon, Spain

The purpose of this study was to research the relationship between executive functions and academic performance in primary education (6–12 years). Based on 21 samples ( n = 7,947), a meta-analysis of random effects demonstrated a moderately significant weighted effect size ( r = 0.365) and was found to be a good predictor of academic performance. For the subjects of language and mathematics, the results of the random effects model were similar and slightly higher for mathematics ( r = 0.350; r = 0.365). Thus, the theory that executive functions have greater influence on mathematical performance is supported, especially in aspects such as coding, organization, and the immediate retrieval of information. Regarding the different executive function components (working memory, inhibition, cognitive flexibility, and planning), working memory had the highest presence ( k = 14, n = 3,740) and predictive weight for performance, with an effect size of r = 0.370 for random effects, with a moderate level of significance. The moderating effect of variables such as gender and age were also analyzed. After performing a meta-regression, gender resulted in a value of R 2 = 0.49; the age variable was not significant. This result is especially important since age has traditionally been considered to be the moderating variable of executive functions. The review reveals a good predictive power of executive functions in the primary education stage, and it is even higher at the early ages, indicating its great significance in describing future performance. The study also revealed the competencies and specific aspects of the executive functions that affect the way in which its components intervene in the academic area, demonstrating the mediating effect of variables such as physical fitness, motor skills, and memory processes.

Introduction

The educational community has traditionally been interested in what is known as academic performance. This outcome is closely related to the teaching-learning process focused on a specific objective—achievement in school ( Fleischhauer et al., 2010 ; Von Stumm and Ackerman, 2013 ). The topics of success or failure in school, discouragement, and dropping out of school have produced a great deal of research activity ( Covington, 2000 ; Balkis, 2018 ). This interest is reflected in the study by Nieto (2008) , who reviewed 654 studies conducted from 1970 to 1990. The author highlights how the studied variables that condition academic success in primary education have changed over time. In addition, the new century has seen the emergence of new variables that are original and methodological in nature such as group collaboration, collaborative work, project-based learning, and the continuous school day. The literature has traditionally categorized these variables as contextual or personal. The first group of variables includes socio-environmental (family, friends, colleagues), institutional (school, school organization, teachers) and instructional (content, methods, tasks) variables. The second group includes cognitive (intelligence, learning styles) and motivational (self-image, goals, values) variables ( Zeegers, 2001 ; Vermunt and Endedijk, 2011 ). Therefore, if academic performance is “a construct that can have quantitative and qualitative values, and these values provide some evidence and a profile of the skills, knowledge, attitudes and values developed by the student in the teaching-learning process” ( Edel, 2003 , pp. 15–16), then brain functions are essential to understanding how this process unfolds. The findings of neuropsychology in this area are very useful for explaining this relationship ( Kolb and Whishaw, 2007 ; Rosen et al., 2018 ). Therefore, according to Sesma et al. (2009) and Zelazo and Carlson (2012) , educational research should focus on executive functions as they are fundamental for language development and thus for literacy (the foundation for learning) as well as for the processing and organization of received information.

Executive functions are understood as the distinct, but related, higher-order neurocognitive processes that control thoughts and behaviors aimed at achieving an objective or goal ( Anderson, 2002 ; Zelazo and Carlson, 2012 ). Therefore, they regulate behavior and cognitive and emotional activity by means of a set of adaptive capabilities. These functions include working memory (the ability to temporarily manipulate information), inhibition (impulse control), cognitive flexibility (the ability to generate different solutions to a problem) and planning (the development of strategies to achieve an objective); the preceding functions are all considered to be basic processes of this variable ( Baddeley, 1996 ; Anderson, 2002 ). Miyake et al. (2000) produced another similar classification that distinguished between working memory, inhibition and flexibility. Some of the research has produced evidence indicating that the components of this factorial structure are different and change with age ( Willoughby et al., 2010 ; Lee et al., 2013 ; van der Ven et al., 2013 ).

Human memory has been one of the most studied constructs by psychologists ( Loftus and Loftus, 2019 ). If the concept of memory represents the ability to store, retain and recall information, working memory or operational memory refers to storage that is short-term, temporary, and with limited capacity; it is also sensitive to distractions that enable the simultaneous performance of tasks ( Baddeley, 2000 ). Its function is to retain information and manipulate it to perform a task or solve a problem. It receives only the information that a selective awareness recognizes as relevant and useful for performing the activity at hand. In addition, working memory is responsible for updating data and then manipulating and transforming them to plan and guide behavior in crucial cognitive processes such as language comprehension, reasoning, and mathematical calculation ( Anderson and Reidy, 2012 ). Memory is thought to be modular, instead of unitary ( Ferbinteanu, 2018 ). Therefore, memory processes are carried out by three coordinated modules: the phonological loop (responsible for manipulating auditory-verbal information), the visuo-spatial sketchpad (linked to visual and spatial information), and the central executive (responsible for the control of memory systems in directing attention to each task that must be performed and monitoring any changes in context) ( Alexander and Stuss, 2000 ). Therefore, working memory is a multifactorial, short-term mnesic system that is prominently involved in the processes that regulate and coordinate the functions of executive control and selective attention and that are involved in problem-solving ( Engle et al., 1999 ; Baddeley, 2000 ; Engle, 2002 ; Wilhelm et al., 2013 ).

Another component of executive functions, as noted by Matthews et al. (2005) , is inhibition or behavioral control, which is the ability to suppress impulsive behaviors; that is, the ability to suppress dominant but irrelevant responses and focus on important information. One could say that inhibitory control moderates behavior, suppresses impulsive reactions to a stimulus, and enables an appropriate and thoughtful response. It allows individuals to make a choice about their own reactions and behaviors—to think before acting. Because this executive component has both behavioral and cognitive aspects, it can be understood in terms of behavioral inhibition (linked to motor control) and cognitive inhibition. The latter's impact on executive functions enables planning, analyzing and choosing the most appropriate response ( Anderson, 2002 ). Therefore, “inhibitory control involves being able to control one's attention, behavior, thoughts, and/or emotions to override a strong internal predisposition or external lure, and instead do what's more appropriate or needed” ( Diamond, 2013 , p. 136).

Cognitive flexibility refers to quickly reconfigure the mind and to switch between tasks ( Braem and Egner, 2018 ). It involves creating and choosing innovative work strategies (linked to creativity) from a variety of alternatives for performing a task but also the ability to modify the action plan depending on the conditions at any given time ( Anderson, 2002 ; Cragg and Chevalier, 2012 ). Coulson et al. (2012) state that the need to approach complex problems from different points of view validates this theory of flexibility. There is evidence that the solution to a problem sometimes requires a broader and more creative vision to correctly implement the solution. Some authors such as Decety and Sommerville (2003) and Eslinger and Grattan (1993) recognize two aspects of this variable: on one hand, it is reactive in its ability to provide varying answers; on the other hand, it is spontaneous due to the wide range of ideas produced when faced with a new task.

Lastly, Anderson (2002) understands planning as the foresight to execute a task correctly and apply appropriate strategies. In the context of executive functions, planning refers to problem resolution, although as noted by Baddeley (1996 , 2000 ), the working memory and the central executive must function properly to enable the ability to think about what should be done and to set priorities for action. However, planning goes further by coordinating these isolated processes in a certain way; an objective is set, the information is analyzed, the strategies that must be applied are selected, and the activities required to achieve the objective are assessed. Thus, achieving academic success is about effectively completing the important and necessary process executed by the executive functions by identifying the problem, defining the problem, finding alternative solutions, and developing an action plan ( Anderson, 2002 ).

One view of academic performance defines it as the “level of knowledge demonstrated in an area or subject compared to the norm for the particular age and level of education” ( Jiménez, 2000 , p. 33). In addition, “it is the sum of distinct and complex factors that act in the person who is learning” ( Garbanzo, 2007 , p. 46). This construct refers to the evaluation of knowledge acquired in a school setting. It is dynamic in nature (the process of learning) as well as static (the product of learning) ( Suazo, 2007 ). Therefore, it is presented as an index that assesses the quality of education, its efficiency and its productivity. It is the reflection of the different stages of an educational process whose objective is academic success, a process that is the focus of all the initiatives and efforts of educational authorities ( Maturana, 2002 ). Currently, there is a general consensus in the scientific community on the existence of multiple variables and factors that explain academic performance, which display the complex and interdependent relationship between cognitive ability and emotion-attitude variables ( Miñano and Castejón, 2011 ; Núñez-Peña et al., 2013 ). Another classification proposed by Passolunghi and Lanfranchi (2012) distinguishes between domain-general capabilities (the cognitive system as a whole) and domain-specific capabilities (which process a particular type of information). Domain-general capabilities notably include cognitive abilities (knowledge) and emotional skills that broadly predict school performance. Domain-specific capabilities (inferential skills, prescriptive processes) include skills that predict future performance in specific fields (development of a competency).

There are numerous articles that relate executive functions to academic performance (see Ahmed et al., 2018 ; Gordon et al., 2018 ). Studies such as those by Best et al. (2011) , Castillo et al. (2009) , and Ostrosky-Solis et al. (2007) conclude that working memory, a main component of the executive functions, is important for academic performance during the first few years of primary school. This variable develops rapidly at a young age and plateaus during adolescence. Align with this, a longitudinal study conducted by Ahmed et al. (2018) indicates that working memory at 54 months significantly predicts working memory at 15 years old. Furthermore, Tsubomi and Watanabe (2017) found that visual working memory, with and without distraction, develops until the age of 10. The study by Hall et al. (2015) on children 5 to 8 years old concluded that primary memory capacity improves with age. In addition, López (2013) study on third grade students found that good academic results in language and mathematics are related to this variable. Therefore, there is clear evidence that memory is a good predictor of academic performance by primary school students. However, this is not the case for the later stages of education because the predictive power of this variable diminishes at around the age of 12. Other authors in this line of research are Aronen et al. (2005) , Best et al. (2011) , Lee et al. (2009) , and St. Clair-Thompson and Gathercole (2006) . In addition, original results from Alloway et al. (2010) and Bull et al. (2008) indicated that the association of these variables is sustained over time, emphasizing the specific relationship between visuo-spatial working memory and performance in mathematics (domain-specific); the other executive components predict domain-general learning. Focusing on another facet, studies by Alloway et al. (2008) and Abreu et al. (2014) concluded that learning difficulties are explained by deficiencies in this executive component and are therefore reflected in academic performance.

Various studies have focused on the analysis of other components of the executive functions. For example, behavioral inhibition, that is self-control is shown to be relevant for academic achievement ( Duckworth et al., 2019 ). In a longitudinal study of children 3 to 7 years old, Blair and Razza (2007) found that the relationship between academic performance and attention control and inhibition depend on age and the subject studied. Latzman et al. (2010) studied whether different academic subjects place specific demands on the various components of executive functions, analyzing the link between this variable and the performance of children 11 to 16 years old in science, mathematics, social studies, and reading. Of the various factors studied, cognitive flexibility was associated with reading and science and the control or regulation of reading and social studies capabilities. Gerst et al.'s (2017) study of children 5 to 11 years old found that inhibition and planning were the strongest predictors of mathematical calculation. For Sesma et al. (2009) , working memory and planning are needed more when the complexity of a written text increases, and inhibition is related to mathematics and science. As such, these results suggest that there are specific demands placed on the various executive functions depending on the academic domain ( Passolunghi and Lanfranchi, 2012 ). Therefore, there is widespread agreement that the skills related to executive functions, such as recalling and retaining information (working memory), the ability to suppress distractors (inhibition control-attention control), the ability to combine different tasks (cognitive flexibility), and planning (the ability to foresee the correct execution of a task) are essential for academic achievement since changes to these skills decrease the likelihood of success.

The current study analyzed the relationship between executive functions and academic performance in primary education. This was considered necessary as most of the publications on academic performance in primary education over the last decade have found this variable to be more significant for academic performance than the intelligence quotient, the variable traditionally considered to be the best predictor of academic success ( Ren et al., 2015 ). In addition, we studied which executive function component (working memory, inhibition, cognitive flexibility and planning) would have a greater predictive weight since most of the existing studies have found a single component in the 2 to 6 years old age group ( Wiebe et al., 2008 ) and a multifactorial composition after the age of seven ( Jarvis and Gathercole, 2003 ; Jacobson and Pianta, 2007 ). It was also important to study whether the executive functions were included within the domain-general or domain-specific cognitive variables, whether their components changed according to the academic subject, whether they predicted performance in specific competencies ( Im-Bolter et al., 2006 ), and whether they have a moderating function in other variables regarding academic performance. Lastly, we analyzed possible moderating variables such as sex or age. Age has traditionally been the variable with the moderating effect. However, this result was not expected for this study as it focuses on primary school students 6 to 12 years old, a group in which males and females have different levels of maturity. Data were obtained for all these study objectives to calculate the effect size of the relationships and the significance of the variability between the samples.

Based on this literature review, our research questions are aimed to explore whether there is a relationship between executive functions and academic achievement among students from Primary education. Also, we will take into account whether this association is influenced by the following aspects: subject –e.g., mathematics, literature…-, gender, and age. For this background, our research questions are about the relationship between executive functions and academic performance in the stage of Primary Education. In addition, a specific study is carried out on this relationship and specific areas such as language and mathematics taking into account other variables such as gender and age.

Inclusion and Exclusion Criteria

The following inclusion criteria were established: (a) the studies should provide clear and correlational statistical data between the variable of executive functions or any of its components (working memory, inhibition, flexibility or attention) and academic performance; (b) age, since the research focused on primary school students 6 to 12 years old; (c) articles that studied the same variables from an inverse approach, that is, the relationship between the executive functions and poor academic performance; (d) articles that included in their samples any individuals diagnosed with a DSM-5 mental disorder and did not exclude individuals with normal development; (e) articles that researched samples of individuals with low socioeconomic status; and (f) longitudinal studies conducted in the pre-school stage that focused on predicting future performance and those that started in primary school and progressed through secondary school (17 years). The following criteria were grounds for exclusion from the study: (a) studies conducted in a clinical context –that is, in samples with a typical development-; (b) studies where the entire sample consisted of individuals diagnosed with learning disorders; and (c) studies that failed to fulfill the criterion of statistical clarity. The reason for this last exclusionary criterion is that, per Chalmers et al. (2002) , the individual studies had to be integrated into the current study to conduct the analysis; in addition, they had to have a certain degree of similarity and comparability.

Search Strategies

An electronic search was conducted (July–September 2018) on the Scopus, PsycINFO, PubMed Central and Redalyc databases. The search was performed in the English language and applied the terms “academic achievement,” “primary education,” and “correlation” with a 2009–2018 date range. This filter yielded a total of 1,012 documents that met the search requirements. Next, the titles and abstracts of these articles were reviewed, and 925 were excluded because they corresponded to clinical settings, did not meet the age parameters, followed a non-descriptive methodological approach, or did not offer clear statistical data. The final sample of 87 publications provided information on the variables that were studied the most over the last decade and that were related to academic performance. The most numerous group of articles studied the executive functions and personal motivation factors (41 articles). Considering the divergence between these two topics, it was decided that each would be studied separately, focusing first on the executive functions. An in-depth review of the material selected reduced the number of valid studies to 10 that could be used in the proposed research. This number was considered to be insufficient; thus, the bibliographic references in the articles were reviewed, and those not meeting the language criterion were eliminated in the search scope. A search of the “gray” or “fugitive” literature ( Cooper et al., 2009 ) was also conducted, which included conference databases, doctoral theses, conferences, and meetings. These publications did not yield any information of interest for the present study; however, it was not an exhaustive search. Ultimately, 19 articles were selected (19 in English and 1 in Spanish) that provided 21 samples or databases ( Alloway and Alloway, 2010 and Hall et al., 2015 provided two each) for use in a meta-analysis of the predictive capacity of executive functions in the academic performance of primary school students. In addition, the executive function components that recurred in the analyzed studies were working memory, inhibition, cognitive flexibility, and planning. No additional data beyond those published were requested from any author. The current research study will become part of a section on logical reasoning, verbal factors and working memory to be included in a doctoral thesis that consists of a compendium of publications titled “Variables that Influence Academic Performance in Primary Education: Tradition or Innovation” ( Figure 1 ).

Figure 1 . Flowchart of the inclusion protocol.

