In some ways, human memory and computer memory are similar. For example, some general characteristics of human short-term memory resemble those of a computer�s random access memory (RAM). As discussed elsewhere on this site, human short-term memory is volatile and has a limited capacity. Computer RAM has essentially the same characteristics. Your computer often does not have enough memory to run certain programs, and when you turn it off, bye-bye data!
Your long-term memory is something like a computer�s hard drive. Both of them take longer to respond, but can store a considerable quantity of data.
But this latter analogy falls apart when you compare the ways that a computer and your own brain store information. Once pieces of information are recorded on a computer�s hard drive, they will not change one bit over the years. But your own memories are totally different. Over the years, they will be continuously altered and reconstructed in response to changes in your moods or fleeting states of mind.
Another difference is that on a hard drive, each piece of information is saved in a specific location, even though some files may be fragmented into several parts when they are first stored. In contrast, although any one of your memories certainly involves the activity of specific neurons, you can retrieve it by activating just a portion of the network of neurons where it was encoded. Likewise, any given neuron can help to encode many different memories by participating in many different neural networks.
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 Currently shopping forChoose a different computer. Crucial Blog How does the human brain compare to a computer?Posted on august 28, 2019 | updated april 20, 2020 by kris sharma in technology. We live in a world where computers can outperform humans at chess, Go, and even Jeopardy. Artificial intelligence and machine learning are creating new breakthroughs all the time, leaving us wondering whether we’ll soon be living in a technological utopia or battling for survival against a cyborg Arnold Schwarzenegger . But do computers outperform the human brain overall? Let’s find out. For the purpose of this article, let’s define a computer as a personal desktop for non-professional use (i.e. not a server running 24/7). And to keep things simple, we’ll limit the comparisons to four areas:
Let the battle begin! For day-to-day usage, most computer users will get by with 500GB of storage. Creatives, gamers, and other data-heavy users will often rely on additional storage on the cloud or on a portable SSD. For the sake of argument, we’ll give the computer an average of 1TB of storage space. What about the brain’s storage capacity? Well, it’s complicated. Estimates vary on how many nerve cells, or neurons, exist in a typical brain. Many studies rely on 100 billion neurons, while a Stanford University study estimates that the brain actually has 200 billion neurons. You might be thinking, “Wait, the computer has bytes and the brain has neurons. How do we compare the two?” One marked difference between the human brain and computer flash memory is the ability of neurons to combine with one another to assist with the creation and storage of memories. Each neuron has roughly a thousand connections to other neurons. With over a trillion connections in an average human brain, this overlap effect creates an exponentially larger storage capacity. Based on our understanding of neurons today, which is very limited, we would estimate the brain’s storage capacity at 1 petabyte, which would be the equivalent of over a thousand 1TB SSDs. Advantage: Human Brain. So far, it’s an even contest. The human brain has significantly more storage than an average computer. And a computer can process information exponentially faster than a human brain. How about accessing memory? Can a human recall information better than a computer? Well, it depends on what kinds of information we’re talking about. For basic facts, the answer is unequivocally no. If a computer “knows” that the capital of Nevada is Carson City, that fact will always be accessible. A human, on the other hand, may get confused or forget that fact over time, particularly after a long weekend in Vegas. Where computers lag behind humans is the ability to assign qualitative rankings to information. For a computer, all information is exactly the same. Humans, on the other hand, have many different types of memories and prioritize memories based on their importance. You will undoubtedly remember numerous details about your wedding day, but you probably forgot what you had for lunch last Thursday. (It was a tuna sandwich on rye, in case you were wondering.) Humans also relate memories to one another, so your memory of New Year’s Eve will tie to all of your other New Year celebrations over the course of your life. A computer lacks this ability, at least for now. Advantage: Unclear Energy EfficiencyThe contest is still a toss-up. Computers are faster and more precise, while humans have more storage capacity and nuance in accessing memories. What about energy efficiency? Here is where it gets really fun. A typical computer runs on about 100 watts of power. A human brain, on the other hand, requires roughly 10 watts. That’s right, your brain is ten times more energy-efficient than a computer. The brain requires less power than a lightbulb . We may not be the brightest bulbs in the box, but then again, we don’t have to be. Advantage: Human Brain Ultimately, there is no clear winner overall. Human beings and computers have their own advantages, depending on the category. If you want precision and raw processing speed, a computer is the clear choice. If you want creativity, energy efficiency, and prioritization, a human is your best bet. The good news is that we don’t have to choose. It doesn’t have to be a contest of humans against computers. We can work together and enjoy the best of both worlds. That is, until Skynet becomes self-aware . Tags: Technology , Kris SharmaGet to know the author:. Kris Sharma is a content creator living in Boise, Idaho. 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Things you buy through our links may earn Vox Media a commission Your Memory Is Not Like a Computer’s If you want to your computer to save the above image of a cute kid dressed up as a robot, it’s simple enough: just right-click and save-as, or the equivalent on your operating system. But if you, the human being, wanted to remember the image, the process is way less clear. Yet the way we popularly conceptualize and measure memory — in the recalling of facts for exams, the remembering of your co-workers’ names so as not to feel like an ass at happy hour, the by-hearting of poetry to console yourself in times of crisis — acts as though it’s a hard drive sitting between your ears rather than a goopy mess of neurons. Research on metaphors shows how much they frame our thinking . When crime is described to experimental subjects as a monster , they recommended force-based solutions like more jails or calling in the national guard; when it’s a disease , they recommend building more schools and public-health solutions. There’s a long tradition of saying the mind is the latest technology: In Beyond the Brain , cognitive scientist Louise Barrett collects a few: Socrates said the mind was a wax tablet; John Locke said it was a blank slate where sense impressions are written; Sigmund Freud thought it a hydraulic system yearning for release. “The mind/brain has also been compared to an abbey, cathedral, aviary, theatre, and warehouse, as well as a filing cabinet, clockwork mechanism, camera obscura, and phonograph, and also a railway network and telephone exchange,” she writes. “The use of a computer metaphor is simply the most recent in a long line of tropes that pick up on the most advanced and complex technology of the day.” Just as one should not mistake the map for the terrain, one should not mistake the metaphorical image for the real thing. “We understand how computer memory works, so we end up with the illusion that we understand how human memory works,” says Daphna Shohamy , a cognitive neuroscientist at Columbia University’s Zuckerman Institute. “For our computers, every unit of information is created equal — it has a size, but there’s no qualitative difference. In our brain memories, that’s not true at all.” Human memories are stretchier, less reliable, and generally weirder than your computer may lead you to believe, as Shohamy and her peers in psychology and neuroscience have found. Here are a few of the ways: There are different types of memories. When you talk about your memories, it’s likely in the sense of a flashback — a sensory-based scene from earlier in your life, like in a movie. Scientists refer to this as “episodic memory,” since you’re remembering an episode from earlier in the TV series called You. But memory also takes the form of “reinforcement learning,” or figuring out how a system or interaction is supposed to work. It’s procedural, like sensing just how much to twist the key to get a fickle front door to lock or the right rhythm to swipe your subway card with. Fittingly enough, Shohamy’s lab has found that teen brains keep the two forms of memory more closely related than adults’. Not all memories are narratives that you can hold in your mind; some are just how you “remember how to do” a procedure. Your memories change. “You can disrupt the re-storage of the memory,” says New York University neuroscientist Elizabeth Phelps , who studies the intersection of emotion, learning and memory. In the language of mind science, memories are “plastic” — meaning that rather than being set in stone, they can be molded like clay. In some ways, this is a destabilizing finding, and it explains why so many eyewitness-based convictions get overturned by DNA evidence. A landmark 1974 study had participants watch movie clips of fender benders, and found that if subjects were asked about how fast the car was going when they “smashed” into each other, people recall faster speeds than if they merely “hit.” They’ll even be more likely, as the New York Times reported , to recall shattered glass they never saw. More hopefully, one of the goals of neuroscience is to find how this mutability can be used to help people with fear and anxiety disorders — since if you could alter the the memory of a trauma, it could free lots of people of lots of suffering. But from a research standpoint, it’s still early. “There is huge promise that we can understand them well enough to make clinical interventions, but we haven’t done it yet,” Phelps says. Memories form depending on their relevance to your life. Compare how you recall the hours spanning 6 p.m. to midnight on Tuesday, November 8, to the Tuesday before, November 1. If those memories were encoded like a computer stores them, there would be no difference — one six-hour recording of dinner and maybe a little Netflix, one six-hour recording of ballots coming in. But for anyone at all politically engaged, the events of Election Night — down to the New York Times’ aggravating electo-meter — will be seared into memory, down to the taste of the booze you reached to drown your sorrows in. “Maybe for a 1-year-old, the two Tuesdays may not differ so much. They don’t find personal significance,” Shohamy says. “Maybe someone who got engaged last Tuesday might have very strong memories for both events.” This is something that good teachers already know: If you want students to remember a lesson, you show them how it connects to their lives. Your memories are tied together. When you say that a new experience “reminds” you of something, that’s an indication of how your memories thread together. Shohamy says that her memories of Election Night aren’t just tied to other second Tuesdays of November, but “disastrous political events” that she’s lived through, like when she was a student in Israel and Yitzhak Rabin, a prime minister pushing the Middle East toward peace , was assassinated . New York felt like it did on the days after 9/11, or so I am told. “We connect the memories we have on a lot of different associative levels,” she says. “It’s not a folder saying, ‘Here are the Election Nights.’ But there are a lot of common features and feelings and concepts that we use to connect across memories.” Relatedly, one of the best ways to learn a new fact is through “elaboration”: new thing X is like old thing Y . “The more you can explain about the way your new learning relates to prior knowledge,” write Peter Brown, Henry Roediger, and Mark McDaniel, authors of Make It Stick: The Science of Successful Learning , “ the stronger your grasp of the new learning will be, and the more connections you create that will help you remember it later.” Like if you’re learning about heat transfer in physics, summon sensations to mind of how a hot cup of cocoa warms your hands on a cool winter evening. The more surprising an experience is, the more likely you’ll recall it. “We’re constantly generating expectations of what’s likely to happen,” Shohamy says, “and when what we expect doesn’t happen, that’s a big signal to our brain to pay attention.” Big surprises make for ready recall, whether that’s this month’s history-shaping election result; the mind-imploding, heart-expanding twist in Arrival ; or a first date that goes way better than you hoped for. “Our brains are built to help us deal with the world in better ways and not just so we can reminisce,” she says. “Predicting the future is remembering what happened in the past. Our memories are a bridge between what happened and what will happen next.” Evolution has disposed us to readily record the unexpected, so that the next time something nuts happens, we’ll be prepared.
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As part of your account, you’ll receive occasional updates and offers from New York , which you can opt out of anytime. Comparing Human Memory to the Working of a ComputerIntroduction. For a long time, human mental ability has remained unparalleled in the world. People exhibit multifaceted thought processes that cannot be compared to any other living organism. Indeed, the only available competitor to human intellectual capability is an invention called the computer. Since the inception of computers, humans have found themselves being surrounded by machines that carry out tasks that were initially theirs. Today, computers have been integrated into almost every sector of man-kind activities like banking, hospitals, businesses, transport, and schools. The machine learning and artificial intelligence of computers create new breakthroughs that improve with time (Johnson, 2016). Some people argue that the human brain works like a computer, while others think that computers are too advanced to be compared with the human brain. This paper demonstrates that despite computers performing complex tasks, the working of the human brain is better than that of a computer’s memory. It shows how the sensory, short, and long memory applies in this comparison and the key factors and theories of forgetting in both humans and computers. Comparison between the Human Brain and the Computer MemoryStorage capacity. Both the computer and the human brain have the capacity to store information. The human brain comprises cells called neurons while computer memory consists of circuits or chips. Both the memory chips on a computer and the human brain store information which makes them operate. For a single wire in a memory chip, millions of neutrons exist to carry pulses in the brain. Computer memory is measured in megabytes but human memory is estimated to be 2.5 million gigabytes (Nishihara et al., 2016). Although computers are fast at accessing and retrieving stored information, the human brain’s storage capacity is billions of times larger than that of a computer. Information Retrieval ProcessTo utilize the stored information, both human beings and computers use different ways to access it. The human memory is content addressable, which means that the stored information is related to one another. However, the computer memory is byte-addressable, which means that instructions are connected to specific files on the computer (Nishihara et al., 2016). The stored information on computers is more consistent and can only be retrieved when a command is given, but the human brain network of neurons recalls diverse and more random information depending on the situation. Content addressable memory in people enables them to retrieve information, and at the same time, access its related information. A computer cannot deviate from its programming, and therefore, the working of the human brain is superior to that of a computer. Thought Versus Memory Processing AbilityThere are some tasks that human memory performs better than the computer. Human beings are good at pattern recognition, creative thinking, and language abilities. A person is able to recognize subtle details that a computer cannot (Nishihara et al., 2016). Artificial intelligence and machine learning are being conducted to improve the computer’s limitations when it comes to pattern of recognition, creative thinking, and language abilities. The thought process of a human brain is dynamic which means it can retrieve specific and related information simultaneously. However, a computer’s memory is static which means it can only follow its programs to retrieve a particular file. The dynamic nature of the brain enables it to perform better than a computer in some tasks. Sensory, Short, and Long Team MemoriesThe sensory memory compares to the computer Random Access Memory (RAM) because they are both short-term memories. Sensory memory is temporary and it registers information about a person’s environment. It allows people to retain impressions of sensory information after the stimulus has stopped. Its main purpose is to retain information long enough for it to be recognized. There are three types of sensory memory, iconic or visual, Echoic or auditory, and Haptic or tactile (Johnson, 2016). A computer has short and long-term memory and its non-permanent memory is known as RAM. When a computer is on, its running programs are loaded on RAM. However, when the computer is switched off, this information is deleted. Thus, sensory memory temporarily stores information in the brain, while RAM briefly stores the running programs on a computer. They are both short-term memories because the information stored in them is volatile. Information is only useful if it can be permanently stored and retrieved whenever needed. This is achieved by using long-term memory which stores a considerable amount of information on the computer and the brain. The data on a computer’s hard drive is permanent and does not change with time. However, information on the long-term memory of the brain changes over the years. According to Johnson (2016), both the brain and the hard drive provide every piece of information with a specific storage location. Long-term memory, therefore, exists in the human brain and the computer. Key Factors and Theories of ForgettingHuman beings and computers tend to forget information that is stored in short-term memory. The RAM stores programs and data that are being utilized by a running computer. Once the computer is switched off, the information gets deleted. People also tend to forget important information such as where they left their keys, the names of other people, and even how to solve mathematics problems. It is so because they are unable to retrieve information from the brain. Another key factor that causes people to forget is time. If the information in the brain has not been used for a long time, people tend to forget it (“Psychology differentiates the power of the human brain and a computer,” 2019). Thus, the factors that make human beings forget are different from the ones of a computer. There are theories that relate to forgetting which include retrieval failure, ineffective encoding, interference, decay or fading, motivated forgetting, and physical injury or trauma (Luo, 2018). In the retrieval theory, memory is forgotten simply because it cannot be retrieved since it was never stored. The ineffective encoding theory explains why forgetfulness has occurred as a result of poor encoding of stored information. Interference happens when confusion takes place in the long term memory and the right information is not retrieved. The decay theory explains why information disappears when it has not been used for a long time. Physical injury or trauma theory explains why one forgets information that occurred prior or after a traumatic event (Luo, 2018). The decay theory is the most common theory of forgetfulness in people. Although computers are extremely fast, the working of the human brain is superior when compared to computer memory. They both store information that they use to perform their tasks, but the human brain capacity is larger than that of a computer. The brain has short-term memory known as sensory, while the computer utilizes RAM. In addition, both the human brain and computer have long-term memory where permanent information is stored. When it comes to forgetfulness, distinct factors affect the brain and computer memory. The information on the brain fades with time but the one on the computer remains the same over the years. Johnson, N. (2016). The human brain vs. computers: The identity challenge . TCDI. Luo, L. (2018). Why is the human brain so efficient ? Nautilus . Nishihara, K., Taya, N., & Kanoh, T. (2016). A consideration of realizing the brain inspired computer . Proceedings of the 9th EAI International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS) . Psychology differentiates the power of human brain and a computer . (2019). Human Behavior Remodelling – Technologies that affect Human Behavior. Web. Cite this paperSelect a referencing style
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Human Memory: The Current State of Research
IntroductionShort-term and long-term memory. Human memory has long been a subject of research and scientific debates, and biology, psychology, and neuroscience are still reaching new frontiers in studying this phenomenon. The development of computer technology in the 1950s and 1960s has advanced scientific understanding and drew a parallel between computer and brain processes. Today, the most common definition of memory is the faculty of the human brain allowing information encoding, storage, and retrieval. However, the assumption that akin to a computer the human brain merely “copies” the original experience is simplistic at best and misleading at worst. While research on human memory has been proliferating in the past few decades, it has also led to plenty of inconsistencies in the field. The purpose of this paper is to review the current research on encoding, storage, and retrieval processes as well as short-term and long-term memory, providing practical implications for psychologists and specialists in related fields. Encoding is one of the key memory processes that means the transformation of incoming information (sensory input) into a form that is palatable by the human brain and apt for further storage. There are a few ways in which incoming information can undergo encoding: visual (pictures), acoustic (sound), and semantic (meaning) (Radvansky, 2015). The existing scientific consensus suggests that acoustic and visual are the primary encoding principle in short-term memory (STM). Simply put, when presented with new information, such as a list of numbers, a person is likely to memorize them by verbally rehearsing or retaining the visual representation of the object (Radvansky, 2015). Conversely, long-term memory (LTM) relies primarily on semantic coding (by meaning), though the principles of encoding may vary from person to person. Current research works toward identifying the factors that affect and improve memory encoding. New findings promise not only to empower the learning process by integrating human brain cues but also mitigate memory loss that is an unavoidable part of