Coding Procedure

The study complies with the guidelines from the manual of systematic reviews (see Cochrane 5.1, point 1.2.2, Higgins and Green, 2008 ), in which it is established a set of clear objectives, specific search terms and eligibility criteria for previously defined studies. All studies were coded separately. In some articles, the executive functions are referred to as a single factor, and others refer to the different factors that compose them (working memory, inhibition, cognitive flexibility, and planning). Academic performance was measured in two dimensions: reading, measured in selected studies such as fluency and reading accuracy by reading words (reading comprehension, reading fluency, vocabulary) and mathematics (mathematical reasoning, calculus, arithmetic). A total of 198 effect sizes were coded using the correlation itself as a reference, and the corresponding standard error and confidence intervals were calculated. Similarly, these data were integrated using averages and weighting, and the academic performance and the overall executive functions of each study were calculated so that statistical analyses could later be performed individually.

Effect Size

This study's statistical approach applied an analysis of two continuous variables; thus, the correlation coefficients were used as the effect size to establish the relationship between executive functions and academic performance. Regarding the sample correlation coefficients, it was decided to transform them into Fisher Z -values, thus ensuring that the variance of the effect size will be based on the sample size. Cohen (2013) , an effect size is considered to be small when the correlation coefficients do not exceed 0.10; they are considered to be moderate at 0.30; and they are considered to be large if they exceed 0.50.

Statistical Analysis

To examine the variability of the sampling, the parameters studied were the Cochran Q test (to test the null hypothesis of homogeneity between the studies) and the I 2 (proportion of the variability). According to Higgins et al. (2003) , if I 2 reaches 25%, it is considered low; It is considered moderate if it reaches 50%; and it is considered high if it exceeds 75%. This may be due to a sampling error, a real variability in the variance and the size of the effect, or the influence of a third variable acting as a moderator. In this sense, different meta-analyses were applied that excluded studies with atypical data. In addition, the results produced by model 1 (fixed effects) and model 2 (random effects) were analyzed. Due to the number of research studies in the sample (21 databases), it was determined that model 1 is initially more appropriate ( Overton, 1998 ; Schulze, 2007 ), since a study of fixed effects assumes a real and real size of the effect, and the variability of the sampling supposes an error in the sampling. However, in a random effects model with a more conservative approach, the sampling variability is lower and is not considered a sampling error but a real variability in the variance and in the size of the effect ( Borenstein et al., 2011 ). Therefore, considering both approaches, we decided to perform an initial analysis that included all the studies and then eliminate those that showed outliers. The comparison of the two showed that the sampling variability was affected by some of them ( Q = 119.359, I 2 = 83.24); therefore, they were treated separately to explain the results. To clarify, these research studies were not eliminated, and although the sampling variability decreased ( Q = 43,536, I 2 = 58,655), even without them, the sample variability did not reach 50%. Therefore, in the presence of variability and heterogeneity, it is established to follow the work based on the random effects model. In addition, a meta-regression of random effects was carried out, taking gender and age as moderators, since numerous studies indicate that executive functions and their cognitive influence vary with age ( Ostrosky-Solis et al., 2007 ; Castillo et al., 2009 ). The software used to classify and encode data and to produce descriptive statistics was the EZAnalyze add-on (Microsoft Excel, 2007). The integral meta-analysis software (CMA, Biostat, USA) was used for meta-analysis and meta-regression calculation data.

General Description of the Studies Included in the Research

A search of the literature related to the topic and published in the last decade produced a small number of articles. This is because the search was limited to a specific age range (6 to 12 years) corresponding to primary education. In addition, there was a requirement that the studies have clear correlation statistics so that the data could be integrated. The studies describe research conducted in various parts of the world, which enabled us to determine if the results displayed significant differences depending on the dominant culture in the respective countries. Therefore, we found not only diverse cultures but also a variety of educational systems, although it should be noted that the African continent and South Asia were not represented. Of the 21 databases pertaining to the 19 articles selected, a total sample of 7,947 individuals was obtained ( Table 1 ). The smallest data set contained only 60 individuals, and the largest data set contained 2,036. Three of the studies did not provide information on the number of participants by gender or on the composition of the sample; the published data indicate that 51.27% of the individuals were male and 48.73% were female. A total of 26.31% of the studies pertained to the United States, representing 45.72% of the participants. The Netherlands had 15% of the studies, representing 25.64% of the participants. The United Kingdom had 15% of the articles but only 5.78% of the participants. The sole Norwegian study stands out with 14.16% of the sample. Therefore, Europe represents 48.55% of the sample, North America 48.28%, South America 1.44%, and Asia 1.73%.

Table 1 . Characteristic of studies included in the meta-analysis.

It is interesting to note that five articles address only the reading aspect of academic performance; one article addresses only the mathematical aspect, and the rest examine both reading and mathematical skills. Furthermore, of the executive functions, working memory is the factor that appears most often, sometimes in conjunction with inhibition, flexibility or attention. Seven of the studies have a longitudinal design, and two address academic performance from the opposite perspective (poor reading performance). Of these, several stand out: Aarnoudse-Moens et al.'s (2013) study on the effect of premature birth on subsequent performance with a control group, and Sesma et al.'s (2009) studies on groups diagnosed with conditions such as ADHD, dyslexia, and dyspraxia. Sesma et al.'s study examines a group with weak word recognition and another with poor reading comprehension. The socioeconomic status of the families, the educational level of the parents, language difficulties, gender, and age all are examples of the diverse interests represented in these articles. The sources of the samples vary; some were obtained from existing projects, whereas others pertain to the entire country, to a single city, to a single school, to rural areas, or to urban areas ( Table 1 ).

The procedures followed to measure academic performance for the most part correspond to the standard achievement tests of each country. The Woodcock-Johnson III test was used by five authors including Welsh et al. (2010) who also used the TOPEL test for reading achievement. Sánchez-Pérez et al. (2018) opted for PROLEC. The WIAT-II tests were selected for the studies by Bryce et al. (2015) and Sesma et al. (2009) . Only Alloway and Alloway (2010) used the Wechsler test, and Oakhill et al. (2011) used the Neale Analysis of Reading Ability (NARA) and CAT tests. The studies by Tsubomi and Watanabe (2017) and by Abreu et al. (2014) were exceptions in that the teachers themselves were responsible for evaluating the academic performance of the students. In any case, it is indicated that the reading tests used are aimed at reading words to measure fluency and accuracy. As for the instruments used to measure the executive functions, sometimes a single component was measured such as working memory, etc., and others considered the executive functions as a whole, always depending on the age of the subjects being evaluated. When working memory was addressed, the applied tests were the “Automated Working Memory Assessment-AWMA” ( Alloway et al., 2008 ) and the Wechsler Intelligence Scale for Children (WISC-III and IV). The Stroop Color test was used in the studies on inhibition, and a wide variety of other instruments were used for other components (for example, the duck task for cognitive flexibility or the Tower of London for planning); the use of computers and specific software for these tests was noteworthy.

Effect Size and Statistical Significance

Figure 2 (Forest Plot) and Table 2 both present the effect size and confidence interval (95%), for the studies with regard to general academic performance and overall executive function. The individual analysis of each sample is presented as well as the weighted results for random effects model. The meta-analysis of the variables concludes that the data obtained have good consistency. The executive functions presented an effect size of r = 0.365, with a 95% confidence interval ranging between 0.309 and 0.419 for a sample of k = 21 and a population of n = 7,947. None of the intervals were zero; as such, there is a medium weighted mean effect size with a significance of p < 0.05. A second calculation (for most of the studies linking academic performance to mathematics and language) presents the effect size and the confidence intervals of the executive functions for the two academic areas in Table 3 . The results indicate that the effect size for mathematics is slightly higher ( r = 0.365), which is consistent with other studies, indicating that executive functions are a better predictor for this area than for language ( Brock et al., 2009 ; Willoughby et al., 2012 ). Again, there is a medium weighted mean effect size with a significance of p < 0.05.

Figure 2 . Forest plot effect size (Pearson's r). Executive functions—academic performance.

Table 2 . Effect size: executive functions—academic performance.

Table 3 . Effect size: executive functions—academic performance in mathematics and language.

Next, the same procedure was performed for the executive components. Working memory is the factor that is most prominent in the research (in 14 of 21 databases). As such, it was the first factor analyzed with respect to overall academic performance and subsequently with respect to mathematics and language ( Table 4 ). The effect size for this first statistical calculation is 0.370 for random effects, with a confidence interval of 95% (0.287 to 0.447). The sample consisted of 13 studies with k = 14 databases and a population of n = 3,740 individuals. Eleven articles studied the links between performance in mathematics and working memory, and 13 studied the links to language development. A moderate and average effect size was found for both studies. These results support the theory that the executive functions are a better predictor of performance in mathematics, especially in aspects such as coding, organization and the immediate retrieval of information—what we call working memory ( Bull and Scerif, 2001 ; St. Clair-Thompson and Gathercole, 2006 ).

Table 4 . Effect size: working memory—academic performance, performance in mathematics and language.

Heterogeneity Analysis

The variability among the different samples of the relationship between academic performance and executive functions was significant ( Q = 119.349, df = 20, p < 0.000), and the I 2 was 83.242%), which was higher than expected. However, the results of the random effects model were more conservative (since there were fewer than 30 samples). With these results, it was appropriate to test the sensitivity of the sample by performing a second meta-analysis that excluded two studies: de Bruijn et al. (2018) and Mulder et al. (2017) . This second meta-analysis yielded the following: Q = 43.537, df = 18, p < 0.001, I 2 of 58.656%, and significant moderate variability. Two findings stand out: the first is the outlier values obtained by the discarded studies, and the second is the possible existence of moderating variables; these will be specifically addressed in another section. The meta-analysis performed for the variables of academic performance and working memory presented similar results ( Table 5 ): Q = 87.910, df = 13, p < 0.000 and I 2 of 85.212%; these values again decreased upon excluding the three above-mentioned articles.

Table 5 . Sampling variability: working memory—academic performance.

Atypical Values or Different Sample Sizes

Once the studies with outlier values ( Mulder et al., 2017 ; de Bruijn et al., 2018) were identified, and although the sampling variability was diminished by excluding them, there were no significant differences in effect size ( r = 0.365, Q = 119.349, df = 20, I 2 = 83.242%; r = 0.398, Q = 43.537, df = 18, I 2 = 58.656%). Furthermore, when studies with larger populations were discarded, there were practically no differences in the results ( r = 0.359, Q = 85.318, df = 17, I 2 = 80.075). Therefore, we decided not to exclude any sample from the meta-analysis since no sample amounted to 50% of the statistical weight. In addition, the differences in effect size were not significant. However, distinct analyses of the three studies were conducted to explain the causes of this reduced variability of the differences indicated by the values for the Q -statistic.

Publication Bias Analysis

The funnel plot ( Figure 3 ) facilitates the verification of the existence or not of bias regardless of the size of the sample. This graph shows that the results obtained for the Z values from the studies included in this meta-analysis, show small values that range between 0 and 1. As indicated by Palma and Delgado (2006) they would indicate an absence of bias, since that the existence of the same is considered from the significantly distant values of 0. In the same way, when the Egger test is performed, the value in the interjection point of the ordinate axis is 0.24 (close to 0). This author points out that a higher value would indicate the existence of bias ( Egger et al., 1997 ). At the same time, the p -value (0.404) is therefore >0.1, so the results of the funnel plot would be confirmed. From all these data it is deduced that the present study does not show any problem related to a possible publication bias.

Figure 3 . Funnel plot. Executive functions—Academic performance.

Moderating Variables

Various studies confirm that both age and gender are two moderating variables of executive functions ( Ostrosky-Solis et al., 2007 ; Castillo et al., 2009 ; Ganley and Vasilyeva, 2011 ; Rogers et al., 2011 ; Bull et al., 2013 ; López, 2013 ). Therefore, these variables were analyzed to check their degree of moderation or their power to explain the variance. First, a meta-regression (over random effects) was performed on age as a moderating variable, and no significance was found. A possible explanation for this lack of significance is the age parameter because primary education covers a range of young ages linked to a specific psychosocial stage of development. A second meta-regression (over random effects) was performed that included gender, and moderate significance was found ( R 2 = 0.49); that is, gender can explain 49% of the variance. Unlike the studies referenced above, the female gender explained this relationship, possibly because of the greater tendency in females toward mature development ( Ausubel and Sullivan, 1983 ); as such, this question was deferred to a future research study. Regarding this model's goodness of fit for the modified sample, the results ( Q = 54.16 and I 2 = 66.77%) were lower than those of the meta-analysis due to the changed values in the meta-regression. In conclusion, of all the possible moderating variables in the meta-analysis, only gender had the capacity to explain a moderate degree of variance (49%). No significance was found for the age variable because of the sample homogeneity that occurs when researching a specific period of education (see Tables 6 , 7 ).

Table 6 . Meta-regression with moderating variables: Age and Gender.

Table 7 . Meta-regression: Gender.

The purpose of this study was to investigate the links between executive functions and academic performance in primary education over the last decade. This review and meta-analysis found that executive functions are considered to be good predictors of academic achievement in normally developed children ( r = 0.365). Delving deeper, evidence of the following was obtained: (a) the multifactorial composition of the executive functions, in which working memory has the most significant influence on academic performance ( r = 0.370); (b) the presence of a certain moderating effect of executive functions on other variables of academic performance; and (c) the moderating function of gender ( R 2 = 0.49).

The literature provides numerous examples of the importance of executive functions in achieving academic success (see Huizinga et al., 2018 ; Willoughby et al., 2019 ). Language development is essential for proper learning, and cases of low reading ability demonstrate some deficiency in these skills ( Abreu et al., 2014) . There is a recognized problem specific to language that is associated with a poor working memory and that prevents normal language development ( Im-Bolter et al., 2006 ). Furthermore, distraction directly influences an individual's ability to focus on and correctly capture external stimuli ( Gray et al., 2015) . Therefore, if the verbal component and logical reasoning are the foundation for good academic performance, they are themselves related to the development of executive functions. In addition, if they have a robust power to predict subsequent academic success, there is ample evidence that justifies an interest in understanding and examining all aspects of their behavior with respect to academic performance.

The intelligence quotient has traditionally been the most important factor in predicting academic performance ( Vukovic and Lesaux, 2013 ; Ren et al., 2015 ); however, it diminishes in importance at the university level ( Reynolds and Walberg, 1992 ; Patrikakou, 1996 ). Some studies conclude that intelligence is the variable with the most variance in explaining school performance ( Staff et al., 2014 ). The data gathered for this review and meta-analysis confirm that, at present, the executive functions and the intelligence quotient have the same degree of predictive capacity regarding school performance, with the intelligence quotient being more important for new learning, and the executive functions being more important for learning that is repetitive and focused on competencies. Therefore, our findings are in line with some recent research ( Costa and Faria, 2018 ; Lotz et al., 2018 ). Particularly, for Aarnoudse-Moens et al. (2013) , the “g” intelligence factor explains poor performance in mathematics during the pre-school years; however, they found similar prediction values for executive functions and the intelligence quotient during primary education. Ribner et al. (2017) obtained similar results, but with respect to good student performance in mathematics and language. These results suggest that mathematical problems are increasingly complex at this stage of education, which is why highly developed cognitive skills are necessary. The research by Best et al. (2011) , Hall et al. (2015) , or Tsubomi and Watanabe (2017) all highlight the importance of executive functions in the early years of primary education and the rapid development of working memory at a young age, to achieve stability between the ages of 10 and 12. In Alloway and Alloway's (2010) article, this mnesic-executive aspect emerges as a better predictor of future performance (in literacy and mathematical reasoning) than the intelligence quotient. In addition, they highlight the importance of early intervention to improve future results as well as the independence of both variables. These results are explained by the static nature of intelligence as opposed to the executive functions that change with age and neurocognitive maturation. Gómez-Veiga et al. (2013) performed a regression analysis and found that working memory (ß = 0.28) and fluid intelligence (ß = 0.30) explain 33% of the variance in reading comprehension. Similar predictive values are found in the direct correlations between the aspects of memory that are linked to executive functions and academic performance and the aspects of intelligence that are linked to academic performance.

Another important issue is the dilemma raised regarding the homogenous or multifactorial composition of the executive functions that are explained by their own evolution and development ( Best et al., 2011 ). The results of this study support the notion of a multifactorial composition of executive functions within the context of primary education (ages 6–12) because the meta-analysis revealed that working memory is the most studied component (14 of 21 databases), displaying an effect size of 0.370 for random effects, which gives it more predictive power than inhibition. There are numerous studies in this field on pre-school children; however, there are few for the primary education years. There are two aspects to consider. On one hand, Wiebe et al. (2008) found that executive functions in children 2 to 6 years old have a homogenous composition. On the other hand is the opinion that this variable has several related but totally distinct components: working memory, inhibition and cognitive flexibility ( Miyake et al., 2000 ; Bull and Scerif, 2001 ; St. Clair-Thompson and Gathercole, 2006 ). Some authors include another factor—planning ( Anderson, 2002 ). Several studies ( Isquith et al., 2004 ; Senn et al., 2004 ; Huizinga and van der Molen, 2007 ) conclude that inhibition is the best predictor of academic performance up to the age of seven. After that age, working memory is the most important, and then cognitive flexibility becomes the most important after the age of 11. These findings supposedly indicate that inhibition develops first, with other components emerging later such as working memory and cognitive flexibility. That is, age produces changes in the relationships between the executive function components and academic performance ( Jarvis and Gathercole, 2003 ; Jacobson and Pianta, 2007 ).

This study sheds light on the question about the modularity of brain, that is, that the brain can be conceptualized as a network which comprises some modules ( Baniqued et al., 2018 ). Therefore, this review of indicates that the executive functions have general, overall characteristics and their components have specific characteristics. The distinct factors of this variable are better related to academic performance depending on the subject matter studied. This is because the specific development of certain skills and abilities is needed for school performance. The results of this meta-analysis are consistent with the literature reviewed, and they highlight the relationship between mathematics and the visuo-spatial aspect of working memory. Moreover, most of the executive function components correlate better with academic performance in mathematics than in language ( r = 0.374; r = 0.331). It can also be concluded that, despite its general nature, by contributing to the development of different aspects of learning, working memory becomes more specific in nature in the development of particular skills. Therefore, it is identified as being a relevant and specific sub-variable depending on whether its auditory-verbal or visuo-spatial aspect is engaged. Similarly, the other executive function components such as inhibition (with verbal or visual distractors), cognitive flexibility, selective attention (of distraction or attention with verbal or visual stimuli), and planning display specific characteristics by significantly correlating with the development of academic skills. de Bruijn et al. (2018) published a study on poor reading performance in which working memory became more important than inhibition. That is, when encountering learning problems, the variables act differently. Gómez-Veiga et al. (2013) , Nouwens et al. (2017) , Oakhill et al. (2011) , and Sesma et al. (2009) all agree that the two aspects of working memory (visuo-spatial and auditory-verbal), are deemed to be predictors of reading comprehension, especially the relationship between the auditory-verbal aspect and the tasks of storage and symbolic recall. Furthermore, Tsubomi and Watanabe (2017) found that visual working memory without distractors selectively predicts performance in mathematics. The study by Welsh et al. (2010) presented results that demonstrate predictive reciprocity between mathematics and the executive functions of working memory and attention. For Aarnoudse-Moens et al. (2013) , Gray et al. (2015) , and Mulder et al. (2017) , inhibition explains the lack of attention and highlights the visuo-spatial component of the memory function for performance in mathematics. In addition, Gerst et al. (2017) contend that cognitive flexibility and planning are good predictors of that area. Abreu et al. (2014) establish relationships between reading and the executive functions of working memory and cognitive flexibility.

It should be noted that in some of the articles reviewed in this study, the executive functions, in addition to acting as a predictor in direct models of academic performance, have a certain moderating influence on other variables such as physical fitness, motor skills or memory processes. The studies by Aadland et al. (2017) and de Bruijn et al. (2018) introduce the variable of activity or physical aptitude. The latter study examines its relationship with poor academic performance; however, both adopt the perspective of the moderating or mediating effect of the executive components. Aadland et al. (2017) did not find any potential moderating influence of executive function on physical activity and academic performance; however, they did find a slight effect on the ability to work with numbers and motor skills. From the opposite perspective, de Bruijn et al. (2018) demonstrated an indirect relationship between physical fitness and poor academic performance, moderated by the executive functions with respect to mathematics and spelling. In addition, verbal working memory is both a domain-general and domain-specific mediator, and its visuo-spatial aspect is related to poor academic performance in mathematics. Similarly, Oberer et al. (2018) find that executive functions, visual motor coordination, and physical fitness predict subsequent academic performance and that executive functions act as moderators between physical fitness and academic performance. Bryce et al. (2015) find that the variable of cognitive abilities robustly contributes to school performance within a structured model where the executive components act as mediators. All this is based on the close relationship between the two such that the performance of the former will be predetermined by the development of the latter. They conclude that executive functions contribute positively to enabling the youngest students to use their cognitive skills appropriately.

This literature review and meta-analysis confirms that the executive functions display greater predictive power at early ages and have a robust, specific capacity for predicting future academic performance. Thus, it is important to detect academic achievement problems as early as possible to initiate intervention programs. The intent would be to minimize any potential problems that are inherent in learning, particularly those that hinder normal development in language and mathematics. This is confirmed by some of the longitudinal studies reviewed here such as those by Aarnoudse-Moens et al. (2013) , Alloway and Alloway (2010) , Hall et al. (2015) , Oberer et al. (2018) , and Welsh et al. (2010) . However, in some cases, the relationship patterns between these variables are sustained throughout the longitudinal study for all of the various age groups ( Oakhill et al., 2011) . Other studies determined that normal, early childhood development helps students who begin their schooling late catch up to the rest of the students ( Ribner et al., 2017 ). Of note are the studies that begin their research in the pre-school stage and conduct follow-ups over 3 to 6 years, with the objective of predicting academic performance in primary school. Best et al.'s (2011) article on a study of children from 5 to 17 years old (the broadest age range) determined that the most intense development of executive functions occurred among the youngest children. It then slowed somewhat in the last years of infancy and declined during adolescence. That study also demonstrated a direct relationship between this variable and academic performance as well as an indirect relationship through the verbal factor and logical reasoning. This connection indicates the link that executive components such as working memory, inhibition and attention have with mathematical competence and language development. On the contrary, Bryce et al. (2015) used a specific structured model based on the moderation of variables and concluded that after the age of seven, executive functions and cognitive abilities decline dramatically in the subsequent 5 years. Despite this discrepancy, our meta-analysis suggests that executive functions are essential for the development of academic skills in primary school.

Characteristics such as age, gender, socioeconomic status, and physical fitness can act as moderators in the relationship between executive functions and academic performance, as shown in previous research (see Thomson, 2018 ; Kvalø et al., 2019 ). In the current meta-analysis, two meta-regressions were performed—one for age and one for gender—and no significance was found for the first (age), and a 49% variance was found for the second (gender). A possible explanation for these findings is that during this age range, females mature more rapidly than males. The 7 to 12 age range corresponds to a period of cognitive transition that Piaget (1991) calls the concrete operational stage. Children can make logical inferences and reversible mental operations, and they can formulate hypotheses. In this stage, the reinforcement of mnesic processes and metacognition occurs (memory, knowledge, learning strategies, the monitoring of one's own thoughts, semantic elaboration). It is precisely in this educational period when gender differences between boys and girls become the basis for the diverging cognitive development of the genders. Different aptitudes, behaviors and abilities emerge ( Calvo, 2009 ). Kovacs and Devlin (1998) make a number of observations on this topic: there is a different rhythm of physical, cognitive and psychic maturation for men and women. Females mature at an earlier age, which produces disparities in learning and academic performance. Consequently, females display better writing skills during the first years of school (due to the development of fine motor skills) as well as better verbal skills and abilities. Males have the advantage of better visuo-spatial capabilities due to the effects of testosterone.

There have been numerous studies that examine the differences in academic performance between the genders. Hyde et al.'s (1990) meta-analysis compiled hundreds of studies on the influence of gender on academic performance. Half of the studies showed very minor differences, and a third of the studies found no differences at all. The widely held acceptance of the disparate cognitive abilities of men and women was broken down, and it was suggested that social and cultural factors influenced performance in the various academic fields. There is no evidence that boys are better at mathematics and girls are better at language. The study by Hyde and Mertz (2009) aligns with the one by Hyde et al. (1990) as it concludes that girls achieve the same results as boys in standardized math tests. In addition, there is no difference in language ability between men and women. Other studies, such as those by Alcaraz and Guma (2001) or Mathiesen et al. (2013) , contend that in addition to the divergence that comes from sexual dimorphism, there are differences in brain anatomy such as the larger corpus callosum in females, which facilitates the processing of language, and a larger nucleus of the hypothalamus in males that influences emotions. The latter author determined that the literature on the cognitive development of the two genders presents different results depending on age, the time period, and location. Along this same line, Bethencourt and Torres (1987) , Herrera et al. (2000) , and Steinmayr and Spinath (2009) determined that girls start school with significantly higher levels of lexical and motor skill development than boys. This could be due to their earlier maturation, which can present differences of almost 2 years during puberty. These authors also assert that overall performance by females is on average slightly higher than male performance during their first years of school. In addition, they note that females have better inhibitory control; however, no gender-based differences in the processes of cognitive flexibility were noted. In the early years of primary education, girls demonstrate better results for working memory, short-term memory and attention. In the later years of primary education, due to age and years of schooling, they achieve a good level of execution, categorization and conceptualization ( Reyna and Brussino, 2015 ). These results, which are better for girls than for boys, will be related to the language capabilities attributed to left-hemisphere brain development, which is delayed in males due to the presence of testosterone ( Acosta, 2001 ). Therefore, the existing literature is completely consistent with the results of our meta-analysis, in which gender emerges as a moderating variable between academic performance and the executive functions during the primary school years.

It is important to note that in the conducted meta-analysis, there are two articles with outlier data. However, this is not due to a bias error but to a real variability in the variance and effect size. This result is due to the particular research design and the treatment of the academic performance variable. In some cases, as in the article by de Bruijn et al. (2018) , this variable was studied from the opposite perspective of poor performance. These studies present out-of-range effect sizes: r = 0.14 ( de Bruijn et al., 2018) and r = 0.137 ( Mulder et al., 2017 ). The study by de Bruijn et al. (2018) also present confidence intervals (95% CI) that contain zero, which annuls any statistical significance in the relationship. The same does not occur in the study by Mulder et al. (2017) ; however, it contains parameters below those established in this meta-analysis (an effect size of r = 0.365, with a 95% CI between 0.309 and 0.419 in the relationship between executive functions and academic performance). These studies were conducted in the Netherlands, and in all two there is a variability of the variance that points directly to language and to the distinctiveness of the design or sample. In one of them, works with two groups in which 25% of the sample corresponds to children with poor academic performance ( de Bruijn et al., 2018) . The last study has the distinct feature of being a longitudinal design that starts with 3 year olds ( Mulder et al., 2017 ). Despite this distinction, no differences were observed in the behavior of the executive function components in relation to language, regardless of the culture, the native language, or the educational system where the research was conducted.

Conclusions and Limitations

Although there are many publications on academic performance and the variables that influence it, the originality of this meta-analysis lies in its focus on the last decade and on primary education. In addition, this study considered and included a variety of samples and research studies conducted in different countries to provide a comprehensive overview of the topic. The publication review made it possible to verify the diversity of the variables related to academic performance, highlighting the executive functions. The primary education stage was the focus of only a small number of studies, compared to the pre-school or secondary education stages; the university stage was studied the most. The number of competency-based measures of academic performance in the studies increased with a corresponding decrease in the use of numerical grades (per quarter and subject), which is considered to be closer to a true measure of learning. There was an increased number of studies with structured models using first-level variables such as the executive functions and other variables deemed to be minor because they are influenced by the former (not because of their direct relationship but because of their moderating power); however, they are also essential for the development of certain competencies and capabilities.

An important finding is that it was possible to confirm that, in the last decade, executive functions have replaced the intelligence quotient as the most studied variable with respect to academic performance and that both currently have the same predictive capacity. The results of this review and meta-analysis support the recognition of the multifactorial composition of executive functions, and they reveal that working memory is the most researched component as well as a better predictor than inhibition. In addition, it is evident that the behavior of the executive function components depends on the subject studied, especially regarding the relationship between mathematics and the visuo-spatial aspect of working memory. Similarly, most of the executive components are better related to performance in mathematics than in language. Given the dilemma of classifying executive functions as a domain-general cognitive variable, the studies reviewed confirm that executive functions can be decomposed into different components (working memory, inhibition, cognitive flexibility and planning) that are distinctly linked to certain types of learning. Furthermore, the moderating role of executive functions was demonstrated with respect to other variables such as physical fitness, motor skills, or memory processes. Similarly, it is evident that the executive functions are an important predictor of academic performance and future learning problems at an early age. However, this variable diminishes in its predictive capacity during secondary education and more so during university-level education where its development cycle comes to an end. Deficiencies detected in the executive components affect levels of school performance, which in turn has a heavy influence on the subsequent development of people at all levels—training, employment, social life. There is another important finding that must be highlighted: the moderating effect of gender in the relationship between executive functions and academic performance. The explanation is found in the significant maturational development that occurs during the years of primary education. Due to physiological and neurological factors, girls mature more quickly than boys during this stage. The studies reinforce the descriptive and moderating nature of this variable with respect to the development of the various skills needed for acceptable learning in primary school, in addition to its link to the student's maturity.

Since our meta-analysis included studies from different continents, from different socioeconomic levels, and from different rural or urban areas, it indirectly addressed the impact that different educational systems can have on intellectual development. However, no significant differences were found that could have produced variability in the executive component resulting from the sociocultural and educational contexts of the samples. In this regard, the diverse measures of academic performance, expressed in the (mostly) nationwide standardized achievement test results and in the traditional numerical grades given by teachers for the various subjects, have not demonstrated any significant differences. All this indicates that culture, native languages, socioeconomic levels, and the various objective methods of assessing this variable do not affect its development nor the resulting statistical data.

With regard to the limitations of this study, its sampling and research design can be noted. The descriptive and correlational nature of the study meant that the only statistics included were those that directly related to the variables studied and that those that compared groups or established indirect relationships were excluded. If these are linked to the results of our systematic review and the conclusions reached, then future research should consider focusing on the specific nature of the executive functions, using as a reference the statistics from the various structured models as well as their connection to the development of specific capabilities and competencies. Moreover, once the importance of maturity on the development of executive functions has been proven, we suggest a study on the relationship between the two, focusing on gender and not exclusively on age. All this can contribute to the development of specific intervention plans for the executive function components and deficient capabilities that can guide efforts to improve the learning process for students. They can also contribute to furthering the understanding of the links between this variable and academic performance at an early age.

Author Contributions

AQ conducted the search, selection, and coding of the research articles. He also conducted the statistical analyses and drafted the initial draft of the manuscript. AC and NM reviewed the coding of the selected articles and reviewed and corrected the initial draft of the manuscript. AC did the coordination work and all three approved the final manuscript submitted.

The Department of Education of the University of Zaragoza ( Universidad de Zaragoza ) funded the translation and publication of this article.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

We would like to thank the University of Zaragoza ( Universidad de Zaragoza ) and specifically the Department of Education Sciences for the support they provided in making this study a reality.

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* ^ articles included in the meta-analysis.

Keywords: executive functions, academic performance, primary education, relationship, meta-analysis

Citation: Cortés Pascual A, Moyano Muñoz N and Quílez Robres A (2019) The Relationship Between Executive Functions and Academic Performance in Primary Education: Review and Meta-Analysis. Front. Psychol. 10:1582. doi: 10.3389/fpsyg.2019.01582

Received: 22 January 2019; Accepted: 24 June 2019; Published: 11 July 2019.

Reviewed by:

Copyright © 2019 Cortés Pascual, Moyano Muñoz and Quílez Robres. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Alejandra Cortés Pascual, alcortes@unizar.es ; Nieves Moyano Muñoz, nmoyano@unizar.es ; Alberto Quílez Robres, alkirova@msn.com

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• Published: 14 January 2021

The developmental trajectories of executive function from adolescence to old age

• Heather J. Ferguson 1   nAff3 ,
• Victoria E. A. Brunsdon 1 &
• Elisabeth E. F. Bradford 2  

Scientific Reports volume  11 , Article number:  1382 ( 2021 ) Cite this article

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• Cognitive ageing
• Learning and memory

Executive functions demonstrate variable developmental and aging profiles, with protracted development into early adulthood and declines in older age. However, relatively few studies have specifically included middle-aged adults in investigations of age-related differences in executive functions. This study explored the age-related differences in executive function from late childhood through to old age, allowing a more informed understanding of executive functions across the lifespan. Three hundred and fifty participants aged 10 to 86 years-old completed a battery of tasks assessing the specific roles of inhibitory control, working memory, cognitive flexibility, and planning. Results highlighted continued improvement in working memory capacity across adolescence and into young adulthood, followed by declines in both working memory and inhibitory control, beginning from as early as 30–40 years old and continuing into older age. Analyses of planning abilities showed continued improvement across adolescence and into young adulthood, followed by a decline in abilities across adulthood, with a small (positive) change in older age. Interestingly, a dissociation was found for cognitive flexibility; switch costs decreased, yet mixing costs increased across the lifespan. The results provide a description of the developmental differences in inhibitory control, working memory, cognitive flexibility and planning, above any effects of IQ or SES, and highlight the importance of including middle-aged adults in studies seeking to establish a more comprehensive picture of age-related differences in executive function.