aging. Makowski et al. (2017) are convinced that presence, or in their words, “being there,” boosts memory encoding. Their research hinges on the well-grounded assumption that the quality of the encoded memory trace is shaped by various characteristics of stimuli as well as by the physical and mental state of a person during the encoding. Machowski et al. (2017) enlist the “pillars” of presence identified by recent literature: first-person perspective, interactivity, attention, and emotional engagement. However, the scholars argue that presence does not have to be real: it may as well be a simulation that will arguably lead to the same improved memory outcomes. Makowski et al. (2017) hired 268 participants that were offered to watch the same live-action movie ( Avengers ) either in 2D or 3D format. After watching the movie, participants filled a questionnaire that measures dimensions such as emotional experience, presence, factual memory, and temporal order memory. The findings showed that subjective presence was associated with the intensity of emotional reactions and, in turn, improved factual memory. However, temporal order memory remained unaffected by enhanced presence due to 3D technologies. In her research, Cheke (2016) draws on similar theoretical underpinnings as she argues that remembering the context of “where” and “when” something happened helps with creating associations that, in turn, boost memory encoding. She then proceeds with hypothesizing that age-related memory deficits may be ascribed to faulty associative ties that include distractor items or irrelevant environmental features. For her study, Cheke (2016) recruited younger and older participants; both of the groups played the treasure hunt game while employing the “what –where – when” episodic memory strategy. The findings suggested that older participants benefited the most from the strategy as it lightened the burden on working memory and attentional resources. The two studies provide cues for medical doctors, psychologists, social workers, and other specialists working with elderly clients and adults with otherwise impaired memory encoding. There is not a single part of the brain that stores all the memory; instead, the storage location is defined by the type and use of memories. Explicit memories (information about events where a person was present, general facts, and information) are stored in the hippocampus, the neocortex, and the amygdala. For implicit memories, also referred to as unconscious or automatic memories, the most crucial brain regions are the basal ganglia and cerebellum (Radvansky, 2015). Short-term working memory relies most heavily on the prefrontal cortex (Radvansky, 2015). They allow a person to perform tasks without thinking about them on purpose: for instance, a person can easily brush teeth without any conscious effort because their actions will be guided by implicit motor memory. Lastly, the storage of short-term working memory needed for the completion of a task at hand takes place in the prefrontal cortex. It has been established that there is no specific site where all memories are stored. Yet, the question arises as to whether their location depends on their type. Fougnie et al. (2015) provide evidence that the storage of working memory in humans may be domain-specific. In their study, Fougnie et al. (2015) assessed participants’ performance when completing concurrent visuospatial and auditory tasks. The findings show that the performance of the two tasks is independent of each other. The paper concludes that while some regions are domain-independent, which is at the moment, the dominant idea in neuroscience, others are responsible for storing specific types of information. Christophel et al. (2018) refer to human memory storage as a distributed system with engaged regions ranging from sensory to parietal and prefrontal cortex. One explanation that Christophel et al. (2018) provide is the nature of memory encoding before storage: the scholars point out the gradient of abstraction from the processing of low-level sensory features to more complex abstract, semantic encoding. This phenomenon also leads one to the realization that all the brain regions responsible for storing memories do not work independently from each other. Conversely, their contributions are best defined in terms of representational stages with varying levels of transformation and abstraction (Christophel et al., 2018). The paper concludes that the scientific community might need a paradigm shift when it comes to understanding memory storage. The focus should be not on the storing functions and capacities of each region but rather on their interaction and collaboration. The concept of memory retrieval refers to accessing memories from the past. There are several types of retrieval: recall, recollection, recognition, and relearning (Radvansky, 2015). A recall is the type of retrieval that occurs without any external cue (e.g. filling one’s name when registering on the website). Unlike recall, recollection requires a conscious effort in the form of logical structures, partial memories, narratives, or clues. In other words, recollection “reconstructs” a memory, using internal and external evidence. Recognition refers to the realization that something is indeed familiar when encountering it (e.g. a song sounds familiar, but the listener cannot quite put a finger on where they heard it before or the name and the artist). Lastly, relearning help when information has now been rendered inaccessible; experiencing it again strengthens memories and makes them retrievable with greater ease in the future. Retrieval is critical for guiding a person’s current thoughts and decisions and being able to handle day-to-day tasks. For this reason, psychology, neuroscience, and related fields are concerned with identifying factors that affect memory retrieval. One of such factors is the stress that triggers specific endocrine responses influencing multiple human memory processes at once – encoding, storage, and, obviously, retrieval. Wolf (2017) explains that it is common for humans to remember an extremely frightening or unnerving experience (assault, terrorist attack, failed job interview, and others) for a lifetime. However, such memories become easily accessible and as vivid as they were on the day of the occasion, other important memories may become suppressed while a person is under stress (Wolf, 2017). What is more, the impairing effects of stress on memory retrieval may last and interfere with an individual’s daily functioning longer than it was initially understood. To further prove these assumptions, Stock and Merz (2018) carried out a controlled trial for which they recruited forty healthy male students. The difference between the control and intervention groups was exposure to psychological stress. For a better assessment of memory retrieval mechanisms under stress, students had to study a material that contained diverse types of information: coherent text, visual information, numerical, and others. The follow-up assessment was conducted 24 hours after the exposure. Stock and Merz (2018) chose the socially-evaluated cold pressor test for the intervention group: each participant had to submerge their dominant arm and forearm into ice-cold water while having a stranger look at and videotape them. Control group participants showed better retrieval of visual and numeric items, while those exposed to the stress test surpassed them in retrieving verbal information. Another curious finding suggested that higher levels of cortisol improved memory retrieval, which provides further support for exposure in psychotherapy of phobias. Short-term and long-term are two main types of memory, and as the name suggests, the key difference between them is duration. The concepts have generated quite a lot of controversy in the fields of cognitive psychology and neuroscience. Norris (2017) explains that for over a century, scientists have believed that the human brain operates two different systems for storing short-term and long-term memories. However, according to the researcher, such claims relied on either sparse experimental or purely introspective data. The holders of dissenting views, to which Norris (2017) himself belongs, argue that there is a single memory system responsible for handling both short-term and long-term memories. Within this paradigm, short-term memories have the capacity of converting to long-term memories. In turn, when activated, the latter become the former and can be used to guide current thoughts and decisions. Norris (2017) supports his argument with neuroimaging data that suggests the presence of a single system with a complex binding mechanism and pointers facilitating interactions between LTM and STM. However, what remains unclear is the activation of LTM to become STM. It may be possible with the help of an additional activating mechanism. In their research, Missaire et al. (2017) concern themselves with the former mechanism: they seek to pinpoint how exactly STM becomes LTM. The scholars assume that STM, which they equate with WM (working memory), is erased and reset shortly after being utilized. The human brain does so to prevent itself from overflooding with irrelevant information that would interfere with newly stored input. Missaire et al. (2017) experimented with rodents that were completing radial maze tasks. The tasks are typical for assessing WM as they require animals to memorize paths for quick decision-making. The findings suggest that the content of WM may not be immediately erased or forgotten, which contradicts the resetting theory. In some cases, the memories were stored for days, which makes one wonder whether it is possible for all types of WM or only geospatial information. Human cognition is critically dependent on the ability to memorize information and use it in a variety of contexts. Today the research on human memory and all its functions, such as encoding, storage, and retrieval, may provide useful practical implications as well as resolve old or generate new controversies. The quality of encoding varies a lot depending on the attentional engagement, subjective presence, and emotional intensity. The gradient of abstraction when encoding sensory input into more abstract representations engages multiple brain regions that are