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Introduction

Executive functions (EF) are high-level cognitive processes that include planning, initiation, shifting, monitoring, and inhibition of behaviours 1 . EFs play an important role in our everyday life, allowing us to focus attention on specific tasks, to engage in successful problem solving, and to plan for the future. EFs demonstrate variable developmental and aging profiles (e.g., 2 , 3 ), with protracted development into early adulthood and a decline into older age that is associated with structural and functional changes in the prefrontal cortex 4 , 5 , 6 , 7 , 8 , 9 , 10 . The majority of these studies have compared dichotomous young/old adult age groups, and few studies include middle-aged adults or adolescents in investigations of age-related changes in EF (c.f. 11 , 12 , 13 who included middle-aged adults). Therefore, many open questions remain about how development changes across the lifespan, and whether these effects are consistent across multiple components of EF. We address this by exploring how different components of EF develop and change across the lifespan, from late childhood through to old age. Specifically, we tested whether four key components of EF (inhibition, working memory, cognitive flexibility and planning) show parallel or distinct developmental trajectories, and aimed to describe any age-related changes in multiple EFs.

EFs begin to emerge early in infancy, with basic skills needed for EFs emerging before three years of age, and more specific skills developing into early childhood 14 . It has been suggested that each component of EF develops at its own rate across childhood and adolescence, reaching maturity at different ages (see 1 ). For instance, cognitive flexibility has been shown to emerge between the ages of 3 and 4 years old, becoming more complex between the ages of 7 and 9 years old, and reaching adult-like levels by age ~ 12 15 , 16 , 17 ; in contrast, Zelazo et al. 18 found that cognitive flexibility abilities continue to improve between the ages of 20 and 29 years old, suggesting prolonged development of these abilities into young adulthood, and highlighting the importance of using different approaches and tasks to assess EF abilities, providing further insight into when these abilities reach maturity. Working memory, inhibition, and planning have been shown to continue to develop throughout childhood and adolescence, and in some circumstances (e.g., task dependent), have also been shown to continue to develop into young adulthood (e.g., 19 , 20 , 21 , 22 , 23 ). The protracted development of EFs across childhood and adolescence is associated with neurological changes, particularly the development of the prefrontal cortex (e.g., 4 , 24 , 25 ). Given this, adolescence is a critical period to study, allowing further examination of the continued development of EFs beyond childhood and into early adulthood to establish when these components of EF reach maturity.

Cognitive performance peaks in young adulthood (e.g., 26 ), with declines emerging as early as 20 or 30 years old, including declines across adulthood in speed of processing 27 , 28 , 29 , 30 , reasoning 29 , 30 , face processing 31 , fluid intelligence 26 , 27 , crystallized intelligence 26 , 27 , working memory 26 , 28 , 32 , 33 , verbal and visuospatial memory 34 , and long-term memory 27 , 28 . There is a vast amount of heterogeneity in regards to when cognitive abilities peak and decline. For example, aspects of short-term memory decline from 18 years of age, working memory declines in the 30 s, and vocabulary peaks in the 40 s or even later 26 . In contrast, other aspects of cognition, such as autobiographical memory and semantic knowledge, remain relatively stable across adulthood 35 , 36 .

These findings raise the question of whether different components of EFs, specifically inhibition, working memory, cognitive flexibility, and planning, are stable across adulthood and decline in older age, or whether age-related declines in EFs begin soon after maturity in early adulthood. Studies have largely established that working memory reaches a peak at 30 years old and declines thereafter 26 , 32 , 33 , 37 . In addition, inhibitory control is poorest in younger children, improves in adulthood, and declines in older age 38 ; however middle-aged adults were omitted from this study, so it is not clear when these declines started to emerge. Overall, there is a paucity of research specifically focussing on multiple components of EF across the lifespan, with studies into aging often limited in their focus due to comparing dichotomous ‘young’ versus ‘old’ adult groups (i.e. few studies include adolescents or middle-age adults in their lifespan sample). This approach means that important evidence is scarce to draw conclusions on the extended developmental trajectory of EF or earlier signs of decline. A notable exception to this is the Cognitive Battery of assessments developed as part of the National Institutes Health Toolbox in the U.S.A (NIHTB-CB; 18 , 39 , 40 ). The NIHTB-CB sought to establish a series of tasks that could be used to assess cognitive function abilities across different populations of individuals, suitable for use in individuals aged from three to 85 years old, and includes measures of inhibitory control, cognitive flexibility, and working memory. Results from the NIHTB-CB support suggestions of an inverted-U-shaped curve in development of a number of EF abilities, including inhibition, cognitive flexibility, and working memory, with abilities first rising across childhood, and falling in later adulthood 18 , 41 , 42 . Ferriera et al. 43 also investigated EF abilities in a specific cohort of healthy middle-aged adults, with results highlighting very early declines in EF before the age of 50; other studies that have included middle-aged adults in a broader adult sample have reported a linear decline across adulthood which is steeper among participants aged 65 + (e.g., 13 ).

Further to these behavioural studies, neuroimaging has revealed changes in both the structure and function of brain regions that underlie EFs in middle-age and older adulthood 44 , 45 , which is highly likely to impact EF performance in these age ranges. The studies cited above have provided important insights into the developmental trajectories of EF capacities across the lifespan, including highlighting the limited studies that have included middle-aged adults in investigations of EFs and, importantly, included analysing age as a continuous measure to track development throughout adulthood (c.f. 11 , 12 , 13 , 46 ). More often, even when studies have included middle-age adults, they have analysed effects of age between groups rather than as a continuous predictor (e.g., 47 , 48 ), or rely on correlation or regression analyses to model only linear trends (e.g., 46 ). As illustrated, studies with middle-aged adults are essential to gain a comprehensive picture of the development of EFs throughout adulthood, to allow pinpointing of when declines in EFs first emerge, and whether the patterns of decline in early adulthood, as shown in other cognitive abilities, are also evident across the different components of EF across different paradigms, or whether they are limited to specific components. Conducting studies with a continuous age sample also provides vital insights to inform theories of healthy and abnormal aging, as, for example, the first pathophysiological changes can commence up to 20 years before a diagnosis of dementia 49 .

Older age is associated with significant declines in EF, including working memory (e.g., 50 ), inhibition (e.g., 51 ), planning (e.g., 52 ), and cognitive flexibility (e.g., 53 ). Additionally, different aspects of cognitive flexibility show distinct age-related effects. Mixing costs are greater in older adults (e.g., 54 , 55 , 56 , 57 , 58 ); however, there are mixed results in regard to switch costs, with some studies reporting an age-related increase (e.g., 59 ), a U-shaped trajectory 53 , or no age-related differences (e.g., 58 , 59 ), most likely due to differences in paradigms. Age-related effects in EFs are thought to be relatively robust, and have been associated with changes in the frontal lobes, specifically age-related volume reduction in the prefrontal cortex 60 . There are some conflicting findings in the literature regarding age-related declines in EF, perhaps because many studies do not account for general slowing in response latencies (see 61 , for a discussion). When accounting for this general slowing, Verhaeghen 61 failed to find evidence for specific age-related declines in inhibition and local task-shifting costs (termed switch costs herein), but found evidence for age-related declines in global task-shifting costs (termed mixing costs herein). Verhaeghen suggested that mixing costs reflect a dual-task cost, with dual-tasks affecting older adults more 62 . Thus, it is important for studies examining effects of cognitive decline in older age to account for age-related changes in response speed, to be sure that effects reflect true changes in executive capacities rather than more general slowing in response latencies.

In addition to age, several factors have been linked to cognitive decline, including genetics, health status, physical activity, socio-economic status (SES), IQ, and physical fitness (e.g., 63 , 64 , 65 , 66 , 67 , 68 ). Childhood SES has been consistently associated with EF 56 , 69 , 70 , 71 , with lower SES predicting poorer performance on tasks of EF in childhood 72 . Less is known about the link between adult SES and EF 73 . IQ is another factor that has been associated with EF, particularly with working memory 74 . IQ and EFs are dissociable yet related in childhood 75 , with evidence that inhibitory control and cognitive flexibility are related to IQ during childhood 76 . In adolescence, working memory is highly correlated with IQ, but inhibition and cognitive flexibility are not 54 . In older adults, IQ has been shown to be related to working memory, verbal fluency, inhibition, and cognitive flexibility 77 . Given that IQ and SES are related to EF abilities, the current study controlled for these factors in analysis, allowing us to assess the role of age in predicting differences in EFs, beyond effects of IQ and SES.

EFs play a critical role in everyday life, allowing individuals to plan ahead, focus their attention, and switch between different tasks. They play a key role in allowing individuals to maintain effective levels of independent functioning, and better EF abilities have been associated with improved self-reported quality of life in older age 1 , 78 . Further, deficits in EF abilities have been associated with issues with obesity 79 , social problems 80 , 81 , and lower levels of productivity 82 . It is therefore important to further our understanding of how these EF abilities continue to change and differ across the lifespan—contributing to our understanding of age-related cognitive changes—which ultimately may be able to provide insight into the optimum age at which cognitive training interventions could be utilized to help maintain real-world functioning across individuals.

The current study investigated how multiple components of EF differ across the lifespan, in a large, community-based sample of 350 10- to 86-year-olds, allowing differences across adolescence, early adulthood, middle adulthood, and older adulthood to be examined within one study. The study focussed on four components of EF: planning, inhibition (also termed inhibitory control and response inhibition), working memory, and cognitive flexibility (also called set shifting or mental flexibility). It is largely accepted that inhibitory control, working memory, and cognitive flexibility form the core components of EF abilities, reflecting largely (but not entirely) separable processes 83 . In the current study we also included a more complex aspect of EF, planning abilities. The ability to plan is a complex executive skill 94 , 102 that plays an important role in daily living, such as the ability to identify a goal and subsequently planning and executing the steps needed to attain that goal 72 , 83 . It is noted that planning abilities themselves, whilst considered an aspect of EF, may require activation of other EFs, including inhibitory control and working memory in order to produce successful outcomes 72 , 83 . Given this, the inclusion of a measure of planning abilities in the current study allowed further insight into how planning capacities may change across the lifespan, and whether we are able to establish a relationship between ‘core’ EF abilities and planning capacities within this lifespan sample.

The aim of this study was to explore the developmental trajectories of these four components of EF, to identify when age-related differences emerge. A cross-sectional design was utilized, to provide insight into differences that can be established across different age cohorts in task performance; importantly, to address our research question, we selected tasks that were appropriate for all participants from 10 to 86 years of age, allowing direct comparisons in task performance to be made across different ages. We used curvilinear regression modelling to establish the shape and trajectory of change across ages for each EF. Due to research suggesting that some components of EF may be related to IQ and SES, we also controlled for the effects of IQ and socio-economic status.

We predicted, firstly, that these components of EF would continue to develop throughout adolescence, indicated by an improvement in performance across tasks up to ~ 30 years of age. Second, we predicted that there would be age-related declines in EF from ~ 50 years of age onwards 43 . Third, we explored whether this decline in EFs would start earlier in adulthood (i.e. between 30 and 50 years of age). We did not stipulate specific predictions in this middle age range due to the dearth of research in adulthood. Instead, we modelled and tested the fit of linear, quadratic and cubic age relationships for each component of EF. Note that each statistical model can represent multiple patterns/directions of effects, however we define our predictions for the linear, quadratic and cubic fit models used here based on existing research on cognitive development and decline with age. We posited that a predicted linear age relationship would indicate either an improvement or decline in EF from adolescence to older age. We predicted that a quadratic age relationship would indicate a developmental improvement in EF in adolescence through to young adulthood, and a decline in EF throughout adulthood. A predicted cubic age relationship would indicate a developmental improvement in EF in adolescence through to young adulthood, a decline in EF across adulthood, and a further steeper decline in EF in older age. Finally, in line with previous research (e.g., 84 ) we predicted that the different aspects of cognitive flexibility would should show distinct effects: we predicted that switching costs (i.e., changing task sets) would not show any age-related changes, but mixing costs (i.e., maintaining multiple task sets) would show an increase across adulthood (e.g., 84 , 85 ).

Materials and method

Participants.

The sample consisted of 354 participants who were recruited from the community, via newspaper/radio adverts, social media, and an institutional research participation database, as part of the CogSoCoAGE project. Two participants were excluded due to low IQ (< 70), one participant was excluded due to being a non-native English speaker, and one participant’s data was lost due to computer failure. The final sample consisted of 350 participants (10–86 years-old; 232 females, 118 males). Table 1 provides a summary of the sample and Table 2 details the demographic characteristics of the CogSoCoAGE sample, each divided into five age groups for illustrative purposes. All participants were native English-speakers, had normal or corrected-to-normal vision, had no known neurological disorders, and had no mental health or autism spectrum disorder diagnoses. The Ethical Committee of the School of Psychology, University of Kent, approved the study, and all methods were carried out in accordance with EU guidelines and regulations. Informed consent was obtained from all participants; for participants under 18 years of age, consent was additionally sought from a parent or legal guardian.

Socio-economic status

Participants (if aged over 18) and parents of participants (if aged under 18) reported on their level of education, the household income, and their occupation (job title and industry). Occupational class was coded using the derivation tables provided by the Office for National Statistics 116 using the simplified National Statistics Socio-Economic Classification (NS-SEC) based on Standard Occupational Classification 2010 (SOC2010). To calculate an SES index, education level was coded on a scale 1–6, and household income and occupational class were coded on a scale 1–7. These three scores were summed to derive an SES index between 3 and 20 86 , with lower scores indicating lower SES. In our sample, scores ranged from 5 to 20.

Intellectual ability was assessed using the Wechsler Abbreviated Scale of Intelligence-Second Edition (WASI; 87 ). The WASI-II comprises of four subtests as a measure of intelligence for individuals aged 6–90 years old. The Vocabulary and Similarities subtests estimated a verbal IQ score. The Block Design and Matrix Reasoning subtests estimated a performance IQ score. Full-scale IQ comprised of both verbal and performance IQ.

Stroop colour-word task

A modified version of a standard Stroop Colour-Word task 88 was used as a measure of inhibition. The words were printed in red, green, blue, or yellow for all trials and were printed on a grey background. The words used in both congruent and incongruent trials were “RED”, “GREEN”, “BLUE”, and “YELLOW”. For congruent trials, the colour word matched the printed colour (i.e., “RED” printed in red). For incongruent trials, the colour word did not match the printed colour (i.e., “RED” printed in green). For filler trials, the non-colour words were matched for length and frequency to the colour words. The filler words used were “TAX”, “CHIEF”, “MEET”, and “PLENTY”. The word stimuli were presented in the middle of the screen in font type Courier New and font size 28. See Fig.  1 for example stimuli.

Illustrations of the stimuli and procedure employed in each of the four EF tasks: ( A ) Stroop colour-word task; ( B ) operation span; ( C ) task switching; ( D ) Tower of Hanoi.

Participants first completed 20 practice trials, which consisted of ten filler and ten congruent trials in a pseudo-randomised order. Participants were told that they would see a word and they were instructed to identify the colour of the word as fast as possible using a button-box (i.e., RED printed in green; participants press ‘green’ button). The experimental trials consisted of 50 congruent trials, 50 incongruent trials, and 50 filler trials presented in a pseudo-randomised order, in which the same colour word, the same printed colour, or the same colour word/printed colour could not appear on two consecutive trials to avoid priming effects. A blank screen appeared for 1000 ms at the start of the experimental trials. After the participant made a response, the next trial was started immediately.

Response times for filler, congruent and incongruent trials were calculated for accurate responses that were made 200 ms after stimuli onset and were within 2.5 SDs of each participant’s overall trial mean. The dependent variable was the Stroop congruency effect (incongruent trial mean RT minus congruent trial mean RT). In addition, we accounted for age-related slowing and declines in information processing speed, which led to positive skew and high kurtosis in reaction times, by log-transforming reaction times for each trial before calculating the Stroop congruency effect. The log-transformation of the Stroop congruency effect reduced skew and kurtosis (untransformed skew = 1.84, kurtosis = 8.84; log-transformed skew = 0.69, kurtosis = 3.44). The log-transformed Stroop congruency effects were reverse scored so that a higher score indicated better performance to aid interpretation of results alongside other measures. Internal consistency was excellent (Cronbach’s alpha = 0.99) and the average inter-item correlation was ideal (r = 0.53).

Operation span

This task was adapted from Unsworth et al.’s 89 automated operation span task (OSpan) as a measure of working memory, which was based on the original OSpan task by Turner and Engle 90 . Participants were required to solve maths equations while remembering a sequence of letters. The letters used were F, H, J, K, L, N, P, Q, R, S, T, and Y. See Fig.  1 for example stimuli.

There were three practice blocks. The first practice block was a simple letter span. A single letter appeared in the middle of the screen for 800 ms. A two-letter span was used for two trials, and a three-letter span was used for a further two trials. At recall, participants were required to recall the letter sequence in the correct order by clicking a box next to the appropriate letter presented in a 4 × 3 matrix. After clicking a box, a number appeared that represented the position of the letter in the sequence. A ‘blank’ box was also presented and participants were told to click this box if they could not remember the letter in the sequence. Participants could also click a ‘clear’ box to clear responses. The letters clicked also appeared at the bottom of the screen. To finish the letter recall stage, participants clicked a box labelled ‘enter’. This recall phase was untimed. After the recall phase, participants were given feedback about how many letters they recalled correctly.

The second practice block introduced the maths equations. A maths equation was presented on screen (e.g., (2 × 1) + 1 = 3) along with a ‘correct’ box and an ‘incorrect’ box. Participants were required to identify whether the maths equation was correct or incorrect by clicking the appropriate box. Accuracy feedback was given. There were three trials in this second practice block.

In the last practice block, participants completed both the maths section and letter recall section together. The maths equation was presented first, and once participants had responded to the problem, a letter to be recalled appeared in the middle of the screen for 800 ms. This equation-letter sequence was repeated twice to create a two-letter span in this final practice block. The letter recall screen with the 4 × 3 letter matrix was then presented. Participants completed three full practice trials, and were given feedback on how many letters they recalled correctly and how many errors they made on the maths problems.

The experimental trials consisted of three trials for each of 2 to 7 letter spans (randomised). This made a total of 18 trials with 81 maths problems and 81 letters. Participants were encouraged to keep their maths accuracy at or above 85% at all times. During recall, a percentage in red was presented in the upper right-hand corner of the screen, indicating the percentage accuracy for the maths problems.

An absolute OSpan score was calculated as the sum of all perfectly recalled sets. A partial OSpan score was also calculated as the total number of letters recalled in the correct position. The absolute and partial OSpan scores were highly correlated (r = 0.92, p  < 0.001) and due to the recommendations of Unsworth et al. 89 , the partial OSpan score was used as the dependent variable. Internal consistency was good (Cronbach’s alpha = 0.85) and the average inter-item correlation was ideal (r = 0.25).

Task switching

The task was adapted 91 , 92 as a measure of cognitive flexibility. Participants were presented with a 2 × 2 matrix on a computer screen. Stimuli were presented one-by-one in the four quadrants of the screen, beginning in the upper-left quadrant and rotating in a clockwise manner. The stimuli were coloured-shapes (circle/triangle, in blue/yellow) that appeared in the quadrant. See Fig.  1 for example stimuli. The same shape/colour combination did not appear on consecutive trials (i.e., a blue triangle could not appear in consecutive trials). Participants’ task was to decide whether the shape was a circle or a triangle, and whether the colour was blue or yellow, dependent on trial-type (see descriptions below). Participants used a button box to respond, pressing the left-hand button for circle/blue and the right-hand button for triangle/yellow. Participants were instructed to respond as fast and as accurately as possible. The next stimulus was presented 150 ms after a key press or after a timeout of 5000 ms. Participants received feedback about their accuracy after practice trials and repeated the practice block if their accuracy was less than 80%.

In the single-task, there were 16 practice trials and 32 experimental trials per block. Participants had to identify whether the shape was a circle or a triangle in one block, and whether the colour was blue or yellow in a second block (single-task trials).

In the mixed-task, there were 16 practice trials, and four blocks of 32 experimental trials. Participants had to indicate whether the shape was a circle or a triangle when the coloured-shape appeared in the top two quadrants, and whether the colour was blue or yellow if the coloured-shape appeared in the bottom two quadrants. Categorising the coloured-shape in the upper left to upper right quadrant, or in the lower right to lower left quadrant did not require switching to a new category (i.e., non-switch trials). However, categorising the coloured-shape in the upper right to lower right quadrant, or in the lower left to upper left quadrant required switching to a new category (from shape to colours, and vice versa, i.e., switch trials). Switch and non-switch trials alternated predictably within these blocks.

Response times were calculated for accurate responses that were made 200 ms after stimuli onset, and were within 2.5 SDs of each participant’s overall trial mean. A switch cost of task-set switching was calculated by subtracting the mean response time for non-switch trials from the mean response time for switch trials in the mixed-task. A mixing cost (indicating maintenance of two task-sets) was calculated by subtracting the mean single-task trial response time from the mean non-switch response time in the mixed-task. To account for age-related slowing and declines in information processing speed, trial level response times were log-transformed before calculating a switch cost and mixing cost. The log-transformation reduced skew and kurtosis for switch cost (untransformed skew = 0.47, kurtosis = 3.25; log-transformed skew = 0.17, kurtosis = 2.54) and mixing cost (untransformed skew = 0.91, kurtosis = 3.39; log-transformed skew = 0.41, kurtosis = 2.83). The log-transformed switch and mixing costs were reverse scored so that a higher score indicated better performance. Internal consistency was excellent for both the single and mixed-task (both Cronbach’s alpha = 0.98). The average inter-item correlation for the single-task (r = 0.49) and for the mixed-task (r = 0.34) was ideal.

Tower of Hanoi

The Tower of Hanoi was used as a measure of planning (based on script obtained from: https://step.talkbank.org/scripts-plus/TOHx.zip ). The Tower of Hanoi required the mouse-controlled movement of different-sized disks across three pegs from an initial state to a target state in a pre-defined number of steps. Participants were presented with three pegs (left, centre, right) and four disks; pink, yellow, blue and green, in increasing size. The target state was shown on the top-centre of the screen and was smaller than the initial state configuration. The initial state was presented on the bottom-centre of the screen. The number of steps remaining was shown in the centre of the screen. Participants were told that they needed to move the disks from their current positions on the bottom of the screen to match the target state in the given number of steps without placing larger disks on top of smaller disks. See Fig.  1 for example stimuli.

Participants first completed three practice trials: one one-step and two two-step problems. Participants continued to 16 experimental trials, which took three- to ten-steps to complete, with two trials at each step. Before the start of each trial, participants were told how many steps were required to complete each trial. During the trials, participants clicked on the disk that they wanted to move and this disk then turned red. The participant then clicked on the rod that they wanted to move the disk to. If the incorrect rod was selected, then an error message was shown and the participant restarted that trial. If the participant made five incorrect movements in a row then the task automatically ended. If the correct disk and rod were selected, then the selected disk moved to the selected rod and the participant moved on to the next step.

The dependent variable was an overall Tower of Hanoi score that used the traditional absolute scoring method, and was the sum of all perfectly completed trials (i.e., score of 5 for a trial with 5 steps completed perfectly with no errors). Internal consistency was acceptable (Cronbach’s alpha = 0.80) and the average inter-item correlation was ideal (r = 0.20).

Participants attended one or two visits to the university to complete the 5 h testing session, which included questionnaires on behaviour and demographic information, computer-based testing to assess cognitive and social skills, and an IQ assessment. The order of tasks was counterbalanced over 12 different lists to ensure that order effects were minimised. All tasks reported here were programmed using E-Prime software.

Analyses were conducted in R version 3.6.0. The datasets and code are available on the Open Science Framework ( https://osf.io/qzrwu ). Descriptive data on the EF measures are summarised in Table 3 , alongside the total number of participants retained per task. For the Stroop task, two participants did not complete the task due to equipment failure and one participant was colour-blind. For the Operation Span, one participant did not complete the task due to equipment failure, 3 participants did not return for their second testing session to complete the task, and 12 participants declined to complete the task or withdrew. For Task Switching, two participants did not complete the task due to equipment failure, 3 participants did not return for their second testing session to complete the task, 10 participants declined to complete the task or withdrew, and two participants’ data was lost due to computer error. For the Tower of Hanoi, two participants did not return for their second testing session to complete the task.

Age-related effects on executive function

A series of regression models were conducted to investigate the relationship between the measures of EF and age, over and above any potential effects of IQ and SES. The models specified the outcome variable as the dependent measure for the specific EF measure, the first predictor variable was age using linear, quadratic or cubic orthogonal polynomial coefficients, and IQ and SES index were included as the second and third predictor variables. Note that quadratic models included both linear and quadratic age coefficients, and cubic models included linear, quadratic and cubic age coefficients.

The best fitting model for each EF measure was deduced by comparing several goodness-of-fit indices shown in Table 4 . Established goodness-of-fit measures were used to evaluate model fit. The ANOVA test and likelihood test contrasted the simpler model against the more complex model (e.g., the model with linear vs. quadratic age coefficients). If the p value was greater than 0.05, then the simpler model was selected as the best fitting model. If the p value was less than 0.05, then the more complex model was selected as the best fitting model. Model comparison also used Akaike’s Information Criterion (AIC) and Bayesian Information Criteria (BIC), with increasingly negative values corresponding to increasingly better fitting models. Model selection evaluated these goodness-of-fit indices and the model (linear, quadratic, or cubic model) with the greatest number of goodness-of-fit indices was selected as the overall best fitting model (see Table 4 ). The model predictions for the overall best fitting models for each EF are plotted in Fig.  2 with the observed data. Analyses for the untransformed variables are reported in Supplementary Materials ( S1 , S2 ).

Relationship between age and executive function measures, adjusted for IQ and SES index. ( A ) log-transformed Stroop congruency effect, ( B ) OSpan partial score, ( C ) Tower of Hanoi score, ( D ) log-transformed Task Switching switch cost; and ( E ) log-transformed Task Switching mixing cost. The bold line indicates the best-fitting regression line and the dashed line indicates the 95% confidence intervals (CIs). Stroop congruency effect and Task Switching switch and mixing costs are reversed scored so that a higher value indicates better performance and all variables are z-scored for ease of comparison. Note : All measures were adjusted for IQ and SES to be comparable to the described regression models. To adjust for IQ and SES, the residuals were obtained from the regression line fit when fitting each executive function measure as a dependent variable in a linear model and IQ and SES index as predictor variables.

The best fitting model for the log-transformed congruency effect in the Stroop task included linear and quadratic age coefficients. The results of the model indicated that there was a significant association between the Stroop congruency effect and age, IQ, and SES (R 2  = 0.14, F (4, 335) = 13.46, p  < 0.001). Age was significantly associated with the Stroop congruency effect (linear β  = − 0.27, p  < 0.001; quadratic β  = − 0.28, p  < 0.001). To interpret the curvilinear relationship between the Stroop congruency effect and age, we consider the model predictions displayed in Fig.  2 A. Figure  2 A indicates that there is some increase in the Stroop congruency effect between 10 and 35 years of age (i.e., an improvement in inhibitory control) and a decrease in the Stroop congruency effect from 36 to 86 years of age (i.e., a decline in inhibitory control). IQ was also significantly associated with the Stroop congruency effect ( β  = 0.20, p  < 0.001), but SES was not ( β  = 0.09, p  = 0.106). The model remained significant when IQ and SES covariates were removed (quadratic R 2  = 0.09, F (2, 344) = 17.27, p  < 0.001), showing that age was significantly associated with the Stroop congruency effect in our sample (linear β  = − 0.19, p  < 0.001; quadratic β  = − 0.27, p  < 0.001).

The best fitting model for the OSpan partial score included linear and quadratic age coefficients. The results of the model indicated that there was a significant association between the OSpan partial score and age, IQ, and SES (R 2  = 0.28, F (4, 322) = 32.59, p  < 0.001). Age was significantly associated with the OSpan partial score (linear β  = − 0.48, p  < 0.001; quadratic β  = − 0.30, p  < 0.001). To interpret the curvilinear relationship between the OSpan partial score and age, we consider the model predictions displayed in Fig.  2 B. Figure  2 B indicates that there is some increase in the OSpan scores from 10 to 30 years of age (i.e., an improvement in working memory capacity), and a decrease from 30 onwards (i.e., a decline in working memory capacity). IQ was also significantly associated with the OSpan partial score ( β  = 0.44, p  < 0.001), but SES was not ( β  = 0.004, p  = 0.930). The model remained significant when IQ and SES covariates were removed (quadratic R 2  = 0.11, F (2, 331) = 20.78, p  < 0.001), showing that age was significantly associated with the OSpan partial score in our sample (linear β  = − 0.27, p  < 0.001; quadratic β  = − 0.22, p  < 0.001).

The best fitting model for the Task Switching switch cost included the linear age coefficient. The results of the model indicated that there was a significant association between the Task Switching switch cost and age, IQ, and SES (R 2  = 0.05, F (3, 323) = 5.19, p  = 0.002). Age was significantly associated with the log-transformed switch cost (linear β  = 0.20, p  < 0.001), indicating a decrease in switch cost from 10 to 86 years old (i.e., an improvement in cognitive flexibility in terms of ‘switch cost’; Fig.  2 D). IQ and SES were not significantly associated with switch cost (both p s > 0.134). The model remained significant when IQ and SES covariates were removed (linear R 2  = 0.04, F (1, 331) = 14.81, p  < 0.001), showing that age was significantly associated with the Task Switching switch cost in our sample (linear β  = 0.21, p  < 0.001).

The best fitting model for the Task Switching mixing cost included the linear age coefficient. The results of the model indicated that there was a significant association between the Task Switching mixing cost and age, IQ, and SES (R 2  = 0.07, F (3, 323) = 8.24, p  < 0.001). Age was significantly associated with the log-transformed switch cost (linear β  = − 0.26, p  < 0.001), indicating an increase in mixing cost from 10 to 86 years old (i.e., a decline in cognitive flexibility in terms of ‘mixing cost’; Fig.  2 E). IQ and SES were not significantly associated with switch cost (both p s > 0.103). The model remained significant when IQ and SES covariates were removed (linear R 2  = 0.07, F (1, 331) = 23.86, p  < 0.001), showing that age was significantly associated with the Task Switching mixing cost in our sample (linear ( β  = − 0.26, p  < 0.001).

The best fitting model for the Tower of Hanoi absolute score included linear, quadratic, and cubic age coefficients. The results of the model indicated that there was a significant association between the Tower of Hanoi absolute score and age, IQ, and SES (R 2  = 0.20, F (5, 336) = 17.00, p  < 0.001). Age was significantly associated with Tower of Hanoi absolute score (linear β  = − 0.15, p  = 0.004; quadratic β  = − 0.25, p  < 0.001; cubic β  = 0.21, p  < 0.001). To interpret the curvilinear relationship between the Tower of Hanoi absolute score and age, we consider the model predictions displayed in Fig.  2 C. Figure  2 C indicates that there is an initial increase in Tower of Hanoi absolute scores from 10 to 30 years of age (i.e., an increase in planning ability), a decrease from 30 to 70 years of age (i.e., a decrease in planning ability), and a small, but variable, increase from 70 years of age onwards. IQ was also significantly associated with the Tower of Hanoi absolute score ( β  = 0.43, p  < 0.001), but SES was not ( β  = 0.005, p  = 0.921). The model remained significant when IQ and SES covariates were removed (cubic R 2  = 0.05, F (3, 344) = 6.65, p  < 0.001), showing that age was significantly associated with the Tower of Hanoi absolute score in our sample (linear β  = − 0.41, p  = 0.002; quadratic β  = − 0.25, p  < 0.001; cubic β  = 0.26, p  < 0.001).