also responsible for memory storage, creating a distributed system. Memory retrieval is affected by emotions and, especially, stress responses that may eventually lead to impairments. Humans utilize both short and long-term memory whose duration as well as belongingness to the same or distinct systems are still debated. Cheke, L. G. (2016). What–where–when memory and encoding strategies in healthy aging. Learning & Memory , 23 (3), 121-126. Christophel, T. B., Klink, P. C., Spitzer, B., Roelfsema, P. R., & Haynes, J. D. (2017). The distributed nature of working memory. Trends in cognitive sciences , 21 (2), 111-124. Fougnie, D., Zughni, S., Godwin, D., & Marois, R. (2015). Working memory storage is intrinsically domain specific. Journal of Experimental Psychology: General , 144 (1), 30. Makowski, D., Sperduti, M., Nicolas, S., & Piolino, P. (2017). “Being there” and remembering it: Presence improves memory encoding. Consciousness and Cognition , 53 , 194-202. Missaire, M., Fraize, N., Joseph, M. A., Hamieh, A. M., Parmentier, R., Marighetto, A.,… & Malleret, G. (2017). Long-term effects of interference on short-term memory performance in the rat. Plos One , 12 (3), e0173834. Norris, D. (2017). Short-term memory and long-term memory are still different. Psychological Bulletin , 143 (9), 992-1009. Radvansky, G. A. (2015). Human memory . Psychology Press. Stock, L. M., & Merz, C. J. (2018). Memory retrieval of everyday information under stress. Neurobiology of Learning and Memory , 152 , 32-38. Wolf, O. T. (2017). Stress and memory retrieval: Mechanisms and consequences. Current Opinion in Behavioral Sciences , 14 , 40-46. Cite this paperSelect style
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Learn more about how Pressbooks supports open publishing practices. CHAPTER 6: LONG-TERM MEMORY Our memories allow us to do relatively simple things, such as remembering where we parked our car or the name of the current governor of California, but also allow us to form complex memories, such as how to ride a bicycle or to write a computer program. Moreover, our memories define us as individuals — they are our experiences, our relationships, our successes, and our failures. Without our memories, we would not have a life. WORKING MEMORY VS LONG-TERM MEMORY As we discussed in the last chapter, working memory is a temporary storage space for information that is being actively stored and manipulated in consciousness. Information that is not rehearsed will be forgotten within 18 to 30 seconds. Long-term memory, on the other hand, is where we store everything from a few moments to the earliest thing we can remember. There is theoretically no upper limit to the amount of information we can store in long-term memory. THE SERIAL POSITION CURVEThe distinction between working memory and long-term memory can be demonstrated with the serial position curve . When we give people a long list of words one at a time (e.g., on flashcards) and then ask them to recall them, the results look something like those in Figure 1. People are able to retrieve more words that were presented to them at the beginning and the end of the list than they are words that were presented in the middle of the list. This pattern, known as the serial position curve, is caused by two retrieval phenomenon: The primacy effect refers to a tendency to better remember stimuli that are presented early in a list. The recency effect refers to the tendency to better remember stimuli that are presented later in a list.  Figure 1. The serial position curve is the result of both primacy effects and recency effects. CONCEPT FOCUS: DIFFERENCES BETWEEN BRAINS & COMPUTERS• In computers, information can be accessed only if one knows the exact location of the memory. In the brain, information can be accessed through spreading activation from closely related concepts. • The brain operates primarily in parallel, meaning that it is multitasking on many different actions at the same time. Although this is changing as new computers are developed, most computers are primarily serial — they finish one task before they start another. • In computers, short-term (random-access) memory is a subset of long-term (read- only) memory. In the brain, the processes of short-term memory and long-term memory are distinct. • In the brain, there is no difference between hardware (the mechanical aspects of the computer) and software (the programs that run on the hardware). • In the brain, synapses, which operate using an electrochemical process, are much slower but also vastly more complex and useful than the transistors used by computers. • Computers differentiate memory (e.g., the hard drive) from processing (the central processing unit), but in brains there is no such distinction. In the brain (but not in computers) existing memory is used to interpret and store incoming information, and retrieving information from memory changes the memory itself. • The brain is self-organizing and self-repairing, but computers are not. If a person suffers a stroke, neural plasticity will help him or her recover. If we drop our laptop and it breaks, it cannot fix itself. • The brain is significantly bigger than any current computer. The brain is estimated to have 25,000,000,000,000,000 (25 million billion) interactions among axons, dendrites, neurons, and neurotransmitters, and that doesn’t include the approximately 1 trillion glial cells that may also be important for information processing and memory. Although cognitive psychology began in earnest at about the same time that the electronic computer was first being developed, and although cognitive psychologists have frequently used the computer as a model for understanding how the brain operates, research in cognitive neuroscience has revealed many important differences between brains and computers. The neuroscientist Chris Chatham (2007) provided the list of differences between brains and computers shown here. You might want to check out the website and the responses to it at: http://www.sentientdevelopments.com/2011/05/chris-chatham-10-important-differences.html There are a number of explanations for primacy and recency effects, but one of them is in terms of the effects of rehearsal on short-term and long-term memory (Baddeley, Eysenck, & Anderson, 2009). Because we can keep the last words that we learned in the presented list in short-term memory by rehearsing them before the memory test begins, they are relatively easily remembered. So the recency effect can be explained in terms of maintenance rehearsal in short-term memory— the most recent words are still available in short-term memory at the time of recall. And the primacy effect may also be due to rehearsal—when we hear the first word in the list we start to rehearse it, making it more likely that it will be moved from short- term to long-term memory. And the same is true for the other words that come early in the list. But for the words in the middle of the list, this rehearsal becomes much harder, making them less likely to be moved to LTM. According to Baddeley’s model, working memory includes a central executive, phonological loop, visuospatial sketchpad, and episodic buffer. What is the structure of long-term memory? As you can see in Figure 2, long-term memory can be divided into two major categories of memory types: explicit memory and implicit memory, which can be further divided into multiple sub-types: semantic, episodic, procedural, priming, and conditioning memory. EXPLICIT MEMORY The first form of long-term memory we will discuss is explicit memory. We are measuring explicit memory when we assess memory by asking a person to consciously remember things. Explicit memory refers to knowledge or experiences that can be consciously remembered . There are two types of explicit memory: episodic and semantic . Episodic memory refers to the firsthand experiences that we have had (e.g., recollections of our high school graduation day or of the fantastic dinner we had in New York last year). Semantic memory refers to our knowledge of facts and concepts about the world (e.g., that the absolute value of −90 is greater than the absolute value of 9 and that one definition of the word “affect” is “the experience of feeling or emotion”).  Figure 1. Types of long-term memory. Explicit memory is assessed using measures in which the individual being tested must consciously attempt to remember the information. A recall memory test is a measure of explicit memory that involves bringing from memory information that has previously been remembered . We rely on our recall memory when we take an essay test, because the test requires us to generate previously remembered information. A multiple-choice test is an example of a recognition memory test , a measure of explicit memory that involves determining whether information has been seen or learned before . Your own experiences taking tests will probably lead you to agree with the scientific research finding that recall is more difficult than recognition. Recall, such as required on essay tests, involves two steps: first generating an answer and then determining whether it seems to be the correct one. Recognition, as on multiple-choice test, only involves determining which item from a list seems most correct (Haist, Shimamura, & Squire, 1992). Although they involve different processes, recall and recognition memory measures tend to be correlated. Students who do better on a multiple-choice exam will also, by and large, do better on an essay exam (Bridgeman & Morgan, 1996). A third way of measuring memory is known as relearning (Nelson, 1985). Measures of relearning (or savings) assess how much more quickly information is processed or learned when it is studied again after it has already been learned but then forgotten . If you have taken some French courses in the past, for instance, you might have forgotten most of the vocabulary you learned. But if you were to work on your French again, you’d learn the vocabulary much faster the second time around. Relearning can be a more sensitive measure of memory than either recall or recognition because it allows assessing memory in terms of “how much” or “how fast” rather than simply “correct” versus “incorrect” responses. Relearning also allows us to measure memory for procedures like driving a car or playing a piano piece, as well as memory for facts and figures. IMPLICIT MEMORY While explicit memory consists of the things that we can consciously report that we know, implicit memory refers to knowledge that we cannot consciously access. However, implicit memory is nevertheless exceedingly important to us because it has a direct effect on our behavior. Implicit memory refers to the influence of experience on behavior, even if the individual is not aware of those influences . As you can see in Figure 2, “Types of Memory,” there are three general types of implicit memory: procedural memory, classical conditioning effects, and priming. Procedural memory refers to our often unexplainable knowledge of how to do things . When we walk from one place to another, speak to another person in English, dial a cell phone, or play a video game, we are using procedural memory. Procedural memory allows us to perform complex tasks, even though we may not be able to explain to others how we do them. There is no way to tell someone how to ride a bicycle; a person has to learn by doing it. The idea of implicit memory helps explain how infants are able to learn. The ability to crawl, walk, and talk are procedures, and these skills are easily and efficiently developed while we are children despite the fact that as adults we have no conscious memory of having learned them. A second type of implicit memory is classical conditioning effects , in which we learn, often without effort or awareness, to associate neutral stimuli (such as a sound or a light) with another stimulus (such as food), which creates a naturally occurring response, such as enjoyment or salivation. The memory for the association is demonstrated when the conditioned stimulus (the sound) begins to create the same response as the unconditioned stimulus (the food) did before the learning. The final type of implicit memory is known as priming , or changes in behavior as a result of experiences that have happened frequently or recently . Priming refers both to the activation of knowledge (e.g., we can prime the concept of kindness by presenting people with words related to kindness) and to the influence of that activation on behavior (people who are primed with the concept of kindness may act more kindly). One measure of the influence of priming on implicit memory is the word fragment test , in which a person is asked to fill in missing letters to make words. You can try this yourself: First, try to complete the following word fragments, but work on each one for only three or four seconds. Do any words pop into mind quickly?  Now read the following sentence carefully: “He got his materials from the shelves, checked them out, and then left the building.” Then try again to make words out of the word fragments. I think you might find that it is easier to complete fragments 1 and 3 as “library” and “book,” respectively, after you read the sentence than it was before you read it. However, reading the sentence didn’t really help you to complete fragments 2 and 4 as “physician” and “chaise.” This difference in implicit memory probably occurred because as you read the sentence, the concept of “library” (and perhaps “book”) was primed, even though they were never mentioned explicitly. Once a concept is primed it influences our behaviors, for instance, on word fragment tests. Our everyday behaviors are influenced by priming in a wide variety of situations. Seeing an advertisement for cigarettes may make us start smoking, seeing the flag of our home country may arouse our patriotism, and seeing a student from a rival school may arouse our competitive spirit. And these influences on our behaviors may occur without our being aware of them. RESEARCH FOCUS: PRIMING OUTSIDE OF AWARENESS INFLUENCES MEMORYOne of the most important characteristics of implicit memories is that they are frequently formed and used automatically , without much effort or awareness on our part. In one demonstration of the automaticity and influence of priming effects, John Bargh and his colleagues (Bargh, Chen, & Burrows, 1996) conducted a study in which they showed undergraduate students lists of five scrambled words, each of which they were to make into a sentence. Furthermore, for half of the research participants, the words were related to stereotypes of the elderly. These participants saw words such as the following: in Victoria retired live people bingo man the forgetful plays The other half of the research participants also made sentences, but from words that had nothing to do with elderly stereotypes. The purpose of this task was to prime stereotypes of elderly people in memory for some of the participants but not for others. The experimenters then assessed whether the priming of elderly stereotypes would have any effect on the students’ behavior — and indeed it did. When the research participant had gathered all of his or her belongings, thinking that the experiment was over, the experimenter thanked him or her for participating and gave directions to the closest elevator. Then, without the participants knowing it, the experimenters recorded the amount of time that the participant spent walking from the doorway of the experimental room toward the elevator. As you can see in Figure 3, “Research Results.” participants who had made sentences using words related to elderly stereotypes took on the behaviors of the elderly — they walked significantly more slowly as they left the experimental room.  To determine if these priming effects occurred out of the awareness of the participants, Bargh and his colleagues asked still another group of students to complete the priming task and then to indicate whether they thought the words they had used to make the sentences had any relationship to each other, or could possibly have influenced their behavior in any way. These students had no awareness of the possibility that the words might have been related to the elderly or could have influenced their behavior. ENCODING, RETRIEVAL, AND CONSOLIDATION Imagine you are able to perfectly study for an exam. You take notes in lecture and read the textbook as the quarter moves along. As you approach the exam, you develop study materials, test yourself on the information, and go to the professor’s office hours to ask about the parts you find the most difficult. The day before the exam, you explain all of the important concepts from class to your best friend. You get a good night’s sleep, and the next morning you find that remembering the important concepts from class feels even easier than it felt the night before. The questions on the exam include bits of information that help you retrieve the concepts that you studied so hard to understand. You leave feeling like your exam performance was a good reflection of the hard work you put in to studying.  Photo by Robert Bye on Unsplash . In this situation, you were able to successfully encode, retrieve, and consolidate the information you sought to learn. Encoding refers to storing new information in long-term memory. This is the process you engaged in during lecture and studying. Retrieval refers to remembering information from long-term memory. This is what you did when you tested yourself on information and when you took the exam. Consolidation is the stabilization of long-term memories after initial encoding. Consolidation is aided by sleep, which is why you felt even more confident in your knowledge by getting a good night of sleep before the exam. The following sections will discuss the factors that affect encoding, retrieval, and consolidation. MAINTENANCE REHEARSALMaintenance rehearsal is a type of memory rehearsal that is useful in maintaining information in working memory. Because this usually involves repeating information without thinking about its meaning or connecting it to other information, the information is not usually transferred to long term memory. That is, maintenance rehearsal does not usually lead to encoding new long-term memories. An example of maintenance rehearsal would be repeating a phone number mentally, or aloud until the number is entered into the phone to make the call. The number is held in working memory long enough to make the call, but never transferred to long term memory. An hour, or even five minutes after the call, the phone number will no longer be remembered. DEPTH OF PROCESSINGThe levels-of-processing effect , identified by Fergus I. M. Craik and Robert S. Lockhart in 1972, describes memory recall of stimuli as a function of the depth of mental processing at encoding. Deeper levels of analysis produce more elaborate, longer-lasting, and stronger memory traces than shallow levels of analysis. Depth of processing falls on a shallow to deep continuum. Shallow processing (e.g., processing based on phonemic and orthographic components) leads to a fragile memory trace that is susceptible to rapid decay. Conversely, deep processing (e.g., semantic processing) results in a more durable memory trace. This theory contradicts the multi-store Atkinson-Shiffrin memory model which represents memory strength as being continuously variable, the assumption being that rehearsal always improves long- term memory. They argued that rehearsal that consists simply of repeating previous analyses (maintenance rehearsal) doesn’t enhance long-term memory.  