Relationships between measures of executive functions

A series of Pearson’s correlations were conducted between the four EF tasks to investigate the relationship between the measures of EF (Table 5 ). Partial correlations were also conducted to control for the effects of age. These effects of age for each EF measure were determined from the previously described regression models, i.e., the EF measures were adjusted for the linear, quadratic or cubic age effects. To adjust for age, the residuals were obtained from the regression line fit when fitting each EF measure as a dependent variable in a linear model and age coefficients (linear, quadratic, or cubic age coefficients) as predictor variables.

The OSpan partial score showed a positive correlation with both the Stroop congruency effect and the Tower of Hanoi score, with only a relationship with the Tower of Hanoi score remaining once accounting for the effects of age. These findings suggest that individuals with a higher working memory capacity also possess better planning ability, and these relationships are present irrespective of any age effects. Finally, Task Switching switch and mixing costs showed a negative correlation, reflecting that individuals with a greater switch cost also had a smaller mixing cost, and vice versa, and this pattern remained when accounting for the effect of age.

Comparing developmental trajectories of executive function

To examine whether each component of EF followed comparable or distinct developmental trajectories, we conducted across model comparisons for the age-related effects in the different EFs. This statistical method allows us to compare EF regression models with the same number of predictor variables, allowing direct comparisons between trajectories across these tasks. In our data, Task Switching switch and mixing cost models have three predictors (i.e., linear age coefficient, plus IQ and SES), Stroop congruency effect and OSpan partial score models have four predictors (i.e., linear and quadratic age coefficients, plus IQ and SES), and the Tower of Hanoi absolute score model has five predictors (i.e., linear, quadratic, and cubic age coefficients, plus IQ and SES). Therefore, Task Switching switch cost and mixing cost and Tower of Hanoi absolute score revealed different age-related effects (i.e., linear-only age effects vs. cubic age effect) and so were not directly compared with any other component of EF; analysis focused on the Stroop congruency effect versus OSpan partial scores.

Stroop congruency effect and OSpan partial score revealed similar curvature in the previous regression models (i.e., a quadratic effect of age), and so were directly compared. Two regression models were conducted and compared to statistically assess whether the age-related effects in the Stroop task and OSpan were significantly different. In the first step, a model was conducted that specified the outcome variable as the z-scores for the Stroop congruency effect and the OSpan partial score, with the predictor variable as the linear and quadratic age coefficients. In the second step, the same model was specified with the addition of an interaction term that included a grouping variable (i.e., a dummy variable) for the Stroop congruency effect (coded as 1) and the OSpan partial score (coded as 2). In the final step, these two models were compared using an ANOVA. If the p value was less than 0.05, then the regression slopes for the relationship between Stroop congruency effect and age versus OSpan partial score and age could be considered significantly different. If the p value was more than 0.05, then the regression slopes could be considered not statistically different.

The results indicated that the regression slopes for the Stroop congruency effect and OSpan partial score were not significantly different (RSSΔ = 2.96, F Δ = 1.11, p  = 0.344), suggesting that inhibitory control and working memory show similar developmental trajectories. As illustrated in Fig.  2 , the regression slopes for the other components of EF follow different patterns over age, indicating that only inhibitory control and working memory have similar developmental trajectories and all other components of EF show distinct developmental trajectories.

The current study explored age-related differences in EF from late childhood through to old age in a large, community-based sample. Three-hundred and fifty individuals aged 10 to 86-years-old completed tasks to measure inhibitory control, working memory, cognitive flexibility, and planning, to identify when age-related changes in these EFs first become apparent. After controlling for any potential effects of IQ and SES, analyses revealed that inhibitory control and working memory capacity was higher in young adulthood compared to adolescence, with inhibitory control showing a decline in participants from ~ 35-years-old, and working memory capacity showing a decline in participants from ~ 30-years-old. Planning ability was also higher in young adulthood compared to adolescence, but then declined across adulthood, with a small positive change in older age. In line with our hypothesis, a dissociation was found for the measures of cognitive flexibility: interestingly, however, this reflected that switch costs decreased across the lifespan, yet mixing costs increased across the lifespan.

These findings provide insight into the developmental trajectories of inhibitory control, working memory, cognitive flexibility, and planning ability across the lifespan, providing a more comprehensive picture of the age-related changes in EF than has previously been established. Many of the existing studies that have examined aging and EFs have compared a dichotomous sample of younger versus older adults (e.g., 51 , 93 , 94 , 95 ), have combined individuals into smaller age groups during analysis (e.g., 53 , 55 ), or have focused on single aspects of EF, such as inhibitory control (e.g., 19 , 23 ). Instead, in the current study, we used a continuous age sample to model curvilinear age relationships to show the development of EFs from adolescence through to older adulthood, and to highlight changes in EFs that emerge throughout adulthood and not specifically at the onset of old age (typically considered 65 years old plus). Studies have largely overlooked adulthood as a period of change, with many studies omitting middle-aged adults in their samples examining lifespan changes. Moreover, cognitive performance among adolescents has rarely been compared to middle- or older-aged adults. The current study therefore makes a unique contribution to the literature by demonstrating developmental changes in different EFs, using the same set of tasks for all participants, with evidence that declines emerge in inhibitory control, working memory, and planning as early as the third decade of life. In addition, inhibitory control and working memory follow comparable developmental trajectories, with distinct developmental trajectories apparent for the other measures of EF.

In line with our predictions, and supporting previous studies 61 , the current study highlighted that different aspects of cognitive flexibility showed distinct age effects. As expected, there was an increase in mixing costs across adulthood, but switch costs decreased across adulthood. Mixing costs have generally been found to be greater in older adults (e.g., 54 , 55 , 56 , 57 , 58 ) there are mixed results in regard to switch costs, with some studies reporting an age-related increase (e.g., 59 ), a U-shaped trajectory 53 or no age-related differences (e.g., 58 , 59 ), most likely due to differences in the task switching paradigms. We note that the current study used an alternating-runs paradigm without a preparatory cue-stimulus interval, which is analogous to Huff et al.’s 84 task-switching paradigm with comparable aging results. In addition, switch and mixing costs showed a negative correlation, reflecting that individuals with a greater switch cost also had a reduced mixing cost, and vice versa, and this pattern also remained when accounting for the effect of age. This finding replicates that seen in Huff et al. 84 in which a dissociation was found between switch and mixing costs across age groups. Huff et al. 84 suggested that this dissociation is due to differences in the attentional systems in younger versus older adults. They suggest that younger and middle-aged adults experience a larger switch cost as their attentional systems become tuned to the task set in the single-task, and this inertia to executing the same rule in the single-task slows the reconfiguration to respond to the switch trials in the mixed-task. Older adults experience a reduced switch cost as their attentional systems are less well tuned to the task set in the single-task, and so do not experience the same slow down to respond to switch trials in the mixed-task. Moreover, older adults experience a larger mixing cost due to the additional attentional demands of maintaining two task sets in the mixed-task as compared to a single task set in the single-task. In summary, results of the task switching paradigm demonstrate dissociations between switch and mixing costs across the lifespan, indicating that adolescents and younger adults have more difficulty switching between task sets, and middle-aged and older adults have more difficulty maintaining task sets.

In the current study, we utilized four widely used tasks to measure inhibitory control, working memory, cognitive flexibility, and planning as components of EF. We investigated the relationship between these measures and found that individuals with a higher working memory capacity also had better planning ability, and these relationships remained when accounting for the effects of age. This finding suggests a link between working memory capacity and planning ability, or alternatively it could suggest that some EF tasks purported to measure singular aspects of EF may also require other EF processes to complete. This is supported by prior literature which has suggested that ‘planning’ may be indicative of a more complex executive skill, requiring activation of other aspects of EF to be successful (e.g., 26 , 96 , 97 ). For instance, working memory may be required when utilizing planning abilities to allow thinking ahead and execution of steps to achieve a set goal 26 , 97 ; Hill and Bird 98 also suggest that the traditional tower tasks (as used here to assess planning abilities) may require working memory, the inhibition of prepotent responses, and the generation of problem-solving ideas.

Interestingly, there were no other relationships between the measures of EF. Other research has documented very weak relationships between EF tests and EF factors, leading to the conclusion that these are dissociable components of EF and providing support for the fractionated EF theory (e.g., 83 , 97 , 99 ). Studies that do report relationships between components of EF tend to use several different EF tasks to assess each component and use an SEM approach to fit and compare models. For instance, Miyake et al. 83 report in their study that, following completion of nine tasks used to assess shifting, updating, and inhibition, a three-factor model fitted the data best, highlighting distinguishable factors of: cognitive flexibility (shifting), updating, and inhibition. In the current study, we did not aim to assess whether EF is a unitary or diverging construct, and as such data is not optimised to investigate this specifically. However, the lack of correlations between tasks in the current data suggest that the tasks are tapping into distinguishable capacities rather than ‘umbrella’ EFs. Furthermore, EFs differentiate from middle to late childhood 100 . Our study is unique in exploring four separate measures of EFs (as opposed to an aggregated measure of cognitive performance), allowing across model comparisons which revealed that inhibitory control and working memory follow similar developmental trajectories, and all other measures of EF show distinct developmental trajectories.

In general, there is no single task or task battery that can exhaustively measure all aspects of EF, and tests of individual EF are rarely “process pure” 97 , 101 . Furthermore, there is some debate about whether tasks measure the underlying concept that they are purported to measure. For example, it has been suggested that participants may solve the Tower of Hanoi problems in a step-by-step manner instead of in a multi-step, planful manner 102 . It is also likely that the specific processes involved in each task differ across individuals and cohorts. For example, the method of administration used in the OSpan task here (i.e. requiring participants to select their answer from letters in a 4 × 3 grid) is likely to have differentially affected performance among the older participants since they are less familiar with computers and are known to experience age-related difficulties in visual search tasks and motor control (e.g., 103 , 104 ). In addition, it is noted that we used a single task to measure each component of EF. There may be specific aspects of each EF that may follow different developmental trajectories—for example, inhibitory control could be divided into automatic and effortful inhibition 105 . However, the aim of the current study was to examine how four separable EFs (inhibitory control, working memory, cognitive flexibility, and planning) may continue to change and differ across the lifespan, to further our understanding of age-related cognitive changes that may be present; to do this, we selected four well-established tasks that were suitable for use across the participant sample age-range, 10–86 years. This allowed direct comparison of task performance across different participant ages. It is noted, as previously recommended 106 , that in future studies it would be beneficial to include multiple measures of each component of EF to elucidate whether these age-related changes reflect the underlying EF or whether the age-related effects are task- or paradigm-specific. Furthermore, dual-tasks of EF may reveal greater age-related declines as multiple EFs are loaded in a single task; for example, loading working memory in younger adults has been found to reduce both inhibitory control and switching ability 107 . Tasks need to be sensitive enough to detect age-related declines 108 and should account for general cognitive processing 61 . The four EF tasks in the current study were found to be age-sensitive after adjusting for general cognitive declines in response latencies and for IQ and SES, and therefore suitably provide an overall lifespan description of EF. We note that our analyses did not factor in the influence of gender on EFs, though gender was unequally distributed across the age groups in our sample (e.g., 47% females among adolescents but 82% females among adults). Previous research has provided mixed evidence for gender or sex differences in executive functioning (e.g., 109 ), and these analyses were beyond the scope of the current paper, however it would be beneficial for future studies to systematically explore this influence further. Gender details in our sample are available alongside task data on the OSF repository.

Here, we describe the overall developmental trajectories of EFs. To increase confidence in findings relating to this main aim, we controlled for any effects of IQ and SES when exploring age effects on EFs, due to evidence suggesting that some components of EF may be related to IQ and SES 54 , 56 , 67 . For IQ measures, our results highlighted a relationship with inhibitory control, working memory, and planning ability, above the effects of age. This may also explain why, in our measure of planning ability, a small, variable, improvement in abilities is seen from 70 years old onwards. Notably, the older age participants who took part in this study had higher IQ scores (full scale, verbal, and performance) than any other age group included in analysis; participants in this study were community-based, and this higher IQ may reflect that those experiencing the optimum ‘healthy’ aging experience are more likely to agree to take part in research studies such as these. This provides insight into healthy aging processes and may indicate that IQ holds a protective role against age-related declines in EF, although further research aimed at directly examining this suggestion, particularly its role in predicting planning abilities, is required. It is interesting to note that in this study, SES was not related to any component of EF, above the effects of IQ and age. So far, no other study has examined current SES on adult EF; literature has instead used a longitudinal approach to examine how SES during a distinct period (typically childhood) predicts later EF (e.g., 110 ). The current findings therefore suggest that an individual’s SES can change over the lifespan, which may have an additional effect on cognition 111 , and that SES may be less critical for EF after childhood.

It is interesting to note that not every individual demonstrated the same developmental profile of EF; for example, some older adults show equivalent performance in tasks to younger adults. The current study used a cross-sectional design to identify when age-related differences emerge when examining performance on four key measures of EF abilities. Given the scope of this design, results can only assess group-level age-related changes. Cross-sectional studies are potentially confounded by cohort effects and might therefore overestimate age-related changes, potentially failing to accurately explore age-related changes in task performance at an individual-level (i.e., how an individual’s EF capacities change over time; see 112 ). For instance, prior studies using longitudinal analysis have highlighted that during middle age (i.e., 20–60 years), cognitive abilities such as speed of processing decline, but at a smaller rate than may be indicated in cross-sectional analyses (e.g., 113 ). The current study provides insight into the presence of age-related differences in EF abilities across the lifespan using a cross-sectional approach; it would be of interest in future to further this research by utilizing longitudinal designs to furthering our understanding of how EFs change with age, and individual differences that may influence these changes. It is also noted that the current sample consisted of a community sample of healthy adult volunteers functioning at high levels and may therefore, as discussed above, represent ‘successful’ aging within this particular population. There may be other factors that influence an individual’s performance on the EF tasks over and above age-related effects, which would be of interest to examine in future research; for example, there may be protective factors that offset declines in EFs, such as increased cardiovascular fitness in older age relating to better inhibitory control 114 .

As previously stated, EFs play an important role in daily life. Poor EFs can lead to social problems 80 , 81 , obesity and overeating 79 , 115 , lower productivity and difficulty keeping a job 82 , and people with better EF abilities have been shown to enjoy an improved quality of life 78 . Diamond 1 highlights the importance of EFs for maintenance of mental and physical health. Given this, it is important to further our understanding of how EF abilities continue to change and evolve across the lifespan, examining not only childhood/adolescence and older adulthood, but observing differences across all of adulthood. Furthering prior research that has sought to establish changes in EFs across the lifespan (e.g., 40 , 42 , 48 ; see also 41 ), the current study used four tasks to assess key EF abilities, including inhibitory control, working memory, cognitive flexibility, and planning abilities, providing further insight into cross-sectional changes seen in EF abilities across the lifespan. EF is a ‘functional construct’, involved in helping individuals conduct deliberate, goal-directed thoughts and actions 48 ; by examining which aspects of EF do or do not change across the lifespan, and which tasks are able to sensitively assess differences in EF abilities across different ages, we are able to gain information about the overall EF construct. The tasks used in the current study were shown to be suitable for use with individuals from ten to 86 years of age, sensitively detecting differences in EF abilities. Additionally, by identifying the ages at which changes in EFs are seen, we may be able to develop targeted interventions to help maintain efficient EF capacities, in turn assisting in increased success in real-world scenarios. By analysing the data in the current study as a continuous sample, allowing curvilinear relationships to be examined, results highlight changes in EF abilities can be observed from young adulthood, and emphasise the importance of looking at all ages when examining cognitive changes, rather than focussing on ‘younger’ versus ‘older’ age groups.

We explored developmental changes in inhibitory control, working memory, cognitive flexibility, and planning ability from 10 years old to 86 years old in a large, community-based sample of healthy individuals. We show that working memory capacity and planning ability continue to develop over adolescence and into early adulthood. Crucially, we show that declines emerge as early as the third decade of life in inhibitory control, working memory, and planning, which is much earlier than has previously been considered. In addition, we demonstrate a dissociation for measures of cognitive flexibility, with switch costs decreasing and mixing costs increasing up to older age, indicating that adolescents and young adults have difficulties switching tasks sets, whereas middle-aged and older adults have difficulties maintaining task sets. In general, studies have largely overlooked adulthood as a period of change in EFs, with studies focussing on their development in childhood, or comparing dichotomous groups of young versus older adults in studies of cognitive aging. The findings of the current study highlight the value of including adolescents and middle-aged adults to provide a comprehensive lifespan description of the distinct developmental trajectories of EFs.