In a study from 1975 (Craik and Tulving) participants were given a list of 60 words. Each word was presented along with three questions. The participant had to answer one of them. Those three questions were in one of three categories. One category of questions was about how the word was presented visually (“Is the word shown in italics?”). This category of questions was meant to promote orthographic processing , or processing related to how the word was written. The second category of questions was Levels of processing based on evidence from Craik & Tulving. Rogers and colleagues would later add an even deeper level— self-reference. about the phonemic qualities of the word (“Does the word begin with the sound ‘bee’?”). This category was meant to promote phonological processing , or processing related to how the words sound. The third category of questions was presented so that the reader was forced to think about the word within a certain context (“Can you meet one in the street [a friend]”?). This category of questions was meant to promote semantic processing, or processing related to the words’ meaning. The result of this study showed that the more deeply words were processed at encoding, the more likely they were to be remembered later. Later work by Rogers, Kuiper, & Kirker (1977) expanded the levels-of-processing effect by demonstrating an even deeper level of processing than semantic processing: self-referential processing (e.g., “Does this word describe you?”). This is referred to as the self-reference effect: processing words in terms of their relation to yourself promotes an even higher rate of recall than normal semantic processing. THE TESTING EFFECTThe testing effect is the finding that encoding into long-term memory is often increased when some of the learning period is devoted to retrieving the to-be-remembered information. The first documented empirical studies on the testing effect were published in 1909 by Edwina E. Abbott. Later, Carrier and Pashler (1992) showed that testing does not just provide an additional practice opportunity, but produces better results than other forms of studying. In their experiment, learners who tested their knowledge during practice later remembered more information than learners who spent the same amount of time studying the complete information. Additionally, a study done by Roediger and Karpicke (2006) showed that students in a repeated-testing condition recalled much more after a week than did students in a repeated-study condition (61% vs. 40%), even though students in the former condition read the passage only 3.4 times and those in the latter condition read it 14.2 times.  Information stored in the memory is retrieved by way of association with other memories. Some memories can not be recalled by simply thinking about them. Rather, one must think about something associated with it. For example, if someone tries and fails to recollect the memories he had about a vacation he went Photo albums can be great sources of retrieval cues. Photo by BBH Singapore on Unsplash. on, and someone mentions the fact that he hired a classic car during this vacation, this may make him remember all sorts of things from that trip, such as what he ate there, where he went and what books he read. ENCODING SPECIFICITY PRINCIPLEThe encoding specificity principle is the general principle that memory is best when the conditions at encoding match the conditions at retrieval. For example, take the song on the radio: perhaps you heard it while you were at a terrific party, having a great, philosophical conversation with a friend. Thus, the song became part of that whole complex experience. Years later, even though you haven’t thought about that party in ages, when you hear the song on the radio, the whole experience rushes back to you. In general, the encoding specificity principle states that, to the extent a retrieval cue (the song) matches or overlaps the memory trace of an experience (the party, the conversation), it will be effective in evoking the memory. One example of the encoding specificity principle is transfer-appropriate processing , in which memory is best when the type of cognitive processing at recall matches the type of cognitive processing at encoding. This was empirically shown in a study by Morris and associates (1977) using semantic and rhyme tasks. In a standard recognition test, memory was better following semantic processing compared to rhyme processing, as predicted by the levels-of-processing effect. However, in a rhyming recognition test, memory was better for those who engaged in rhyme processing compared to semantic processing. This adds a level of complexity to the levels-of-processing theory: while the levels-of-processing framework generally holds for a normal recognition test, performance on rhyming tests is actually better with phonological than semantic processing at encoding.  Other facets of the encoding specificity principle include context-dependent memory . Context-dependent learning refers to an increase in retrieval when the external situation in which information is learned matches the situation in which it is remembered. Godden and Baddeley (1975) conducted a study to test this idea using scuba divers. Results from Morris and colleagues’ Transfer-Appropriate Processing experiment. They asked the divers to learn a list of words either when they were on land or when they were underwater. Then they tested the divers on their memory, either in the same or the opposite situation. The divers’ memory was better when they were tested in the same context in which they had learned the words than when they were tested in the other context. In this instance, the physical context itself provided cues for retrieval. Whereas context-dependent memory refers to a match in the external situation between learning and remembering, state-dependent memory refers to superior retrieval of memories when the individual is in the same physiological or psychological state as during encoding. Research has found, for instance, that animals that learn a maze while under the influence of one drug tend to remember their learning better when they are tested under the influence of the same drug than when they are tested without the drug (Jackson, Koek, & Colpaert, 1992). Research with humans finds that bilinguals remember better when tested in the same language in which they learned the material (Marian & Kaushanskaya, 2007). Mood states may also produce state-dependent learning. People who learn information when they are in a bad (rather than a good) mood find it easier to recall these memories when they are tested while they are in a bad mood, and vice versa. It is easier to recall unpleasant memories than pleasant ones when we’re sad, and easier to recall pleasant memories than unpleasant ones when we’re happy (Bower, 1981). CONSOLIDATIONMemory consolidation is a category of processes that stabilize a memory trace after its initial acquisition. 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Psychological Monographs, 74 (11), 1–29. Unsworth, N., & Engle, R. W. (2007). On the division of short-term and working memory: An examination of simple and complex span and their relation to higher order abilities. Psychological Bulletin, 133 (6), 1038–1066. Wamsley, E. J., Tucker, M., Payne, J. D., Benavides, J. A., & Stickgold, R. (2010). Dreaming of a learning task is associated with enhanced sleep-dependent memory consolidation. Current Biology, 20(9), 850-855. Wang, Y., Liu, D., & Wang, Y. (2003). Discovering the capacity of human memory. Brain & Mind, 4 (2), 189–198. CHAPTER 6 LICENSE & ATTRIBUTIONSource: Stangor, C. and Walinga, J. (2014). Introduction to Psychology – 1st Canadian Edition . Victoria, B.C.: BCcampus. Retrieved from: https:// opentextbc.ca/introductiontopsychology/ Introduction to Psychology – 1st Canadian Edition by Charles Stangor is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Changes and additions (c) 2014 Jennifer Walinga, licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. Condensed from Walinga version; American spellings used; Imperial measurements used; some content adapted to suit course. Serial position curve information from: Andrade, M., & Walker, N. (n.d.) Cognitive Psychology. Cognitive Psychology by Mehgan Andrade and Neil Walker is licensed under a Creative Commons Attribution4.0 International License. Encoding, Retrieval, and Consolidation Source: The following entries accessed from http:/ www.en.wikipedia.org/ served as sources for this chapter: Memory Rehearsal; Levels-of-processing Effect; Testing Effect; Encoding Specificity Principle; Transfer-Appropriate Processing; Memory Consolidation. Wikipedia text is licensed under the Creative Commons Attribution- ShareAlike License. Chapter introduction added. Transitions and images added. Edited for content and clarity throughout. Some encoding specificity principle information from: Andrade, M., & Walker, N. (n.d.) Cognitive Psychology. Cover photo by Julian Dik on Unsplash. Cognition Copyright © 2023 by Karenna Malavanti is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted. Share This BookThank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.
Focus on learning and memoryNature Neuroscience volume 22 , page 1535 ( 2019 ) Cite this article 17k Accesses 3 Citations 24 Altmetric Metrics details In this special issue of Nature Neuroscience , we feature an assortment of reviews and perspectives that explore the topic of learning and memory. Learning new information and skills, storing this knowledge, and retrieving, modifying or forgetting these memories over time are critical for flexibly responding to a changing environment. How these processes occur has fascinated philosophers, psychologists, and neuroscientists for generations, and the question continues to inspire research encompassing diverse approaches. In this special issue, Nature Neuroscience presents a collection of reviews and perspectives that reflects the breadth and vibrancy of this field. Many of these pieces touch on topics that have animated decades of investigation, including the roles of synaptic plasticity, adult neurogenesis, neuromodulation, and sleep in learning and memory. Yet recently developed technologies continue to provide novel insights in these areas, leading to the updated views presented here. Synaptic plasticity, such as long-term potentiation and depression, remains the prevailing cellular model for learning and memory. While many presume that these processes are engaged by learning and mediate lasting changes in behavior, this link has yet to be conclusively demonstrated in vivo. Humeau and Choquet ( https://doi.org/10.1038/s41593-019-0480-6 ) outline the latest tools that can be used to visualize and manipulate synaptic activity and signaling in behaving animals, and they discuss further advances that are needed to help bridge this gap in our understanding. Neuroscientists have also long been intrigued by the role that the formation of new neurons could play in memory formation and maintenance of new memories. Miller and Sahay ( https://doi.org/10.1038/s41593-019-0484-2 ) integrate recent research on adult hippocampal neurogenesis to present a model of how the maturation of adult-born dentate granule cells contributes to memory indexing and interference. While the neural mechanisms underlying memory acquisition and consolidation are