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Acknowledgements

This work was carried out with the support of a European Research Council grant to HF (Ref: CogSoCoAGE; 636458). The datasets and code supporting this article are available on the Open Science Framework ( https://osf.io/qzrwu ).

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Heather J. Ferguson

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Heather J. Ferguson & Victoria E. A. Brunsdon

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Elisabeth E. F. Bradford

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H.F. conceived of the study, designed the study, won the funding, data analysis and interpretation, and revised the manuscript; V.B. contributed to study design, data collection, data analysis and interpretation, and drafting the manuscript; E.B. contributed to study design, data collection, and revising the manuscript. All authors gave final approval for publication.

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Ferguson, H.J., Brunsdon, V.E.A. & Bradford, E.E.F. The developmental trajectories of executive function from adolescence to old age. Sci Rep 11 , 1382 (2021). https://doi.org/10.1038/s41598-020-80866-1

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Effects of chronic physical exercise on executive functions and episodic memory in clinical and healthy older adult populations: a systematic review and meta-analysis protocol

• Soledad Ballesteros   ORCID: orcid.org/0000-0002-8391-9615 1 ,
• Michel Audifren 2 ,
• Andreea Badache 3 ,
• Vera Belkin 4 ,
• Christoforos D. Giannaki 5 ,
• Antonia Kaltsatou 6 ,
• Uros Marusic 7 ,
• Mohammad Mosaferi Ziaaldini 8 ,
• Manca Pescar 7 , 12 ,
• José M. Reales 9 ,
• Jennifer A. Rieker 10 ,
• Pinelopi S. Stavrinou 5 ,
• Juan Tortosa-Martinez 11 ,
• Claudia Voelcker-Rehage 4 &
• Yael Netz 13 , 14  

Systematic Reviews volume  13 , Article number:  98 ( 2024 ) Cite this article

Metrics details

Executive functions (EFs) and episodic memory are fundamental components of cognition that deteriorate with age and are crucial for independent living. While numerous reviews have explored the effect of exercise on these components in old age, these reviews screened and analyzed selected older adult populations, or specific exercise modes, thus providing only limited answers to the fundamental question on the effect of exercise on cognition in old age. This article describes the protocol for a systematic review and multilevel meta-analytic study aiming at evaluating the effectiveness of different types of chronic exercise in improving and/or maintaining EFs and long-term episodic memory in older adults.

Methods and analysis

The study protocol was written in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Several databases will be searched. Randomized controlled trials (RCTs) conducted in older adults aged ≥ 60 years providing any kind of planned, structured, and repetitive exercise interventions, and EFs and/or episodic memory measures as outcomes, published in English in peer-reviewed journals and doctoral dissertations will be included. Two independent reviewers will screen the selected articles, while a third reviewer will resolve possible conflicts. The Cochrane risk-of-bias tool will be used to assess the quality of the studies. Finally, data will be extracted from the selected articles, and the formal method of combining individual data from the selected studies will be applied using a random effect multilevel meta-analysis. The data analysis will be conducted with the metafor package in R.

Discussion and conclusion

This review will synthesize the existing evidence and pinpoint gaps existing in the literature on the effects of exercise on EFs and episodic memory in healthy and unhealthy older adults. Findings from this meta-analysis will help to design effective exercise interventions for older adults to improve and/or maintain EFs and episodic memory. Its results will be useful for many researchers and professionals working with older adults and their families.

Systematic review registration

PROSPERO CRD42022367111.

Peer Review reports

Introduction

Developed nations are experiencing unprecedented increases in the population of older adults mostly due to the reduced birth rates and the increased longevity of their citizens. The latest projections by the United Nations suggest that the global population could grow to around 8.5 billion in 2030, 9.7 billion in 2050, and 10.4 billion in 2100 [ 1 ]. More importantly, it was estimated that in the European Union, the old-age dependency will increase from 29.6% in 2016 to 51.2% in 2070 [ 2 ].

With respect to brain and cognition, aging is the main risk factor for neurodegeneration with prevalence increasing further with age [ 3 ]. Given the demographic situation and the relation of aging with cognitive decline, there is great interest in exploring effective ways to improve and/or maintain cognitive functions for independent living [ 4 ]. The main approaches to improving brain functionality and cognition in older adults are physical activity (PA), cognitive training, and social engagement [ 5 ]. The focus of this paper is PA.

Colcombe and Kramer [ 6 ] conducted two decades ago a seminal meta-analytic study on the effect of aerobic fitness on cognition in older adults. The study included 18 intervention studies and showed robust benefits in cognition with the largest fitness-induced benefits occurring for executive control processes, as previously hypothesized by Kramer, Hahn et al. [ 7 ]. The magnitude of the effect was moderated by the length of the training intervention, the length of the training sessions, the type of the intervention, aerobic training or aerobic combined with strength training with better results for combined training, and the gender of the participants with larger benefits for women.

The research conducted since then has provided compelling evidence that regular practice of PA can promote and/or maintain cognitive and brain functioning in late adulthood and old age [ 8 , 9 , 10 ]. The literature usually distinguishes between PA and exercise. The former entails any bodily movement produced by skeletal muscles that increases energy expenditure relative to rest. Exercise is a subcategory of PA that is planned, structured, and repetitive and is more specifically designed to improve one or more components of fitness: cardiorespiratory fitness, flexibility, balance, coordination, strength, and/or power [ 11 ].

The main objective of this review focuses on analyzing the effect of various exercise interventions, including aerobic exercise, strength straining, dance, and balance exercises on executive functions (EFs) and episodic memory of older adults. There is agreement among aging researchers that significant declines appear with age in EFs [ 8 , 12 ] and long-term episodic memory, related to intentional retrieval of episodes [ 13 , 14 ]; thus, several studies focus on these components [ 15 , 16 , 17 , 18 ]. EFs are formed by a series of effortful top-down cognitive processes necessary for mental and physical health, success in life, and cognitive, social, and psychological development [ 19 ]. The dorsolateral prefrontal cortex (DLPFC) plays a crucial role in the different components of EFs [ 20 ] and contributes to these components via functional connectivity with different brain regions [ 21 ].

Improvements in fitness are expected to improve EF processes such as coordination, inhibition, planning, and updating of working memory [ 7 ] but also cognitive flexibility as well as higher-order executive functions related to reasoning and fluid intelligence. Inhibitory control refers to the ability to control one’s attention and do what is more appropriate in each circumstance. Moreover, inhibitory control allows us to selectively attend to a certain stimulus suppressing other stimuli. Self-control is another aspect of inhibitory control related to resisting temptations and avoiding impulsivity. Inhibitory control declines greatly in normal aging [ 22 ], and older adults struggle to avoid distractions [ 23 ]. A recent cross-sectional study has showed that the EFs inhibition, shifting, updating, and dual tasking decline in healthy older adults but not with the same intensity with inhibition showing the greatest decline and dual tasking the smallest [ 24 ].

Working memory (WM), and more particularly updating of WM, is another key EF that serves to hold verbal or visual-spatial information in mind that is no longer perceptually present and working with it [ 25 ]. WM and inhibitory control are closely related and often support one another. The decline in WM with aging correlates with a decrease in the speed of information processing in older adults [ 26 , 27 ].

The third component of EFs, cognitive flexibility, builds on working memory and inhibitory control. Flexibility means to being able to adjust to changed demands and to change perspectives, task switching, and set shifting. Cognitive flexibility is a property of the cognitive system that helps us to pursue complex tasks [ 28 ]. An additional component of EFs is higher-order EFs which is related to reasoning, problem-solving, and planning and is synonymous with fluid intelligence [ 19 ].

Episodic memory is a key cognitive process that allows us to represent past experiences and employ these representations to serve current and future goals [ 29 , 30 ]. It is one of the earliest memory systems that decline with increasing aging. Impaired episodic memory with aging, involving retrieval of personal experiences and their spatial and temporal contexts, is well documented in the literature [ 31 ]. At the brain level, the medial temporal lobe and the hippocampus play a crucial role in retrieving information from episodic memory [ 32 ].

Since the influential meta-analytic study conducted by Colcombe and Kramer [ 6 ], the effect of exercise on EFs and episodic memory has been examined in numerus meta-analyses [ 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 ]. However, some reviews included only healthy populations [ 39 , 40 , 46 ], while others included only cognitively impaired or demented older adults [ 34 , 35 , 41 , 43 , 44 , 48 ]. Chen et al. [ 36 ] included both healthy and cognitively impaired older adults but not demented. While one review examined only nursing home residents [ 38 ], another review [ 45 ] included only community-dwelling older adults. On the other site, while one review [ 33 ] focused only on aerobic exercise, another review [ 37 ] centered merely on resistance training, yet a third one [ 42 ] focused on exergames.

The current study addresses the gaps of the existing literature and aims to extend the knowledge of the effect of exercise on the principal components of cognition in old age. Our comprehensive review will potentially include healthy and non-healthy older adults and a wide range of exercise modes. This argument stems from a gap in evidence-based literature as pointed in a recent article [ 49 ]. For example, it has been argued that research on older populations is typically biased towards healthy and relatively young older adults, with certain groups of older individuals frequently being excluded from research on aging — especially in studies with physical activity interventions [ 49 ]. Such a review will pose a general question on the effect of exercise on cognition in advanced age (a general effect size will be calculated) followed by examining the moderating effect of various exercise modes (e.g., aerobics, strength, balance), several exercise characteristics (e.g., intensity, frequency, length), and a wide range of population characteristics (e.g., education level, percentage of females, health status), protocol characteristics (e.g., type of control group, type of analysis — intention-to-treat vs. per-protocol), and exercise settings (community dwelling and nursing homes). In addition, the present review will make an in-depth examination of the moderating effect of the outcomes used to assess cognitive functions, distinguishing, for example, working memory span indexes (e.g., number of correct responses in reading span tasks) from updating working memory indexes (e.g., error rate in n-back tasks), the latter requiring much more executive control than the former. The choice of adequate indexes of EFs is a very sensitive problem when estimating the effect size of the influence of regular exercise on EFs.

To summarize, the main objective of this systematic review and meta-analysis is to address the gaps encountered in the existing literature and to investigate the advantages of a broad range of exercise interventions on two key cognitive components, EFs and long-term episodic memory, across diverse groups of older adults and considering very selective outcomes. The findings from this review will be instrumental in developing effective training methods to enhance EFs and episodic memory in healthy and unhealthy older adults.

The protocol of this review was prepared following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols (PRISMA-P) 2015 statement and Cochrane systematic review methodology [ 50 , 51 ]. The protocol is registered on the International Prospective Register of Systematic Reviews (number CRD42022367111).

Figure  1 presents the planned flow chart of the systematic review and meta-analysis with a summary of the selection process.

Flow chart diagram of the search strategy of the systematic review

Eligibility criteria

Eligibility criteria follows the PICO framework regarding population, intervention, comparator, outcome, and study type.

The study will include participants with a mean age of 60 years or older and a minimum age of 50 years. It will include both healthy older adults and older adults diagnosed with various conditions such as mild cognitive impairment (MCI), Alzheimer’s disease (AD), or Parkinson disease (PD).

Intervention

Any randomized controlled trial (RCT) focusing on the effects of any type of exercise will be screened for inclusion, including aerobic, resistance training, coordination training, and other exercise programs such as tai chi, qigong, dancing, and exergaming. Several main exercise characteristics (type, session duration, session intensity, session frequency, intervention duration) will be assessed.

Comparators

Comparators will include light exercise, stretching, meditation, relaxation, and/or passive control groups (waiting list, treatment as usual, and habitual activities).

Cognitive outcomes will include objectively assessed cognitive domains of EFs (inhibition, working memory, cognitive flexibility, and high-level EFs) and episodic memory. These cognitive domains should have been assessed at baseline and at the end of the intervention through well-validated cognitive tasks and psychological tests. Regarding EFs, the outcomes for assessing inhibitory control may include the Stroop task, Eriksen-Flanker test, Simon task, global–local task, go-no go task, random number generation task, saccade-antisaccade task, and stop-signal task (STT). To assess working memory, the tasks and tests may include the Corsi block-tapping test, reading span task (RST), operation span task (OSpan), backward verbal digit span task, visuospatial N-back task, or tone monitoring task. To assess cognitive flexibility, the instruments may include the Trial Making Test (TMT), the Alternative Uses Task (AUT), Brixton Spatial Anticipation Test (BSAT), Delis-Kaplan Executive Function System (D-KEFS, subtests: the Trail Making Test, the Color-Word Test, the Tower Test, the design fluency test, The Sorting Test), Remote Associates Test (RAT), Implicit Relational Assessment Procedure (IRAP), attentional set-shifting task (AST), or Wisconsin Card Sorting Test (WCST). Among the instruments to assess high-level executive functions are the Raven’s Colored Progressive Matrices (RCPM) and Tower of Hanoi (TOH). In the case of episodic memory, the assessment tools may include the Rey Auditory Verbal Learning Test (RAVLT), SEMantic Episodic Memory Test (SEMEP), Wechsler Memory Scale (WMS; only the subtests that assess episodic memory), Hopkins Verbal Learning Test (HVLR-R), language-based paradigms, or the 360° video for episodic memory assessment. All the indexes of performance used for each of these tasks will be carefully selected to be sure that they reflect the targeted cognitive function (e.g., interference score in the case of the Stroop task, the Ericksen task, and the Simon task).

Inclusion and exclusion criteria

The inclusion criteria will be age (mean ≥ 60 with a minimum of 50 years), the practice of any type of physical exercise for at least 3 months, and provide outcomes including any EFs or episodic memory measure assessed at baseline (before physical training) and after training (post-training). If there were enough follow-up studies (e.g., 3 months, 6 months after training), they will be analyzed. Characteristics of exercise intervention such as frequency, intensity, type, and/or time of exercise (FITT) of the intervention program will have to be informed. Studies will be excluded if they do not meet the PICO conditions mentioned above, if they are not RCTs, if they do not have at least an active or passive control group, or if the written language is not English.

Research questions

The present study is directed to answer six main research questions described below.

To what extent does exercise enhance EFs and episodic memory in old age (the global effect)?

Do different types of chronic exercise (aerobic, resistance training, coordination training, and other exercise programs, such as Tai Chi or Qigong, dancing, or exergaming) have a different impact on EFs and episodic memory in older adults?

Is the type of control group (active vs. passive) a moderator of the changes in the investigated cognitive domains?

Is the effect of exercise different in healthy older adults and clinical older adults suffering neuropsychological disorders (MCI, PD, AD)?

Is age a moderator of the effect of exercise on the investigated components of cognition?

Are duration and intensity of exercise moderators of the effect of regular exercise on cognitive aging?

Literature search strategy

An initial search will be conducted at MEDLINE, Embase, PsychINFO, Google Scholar, EBSCO, SportDiscuss, CINAHL, Science Direct Dissertations, Web of Science, and Cochrane Central Registered of Controlled Trials (CENTRAL). These databases were selected because they are the most important and widely used to assure that relevant articles were not missed and in consultation with experienced researchers and librarians. Table 1 shows the detailed search strategy for PsychINFO. In addition, systematic reviews and meta-analyses published on episodic memory and the different EFs processes will be screened to check if the articles included in these publications should be considered in the present review.

Inclusion will be restricted to articles written in English published in peer-reviewed journals and doctoral theses. Studies published in other language will not be included. English is the most widely used scientific language to publish intervention studies and the language used in most systematic reviews and meta-analytic studies. Articles published from the inception will be considered for inclusion. An additional final search in the different databases will be conducted at the end of the review process to include more recently published studies.

After carefully reading all the retrieved articles, the data will be extracted for conducting the meta-analyses.

Data extraction

Once the databases are searched, the retrieved articles will be exported in a Research Information Systems (RIS) format and imported into Rayyan [ 52 ], a web application created for article screening. The first step in Rayyan will consist of removing all the duplicates. Then, pairs of reviewers will work independently and blinded on screening articles based on title and abstract. Possible conflicts between the two independent reviewers will be solved by a third reviewer (J. M. R.). After completing the first selection stage by title and abstract, the next step will be retrieving the full articles corresponding to the included articles for careful reading. The idea is to extract in an Excel spreadsheet all the relevant information. The extracted data will include the following: (i) Characteristics: information regarding author(s), journal, publication year, and country; (ii) population: number of participants in each group, participants’ characteristics including mean age, sex, and clinical condition; (iii) interventions: including type of physical activity, intensity, session duration, total duration of the intervention, and adherence; and (iv) outcomes: in terms of tasks and psychological instruments used to assess memory and EFs, including sample size, means, and standard deviations at baseline and post-intervention and other possible time points corresponding (follow-up assessments) to the different (intervention and control) groups.

If a study will be relevant for our analysis but the data necessary to calculate the effect sizes will be missing or just the graphs were available, we will contact the corresponding author by email to ask for the relevant data. If the author does not respond, the missing data will be extracted from the graphs provided in the article using the online tool WebPlotDigitizer version 4.3.

In the case of RCTs with several time points, we will focus on the post-intervention at the end of the physical exercise training. If more time points or follow-up assessments were provided and enough articles contained assessments at 3 or 6 months after the end of the intervention program, the effects will also be considered. We will calculate Hedges’s g as the effect size.

Risk of bias

The risk of bias (RoB) of each included study will be evaluated using the Cochrane ROB 2 tool [ 50 , 53 , 54 ]. Biases are assessed across five areas including randomization, deviations from intended interventions, missing outcome data, outcome measurement, and selection of the reported results. The risk of bias of each study will be assessed based on a series of questions provided for each of the five areas and the possible answers in the following five categories: “yes,” “probably yes,” “no,” “probably no,” and “no information.” Finally, the risk of bias in each area will be assessed as “low risk of bias,” “some concerns,” or “high risk of bias.” Teams of two reviewers will independently assess the risk of bias in the included studies. A third independent reviewer will resolve possible disagreements.

Statistical analysis

Effect sizes (ES) will be modelled using a three-level structure because it is a better approach than a two-level structure when there are several dependent effect sizes in each independent study but only if the heterogeneity of the sampling variance is substantial. In three-level meta-analytic models, three different sources of variance are modelled. The third level represents the variance of effect sizes between studies; the second level describes the variance of effect sizes of the experiments, or measurements nested within each study; and the first level describes the sample variance. In the present study, we will perform a multilevel random-effects analysis using restricted maximum likelihood estimation. This analytical solution was designed to account for the nonindependence among effect sizes. This is the preferred methodology when the sampling variability is not too high. Heterogeneity among effect sizes ( I 2 ) will be assessed using the omnibus homogeneity test (Q), 0–40% indicates negligible heterogeneity, 30–60% indicates moderate heterogeneity, and 50–90% suggests substantial heterogeneity. A large Q -value means that differences between effect sizes do not derive from a common population mean from the study samples but are accounted for by other reasons.

The statistical analysis will be performed using rma.mv function of the metaphor package (version 2.4) [ 55 ] within the R software environment (version 4.0.1; R Core Team, 2021) [ 56 ]. The analytical steps provided by Assink and Wibbelink [ 57 ] will be followed. Dot-plot figures will be depicted using Mathematica (version 10.4) with software developed specifically for the present study.

To avoid outliers or influential cases that could distort the results of the meta-analysis, outlier and influential case diagnostics will be performed using the influence function of the metaphor package. The influence function calculates the influence of deleting one case at a time on the model fit or the fitted/residual values. Statistical heterogeneity will be assessed using the I 2 test.

After a systematic publication search, it might occur that some studies were missed due to publication bias. That is, intervention studies that did not obtain significant results are not published, either because the authors did not submit them to a journal for publication or because the editor rejected them. We will address this important issue using two complementary statistics. The first explores the relationship between the precision and the observed effect size of the studies (the funnel plot and the statistical test of its asymmetry known as Egger’s regression test) under the assumption that effect sizes drive publication bias. In a funnel plot, the effect sizes are plotted against the standard error. An asymmetric funnel plot would suggest that publication bias exists, for example, an underrepresentation of nonsignificant results and/or negative effects on the bottom left side of the funnel plot. To evaluate the statistical significance of the funnel plots, we will apply the Egger’s test [ 58 ]. This test analyzes in a linear regression whether the standardized effect sizes can predict study precision, defined as the inverse of the standard error. The main goal of this analysis is to find a significant regression intercept that differs significantly from zero which would indicate a significant funnel plot asymmetry. We will also use the trim-and-fill method [ 59 , 60 ] to determine the number of effect sizes that would need to be imputed to restore the symmetry of the funnel plot.

The second statistics we are going to use to assess publication bias is the P-curve technique, which assumes that publication bias is driven primarily through p -values, not by effect sizes. This relatively new methodology is based on the shape of the histogram of p -values, which depends on the sample sizes of studies and the actual effect size of the data. The method determines if the data estimates an actual, non-spurious effect size.

Once we had all the required information regarding the types of interventions, comparators, outcomes, and the healthy or clinical conditions of the participants of the finally included studies, we would be able to provide information regarding search results, descriptive results corresponding to studies and participants’ characteristics, overall effect size, and moderator analyses.

The demographic data suggest that the world is aging very rapidly, and it is necessary to take actions against the cognitive decline that comes with aging. EFs and episodic memory are fundamental components of cognition that deteriorate with age and are vital for independent living. These cognitive declines significantly impact the performance of activities of daily living, independent living, and well-being among older adults. Previous reviews and meta-analyses screened and analyzed certain older adult populations [ 39 , 46 , 34 , 41 , 48 ], or specific type of exercise [ 33 , 37 , 42 ], providing limited answer to the question on the effect of exercise on EFs and episodic memory of older adults. The novelty of the present review is that it extends the knowledge about the effects of exercise on specific and central aspects of cognition to include different exercise modes and both healthy and unhealthy older adults.

Considering the key procedures and analyses, this systematic and meta-analytic review follows the PRISMA-P 2015 statement and the Cochrane systematic review methodology [ 50 , 51 ]. The eligibility criteria of the articles to be included follows the PICO framework (population, intervention, comparator, and outcomes). Articles that met the inclusion criteria will be carefully read by pairs of reviewers who will extract the data for conducting the meta-analysis. Hedges’s g will be calculated as the effect size. Risk of bias of the included studies will be assessed with the Cochrane ROB 2 tool [ 50 , 53 , 54 ] by pairs of reviewers.

If the heterogeneity of the sampling variance is substantial, effect sizes (ES) will be modelled using a three-level structure. This approach is superior than a two-level structure. In a three-level structure, the third level corresponds to the variance of effect sizes between studies, while the second level refers to the effect sizes of the experiments within each study. Finally, the first level describes the sample variance.

The statistical analysis will be conducted using rma.mv function of the metaphor package (version 2.4) within the R software environment (version 4.0.1; R Core Team 2021), following the analytical steps of Assink and Wibbelink [ 57 ]. A specific software developed for the present study will be used to depict dot-plot figures. We will address possible publication bias using two complementary statistics, the funnel plot and the Egger’s regression test. The trim-and-fill method [ 59 , 60 ] will reveal the number of effect sizes necessary to be imputed to restore the symmetry of the funnel plot.

The fact that this review includes only articles written in English may be a limitation. However, clearly, most studies are reported in English, and it is expected to extract very comprehensive information.

The central research question of this study is whether all training components recommended by official bodies are efficient for enhancing EFs and episodic memory and whether moderators, such as exercise program types and participants’ characteristics, could influence the effect size of the effect of regular exercise on cognitive aging [ 46 , 61 ].

This systematic review and multilevel meta-analysis will provide evidence on how to optimize physical activity programs to improve and/or maintain these cognitive functions that decline more with age. So, the results of the present study would contribute to identify the gaps and limitations of current physical exercise research on executive functions and episodic memory in older adults. It would also allow to understand the quality of the research conducted to date in this field and summarize its main findings. The findings of this study will be useful for clinicians, physical trainer specialists, psychologists, social workers, and gerontologists, as well as older adults, their families, and wider public.

Ethics and dissemination

This systematic review and meta-analytic study do not require approval from an ethics committee. The results will be disseminated in peer-reviewed journals and at international conferences and scientific meetings.

Abbreviations

Confidence intervals

• Executive functions
• Episodic memory

Frequency, intensity, type, and time of exercise

Mean difference

Physical activity

Standardized mean difference

Working memory

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This article is the result of work from COST Action CA20104 — network on evidence-based physical activity in old age (PhysAgeNet), supported by COST (European Cooperation in Science and Technology). https://www.cost.eu , https://physagenet.eu/ . The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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SB, YN, MA, and CV-R conceptualized the design. SB wrote the original draft, and YN, MA, and CV-R reviewed and edited the draft. Funding acquisition, supervision, validation, and visualization were done by YN and SB. JMR and MA dealt with the methodology issues. All authors have taken part in initiating the idea, deciding the exercise types, the search terms, the specific measures of the outcomes, and the inclusion/exclusion criteria. All authors read and approved the final version of the manuscript.

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Ballesteros, S., Audifren, M., Badache, A. et al. Effects of chronic physical exercise on executive functions and episodic memory in clinical and healthy older adult populations: a systematic review and meta-analysis protocol. Syst Rev 13 , 98 (2024). https://doi.org/10.1186/s13643-024-02517-0

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Theses and Dissertations

Examining the moderating role of executive functioning on flooding and intimate partner violence.

Gabriella Damewood , Saint John's University, Jamaica New York

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Tamara Del Vecchio

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Robin L. Wellington

Intimate partner violence (IPV) is highly prevalent, so much so that it has been described as a global public health crisis. Therefore, it is important to elucidate what conditions increase risk for IPV to better understand its etiology. Research emphasizing dyadic and self-regulatory processes may shed light on what differentiates those who perpetrate IPV. Specifically, both emotional flooding and executive functioning (EF) deficits have been implicated with IPV, but it is unclear how these variables may interact in predicting dating aggression. The current study explored how emotional flooding may differentially amplify risk for IPV under varying levels of executive functioning (comprised of inhibition, cognitive flexibility, and working memory). A total of 105 participants completed task measures of EF and self-report questionnaires on flooding, physical, and psychological aggression. Results found that flooding was significantly associated with psychological, but not physical aggression. EF was not associated with physical or psychological aggression. Moderation analyses were nonsignificant, and implications of null findings are discussed.

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Damewood, Gabriella, "EXAMINING THE MODERATING ROLE OF EXECUTIVE FUNCTIONING ON FLOODING AND INTIMATE PARTNER VIOLENCE" (2023). Theses and Dissertations . 647. https://scholar.stjohns.edu/theses_dissertations/647

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10 Hacks to Boost Teen’s Executive Function Skills and Manage Screen Time

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Teens’ focus is interrupted, on average, every 90 seconds. Something as simple as an audible notification can draw focus away from a task. And when humans are distracted, it takes 23 minutes to get back to that previous level of focus. In schools, that means that in a 55-minute class period, multiple distractions across the classroom create an almost impossible task of staying on topic and focused. “When you toggle between two things, you lose cognitive energy and it takes a lot longer to get into deep focus,” said school psychologist Rebecca Branstetter . Teens “don’t realize that multitasking is neurologically impossible.”

Branstetter recently spoke at the Learning and The Brain: Teaching Engaged Brains conference in San Francisco, where she cited the above statistics from the book Stolen Focus by Johann Hari. When Branstetter asked about challenges with screens in the classroom, the audience of teachers shouted out familiar student behaviors, including: 

• playing games during a lesson,
• denying their phone was out when it was visible and
• showing up tired from scrolling all night long.

These distractions aren’t only frustrating for educators, research shows they reduce cognitive efficiency. Because social media is designed to keep users engaged for long periods of time, and kids and teens are still learning executive function skills , it’s important for parents and teachers to set boundaries and serve as tech mentors, she said. “Willpower alone is not enough. You have to require that environment to set the stage for how to help kids prioritize and focus.” In her talk and a follow-up interview with MindShift, Branstetter offered 10 tips and hacks to help boost teen’s executive function skills and manage screen time. 

1. See tech as a tool

Technology is like a hammer, said Branstetter. “It’s a tool, and you can use it to create beautiful things and you can create to destroy things. It depends on how you use it.” Adults can help to empower kids to see tech as a tool by encouraging them to find an app or tech tool that will address a specific challenge they are facing. If a teen is dealing with anxiety, for example, they can test out a few meditation apps and report back to the adult.

Branstetter also pointed out that there are apps that block the most searched websites on a device for a period of time, which can be useful for a student having a hard time focusing on tasks for extended periods of time.

2. Coach through task initiation

Task initiation is one of the big executive function skills that are interrupted by technology and cell phone use, according to Branstetter. Adults might assume that stopping a previous task is an obvious precursor to initiating a new task, but kids and teens might need more explicit instruction to develop that sequencing habit. This can look like asking students what needs to be done in order to start a specific task. Students might suggest that phones need to go away and that they need to pull out necessary materials to perform the new task at hand. According to Branstetter, this is an important practice in self-awareness. 

3. Probe for the feelings behind phone distractions

Impulse control is another executive function skill that teens are developing. If a student is having trouble refraining from looking at their phone when initiating a new task, it can help to encourage quick mindful reflection. An adult can ask a teen, “What is it that’s making you go on your phone?” and suggest some feelings like anxiety or boredom that they might identify with. Then the adult and teen can create a quick plan for stopping phone use at that moment and refocusing on the more immediate task.

4. Try the scrunchie trick or airplane mode

Putting a scrunchie over the front camera prevents smartphone facial recognition from effortlessly unlocking aphone. Branstetter recommended guiding teens to use that moment when the phone doesn’t unlock for a mental check-in: “Why am I checking this? How do I feel?” If the scrunchie method doesn’t work, Branstetter suggested teaching teens to use airplane mode during a time when phone distractions are unwelcome.

5. Take advantage of A.I.

There are also some useful A.I. tools for teens who might struggle with task breakdown and completion. Branstetter recommended Goblin Tools , which takes a prompt like “I have to write a five-page paper on Mesopotamia,” and creates a checklist with the steps that a student might need to do to complete the assignment.

6. Use a focus timer

The Pomodoro technique , which uses 25-minute bursts of focused time with breaks in between, has been a useful tool for the teens that Branstetter works with. She also recommended Forest , which can be downloaded as a smartphone app or used as a Chrome extension. Forest helps users track their focus time with a visual reminder of focus as a tree slowly grows on the screen, as well as real-world incentive. When a user completes a certain amount of focus time, without distraction, a real tree is planted through Forest app’s partner, Trees For The Future.

7. Create a tech contract

Tech agreements or contracts, allow teachers or parents to collaborate with young people on expectations for technology. One aspect of a tech agreement can be determining where the technology “hot spots” and “cold spots” are in the classroom or home. By predetermining where technology is expected to be used or not to be used, students have a better chance at applying their learned executive functioning and anticipatory thinking skills. Tech agreements can be revisited and adjusted as often as needed, said Branstetter.

8. Keep a technology diary

Another exercise that parents and teachers might find useful when it comes to making teens aware of their own habits, is to have them create a log of their daily activities, said Branstetter. For example, students can write a timeline of their day and determine how much time is spent outside, doing physical activity, socializing, having fun, focusing, and downtime without technology. By having kids take the time to reflect on their own data and see how much time is spent during their day doing certain activities, the unbalanced moments become very apparent, said Branstetter.

9. Encourage future thinking

Future planning is also a learned executive function skill . “Because motivation is the ability to see a positive emotion of the future … we need to help kids do a future sketch,” said Branstetter. Helping students visualize what it might look like and feel like in the future to complete a task will help them with anticipatory thinking.

Branstetter likes doing a future sketch that she calls a “movie in your mind.” For example, if a teacher notices a student on their phone when they should be completing a math task, they might say something like this: “Here’s the movie that is playing in my mind right now. You have your phone out and there’s a no-phone policy, so I’m supposed to take it from you, and that’s how the movie ends, with me taking it.” The teacher then prompts the student to narrate how an episode might play out if they finish their math task versus if they don’t finish their math task. The teacher can then simply ask, “which one feels better to you?” leaving the anticipatory thinking to the student.

10. Reinforce positive behaviors

Branstetter has also seen success in positive reinforcement from adults when it’s specific and sincere. She said praise is best paired with corrective feedback in a 5:1 ratio. But with teenagers, praise is not often received as well if it’s made publicly, so try to offer both praise and corrective feedback in quieter, more private settings. When it comes to regulating screen time in the classroom, praise can be as simple as saying to a student, “I haven’t seen you with your phone all day in my class,” Branstetter suggested in her conference session.