relatively well-described, less is known about how memories are retrieved. Frankland, Josselyn, and Köhler ( https://doi.org/10.1038/s41593-019-0493-1 ) discuss how recent approaches that enable the manipulation of memory-encoding neural ensembles (termed ‘engrams’) have informed our current understanding of retrieval. They highlight the ways in which retrieval success is influenced by retrieval cues and the congruence between encoding and retrieval states. They also discuss important open questions in the field. External stimuli and internal states can affect various aspects of learning and memory, which is mediated in part by neuromodulatory systems. Likhtik and Johansen ( https://doi.org/10.1038/s41593-019-0503-3 ) detail how acetylcholine, noradrenaline, and dopamine systems participate in fear encoding and extinction. They discuss emergent themes, including how neuromodulation can act throughout the brain or in specifically targeted regions, how it can boost selected neural signals, and how it can tune oscillatory relationships between neural circuits. The efficacy of memory storage is also influenced by sleep. Klinzing, Niethard, and Born ( https://doi.org/10.1038/s41593-019-0467-3 ) review evidence from rodent and human studies that implicates reactivation of memory ensembles (or ‘replay’), synaptic scaling, and oscillations during sleep in memory consolidation. They also discuss recent findings that suggest that the thalamus coordinates these processes. Effective learning requires us to identify critical information and ignore extraneous details, all of which varies depending on the task at hand. Yael Niv ( https://doi.org/10.1038/s41593-019-0470-8 ) discusses computational and neural processes involved in the formation of such task representations, how factors such as attention and context affect these representations, and how we use task representations to make decisions. The ability to issue appropriate outputs in response to neural activity is a critical brain function, and is often disrupted in injury and disease. Maryam Shanechi ( https://doi.org/10.1038/s41593-019-0488-y ) discusses how ‘closed-loop’ brain–machine interfaces (BMIs) have been used to monitor motor impulses and in turn control prosthetic or paralyzed limbs in order to restore function. Furthermore, she discusses how manipulation of BMI parameters can aid the study of learning. Finally, she explores how BMIs could be used in a similar vein to monitor and correct aberrant mood processes in psychiatric disorders. By highlighting the topic of learning and memory, we honor its importance and centrality in neuroscience, while also celebrating the ways that other disciplines, including psychology, cellular and molecular biology, computer science, and engineering fuel insights in this area. We hope to continue to publish outstanding research in this area, particularly studies that resolve long-standing questions, that develop or leverage new methodologies, and that integrate multiple approaches. Rights and permissionsReprints and permissions About this articleCite this article. Focus on learning and memory. 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Memory is the ongoing process of information retention over time. Because it makes up the very framework through which we make sense of and take action within the present, its importance goes without saying. But how exactly does it work? And how can teachers apply a better understanding of its inner workings to their own teaching? In light of current research in cognitive science, the very, very short answer to these questions is that memory operates according to a "dual-process," where more unconscious, more routine thought processes (known as "System 1") interact with more conscious, more problem-based thought processes (known as "System 2"). At each of these two levels, in turn, there are the processes through which we "get information in" (encoding), how we hold on to it (storage), and and how we "get it back out" (retrieval or recall). With a basic understanding of how these elements of memory work together, teachers can maximize student learning by knowing how much new information to introduce, when to introduce it, and how to sequence assignments that will both reinforce the retention of facts (System 1) and build toward critical, creative thinking (System 2). Dual-Process TheoryThink back to a time when you learned a new skill, such as driving a car, riding a bicycle, or reading. When you first learned this skill, performing it was an active process in which you analyzed and were acutely aware of every movement you made. Part of this analytical process also meant that you thought carefully about why you were doing what you were doing, to understand how these individual steps fit together as a comprehensive whole. However, as your ability improved, performing the skill stopped being a cognitively-demanding process, instead becoming more intuitive. As you continue to master the skill, you can perform other, at times more intellectually-demanding, tasks simultaneously. Due to your knowledge of this skill or process being unconscious, you could, for example, solve an unrelated complex problem or make an analytical decision while completing it. In its simplest form, the scenario above is an example of what psychologists call dual-process theory. The term “dual-process” refers to the idea that some behaviors and cognitive processes (such as decision-making) are the products of two distinct cognitive processes, often called System 1 and System 2 (Kaufmann, 2011:443-445). While System 1 is characterized by automatic, unconscious thought, System 2 is characterized by effortful, analytical, intentional thought (Osman, 2004:989).  Dual-Process Theories and LearningHow do System 1 and System 2 thinking relate to teaching and learning? In an educational context, System 1 is associated with memorization and recall of information, while System 2 describes more analytical or critical thinking. Memory and recall, as a part of System 1 cognition, are focused on in the rest of these notes. As mentioned above, System 1 is characterized by its fast, unconscious recall of previously-memorized information. Classroom activities that would draw heavily on System 1 include memorized multiplication tables, as well as multiple-choice exam questions that only need exact regurgitation from a source such as a textbook. These kinds of tasks do not require students to actively analyze what is being asked of them beyond reiterating memorized material. System 2 thinking becomes necessary when students are presented with activities and assignments that require them to provide a novel solution to a problem, engage in critical thinking, or apply a concept outside of the domain in which it was originally presented. It may be tempting to think of learning beyond the primary school level as being all about System 2, all the time. However, it’s important to keep in mind that successful System 2 thinking depends on a lot of System 1 thinking to operate. In other words, critical thinking requires a lot of memorized knowledge and intuitive, automatic judgments to be performed quickly and accurately. How does Memory Work?In its simplest form, memory refers to the continued process of information retention over time. It is an integral part of human cognition, since it allows individuals to recall and draw upon past events to frame their understanding of and behavior within the present. Memory also gives individuals a framework through which to make sense of the present and future. As such, memory plays a crucial role in teaching and learning. There are three main processes that characterize how memory works. These processes are encoding, storage, and retrieval (or recall).
Improving RecallRetrieval is subject to error, because it can reflect a reconstruction of memory. This reconstruction becomes necessary when stored information is lost over time due to decayed retention. In 1885, Hermann Ebbinghaus conducted an experiment in which he tested how well individuals remembered a list of nonsense syllables over increasingly longer periods of time. Using the results of his experiment, he created what is now known as the “Ebbinghaus Forgetting Curve” (Schaefer, 2015).  Through his research, Ebbinghaus concluded that the rate at which your memory (of recently learned information) decays depends both on the time that has elapsed following your learning experience as well as how strong your memory is. Some degree of memory decay is inevitable, so, as an educator, how do you reduce the scope of this memory loss? The following sections answer this question by looking at how to improve recall within a learning environment, through various teaching and learning techniques. As a teacher, it is important to be aware of techniques that you can use to promote better retention and recall among your students. Three such techniques are the testing effect, spacing, and interleaving.
As useful as it is to know which techniques you can use, as a teacher, to improve student recall of information, it is also crucial for students to be aware of techniques they can use to improve their own recall. This section looks at four of these techniques: state-dependent memory, schemas, chunking, and deliberate practice.
For more information...Brown, P.C., Roediger, H.L. & McDaniel, M.A. 2014. Make it stick: The science of successful learning . Cambridge, MA: Harvard University Press. Gobet, F., Lane, P.C., Croker, S., Cheng, P.C., Jones, G., Oliver, I. & Pine, J.M. 2001. Chunking mechanisms in human learning. Trends in Cognitive Sciences . 5(6):236-243. Kaufman, S.B. 2011. Intelligence and the cognitive unconscious. In The Cambridge handbook of intelligence . R.J. Sternberg & S.B. Kaufman, Eds. New York, NY: Cambridge University Press. Osman, M. 2004. An evaluation of dual-process theories of reasoning. Psychonomic Bulletin & Review . 11(6):988-1010. Roediger, H.L. & McDermott, K.B. 1995. Creating false memories: Remembering words not presented in lists. Journal of Experimental Psychology: Learning, Memory, and Cognition . 21(4):803. Schaefer, P. 2015. Why Google has forever changed the forgetting curve at work. Weissenborn, R. & Duka, T. 2000. State-dependent effects of alcohol on explicit memory: The role of semantic associations. Psychopharmacology . 149(1):98-106.
February 21, 2024 Why Writing by Hand Is Better for Memory and LearningEngaging the fine motor system to produce letters by hand has positive effects on learning and memory By Charlotte Hu  Studies continue to show pluses to writing by hand. Image Source/Getty Images Handwriting notes in class might seem like an anachronism as smartphones and other digital technology subsume every aspect of learning across schools and universities. But a steady stream of research continues to suggest that taking notes the traditional way—with pen and paper or even stylus and tablet—is still the best way to learn, especially for young children. And now scientists are finally zeroing in on why. A recent study in Frontiers in Psychology monitored brain activity in students taking notes and found that those writing by hand had higher levels of electrical activity across a wide range of interconnected brain regions responsible for movement, vision, sensory processing and memory. The findings add to a growing body of evidence that has many experts speaking up about the importance of teaching children to handwrite words and draw pictures. Differences in Brain ActivityOn supporting science journalism. If you're enjoying this article, consider supporting our award-winning journalism by subscribing . By purchasing a subscription you are helping to ensure the future of impactful stories about the discoveries and ideas shaping our world today. The new research, by Audrey van der Meer and Ruud van der Weel at the Norwegian University of Science and Technology (NTNU), builds on a foundational 2014 study . That work suggested that people taking notes by computer were typing without thinking, says van der Meer , a professor of neuropsychology at NTNU. “It’s very tempting to type down everything that the lecturer is saying,” she says. “It kind of goes in through your ears and comes out through your fingertips, but you don’t process the incoming information.” But when taking notes by hand, it’s often impossible to write everything down; students have to actively pay attention to the incoming information and process it—prioritize it, consolidate it and try to relate it to things they’ve learned before. This conscious action of building onto existing knowledge can make it easier to stay engaged and grasp new concepts . To understand specific brain activity differences during the two note-taking approaches, the NTNU researchers tweaked the 2014 study’s basic setup. They sewed electrodes into a hairnet with 256 sensors that recorded the brain activity of 36 students as they wrote or typed 15 words from the game Pictionary that were displayed on a screen. When students wrote the words by hand, the sensors picked up widespread connectivity across many brain regions. Typing, however, led to minimal activity, if any, in the same areas. Handwriting activated connection patterns spanning visual regions, regions that receive and process sensory information and the motor cortex. The latter handles body movement and sensorimotor integration, which helps the brain use environmental inputs to inform a person’s next action. “When you are typing, the same simple movement of your fingers is involved in producing every letter, whereas when you’re writing by hand, you immediately feel that the bodily feeling of producing A is entirely different from producing a B,” van der Meer says. She notes that children who have learned to read and write by tapping on a digital tablet “often have difficulty distinguishing letters that look a lot like each other or that are mirror images of each other, like the b and the d.” Reinforcing Memory and Learning PathwaysSophia Vinci-Booher , an assistant professor of educational neuroscience at Vanderbilt University who was not involved in the new study, says its findings are exciting and consistent with past research. “You can see that in tasks that really lock the motor and sensory systems together, such as in handwriting, there’s this really clear tie between this motor action being accomplished and the visual and conceptual recognition being created,” she says. “As you’re drawing a letter or writing a word, you’re taking this perceptual understanding of something and using your motor system to create it.” That creation is then fed back into the visual system, where it’s processed again—strengthening the connection between an action and the images or words associated with it. It’s similar to imagining something and then creating it: when you materialize something from your imagination (by writing it, drawing it or building it), this reinforces the imagined concept and helps it stick in your memory. The phenomenon of boosting memory by producing something tangible has been well studied. Previous research has found that when people are asked to write, draw or act out a word that they’re reading, they have to focus more on what they’re doing with the received information. Transferring verbal information to a different form, such as a written format, also involves activating motor programs in the brain to create a specific sequence of hand motions, explains Yadurshana Sivashankar , a cognitive neuroscience graduate student at the University of Waterloo in Ontario who studies movement and memory. But handwriting requires more of the brain’s motor programs than typing. “When you’re writing the word ‘the,’ the actual movements of the hand relate to the structures of the word to some extent,” says Sivashankar, who was not involved in the new study. For example, participants in a 2021 study by Sivashankar memorized a list of action verbs more accurately if they performed the corresponding action than if they performed an unrelated action or none at all. “Drawing information and enacting information is helpful because you have to think about information and you have to produce something that’s meaningful,” she says. And by transforming the information, you pave and deepen these interconnections across the brain’s vast neural networks, making it “much easier to access that information.” The Importance of Handwriting Lessons for KidsAcross many contexts, studies have shown that kids appear to learn better when they’re asked to produce letters or other visual items using their fingers and hands in a coordinated way—one that can’t be replicated by clicking a mouse or tapping buttons on a screen or keyboard. Vinci-Booher’s research has also found that the action of handwriting appears to engage different brain regions at different levels than other standard learning experiences, such as reading or observing. Her work has also shown that handwriting improves letter recognition in preschool children, and the effects of learning through writing “last longer than other learning experiences that might engage attention at a similar level,” Vinci-Booher says. Additionally, she thinks it’s possible that engaging the motor system is how children learn how to break “ mirror invariance ” (registering mirror images as identical) and begin to decipher things such as the difference between the lowercase b and p. Vinci-Booher says the new study opens up bigger questions about the way we learn, such as how brain region connections change over time and when these connections are most important in learning. She and other experts say, however, that the new findings don’t mean technology is a disadvantage in the classroom. Laptops, smartphones and other such devices can be more efficient for writing essays or conducting research and can offer more equitable access to educational resources. Problems occur when people rely on technology too much , Sivashankar says. People are increasingly delegating thought processes to digital devices, an act called “ cognitive offloading ”—using smartphones to remember tasks, taking a photo instead of memorizing information or depending on a GPS to navigate. “It’s helpful, but we think the constant offloading means it’s less work for the brain,” Sivashankar says. “If we’re not actively using these areas, then they are going to deteriorate over time, whether it’s memory or motor skills.” Van der Meer says some officials in Norway are inching toward implementing completely digital schools . She claims first grade teachers there have told her their incoming students barely know how to hold a pencil now—which suggests they weren’t coloring pictures or assembling puzzles in nursery school. Van der Meer says they’re missing out on opportunities that can help stimulate their growing brains. “I think there’s a very strong case for engaging children in drawing and handwriting activities, especially in preschool and kindergarten when they’re first learning about letters,” Vinci-Booher says. “There’s something about engaging the fine motor system and production activities that really impacts learning.” A version of this article entitled “Hands-on” was adapted for inclusion in the May 2024 issue of Scientific American.  Human memory: How we make, remember, and forget memoriesHuman memory happens in many parts of the brain at once, and some types of memories stick around longer than others. From the moment we are born, our brains are bombarded by an immense amount of information about ourselves and the world around us. So, how do we hold on to everything we've learned and experienced? Memories. Humans retain different types of memories for different lengths of time . Short-term memories last seconds to hours, while long-term memories last for years. We also have a working memory, which lets us keep something in our minds for a limited time by repeating it. Whenever you say a phone number to yourself over and over to remember it, you're using your working memory. Another way to categorize memories is by the subject of the memory itself, and whether you are consciously aware of it. Declarative memory, also called explicit memory, consists of the sorts of memories you experience consciously. Some of these memories are facts or “common knowledge”: things like the capital of Portugal (Lisbon), or the number of cards in a standard deck of playing cards (52). Others consist of past events you've experienced, such as a childhood birthday. Nondeclarative memory, also called implicit memory, unconsciously builds up. These include procedural memories, which your body uses to remember the skills you've learned. Do you play an instrument or ride a bicycle? Those are your procedural memories at work. Nondeclarative memories also can shape your body's unthinking responses, like salivating at the sight of your favorite food or tensing up when you see something you fear. In general, declarative memories are easier to form than nondeclarative memories. It takes less time to memorize a country's capital than it does to learn how to play the violin. But nondeclarative memories stick around more easily. Once you've learned to ride a bicycle, you're not likely to forget. The types of amnesiaTo understand how we remember things, it's incredibly helpful to study how we forget— which is why neuroscientists study amnesia, the loss of memories or the ability to learn . Amnesia is usually the result of some kind of trauma to the brain, such as a head injury, a stroke, a brain tumor, or chronic alcoholism. There are two main types of amnesia. The first, retrograde amnesia, occurs where you forget things you knew before the brain trauma. Anterograde amnesia is when brain trauma curtails or stops someone's ability to form new memories. The most famous case study of anterograde amnesia is Henry Molaison , who in 1953 had parts of his brain removed as a last-ditch treatment for severe seizures. While Molaison—known when he was alive as H.M.—remembered much of his childhood, he was unable to form new declarative memories. People who worked with him for decades had to re-introduce themselves with every visit. You May Also Like This neuroscientist makes memories—and suppresses them She had a severe brain injury—so her husband turned to an unprecedented therapy 7 medical breakthroughs that gave us hope in 2023By studying people such as H.M., as well as animals with different types of brain damage, scientists can trace where and how different kinds of memories form in the brain. It seems that short-term and long-term memories don't form in exactly the same way, nor do declarative and procedural memories. There's no one place within the brain that holds all of your memories; different areas of the brain form and store different kinds of memories, and different processes may be at play for each. For instance, emotional responses such as fear reside in a brain region called the amygdala. Memories of the skills you've learned are associated with a different region called the striatum. A region called the hippocampus is crucial for forming, retaining, and recalling declarative memories. The temporal lobes, the brain regions that H.M. was partially missing, play a crucial role in forming and recalling memories. How memories are formed, stored, and recalledSince the 1940s scientists have surmised that memories are held within groups of neurons, or nerve cells, called cell assemblies. Those interconnected cells fire as a group in response to a specific stimulus, whether it's your friend's face or the smell of freshly baked bread. The more the neurons fire together, the more the cells' interconnections strengthen . That way, when a future stimulus triggers the cells, it's more likely that the whole assembly fires. The nerves' collective activity transcribes what we experience as a memory. Scientists are still working through the details of how it works. For a short-term memory to become a long-term memory, it must be strengthened for long-term storage, a process called memory consolidation. Consolidation is thought to take place by several processes. One, called long-term potentiation, consists of individual nerves modifying themselves to grow and talk to their neighboring nerves differently. That remodeling alters the nerves' connections in the long term, which stabilizes the memory. All animals that have long-term memories use this same basic cellular machinery; scientists worked out the details of long-term potentiation by studying California sea slugs . However, not all long-term memories necessarily have to start as short-term memories. As we recall a memory, many parts of our brain rapidly talk to each other, including regions in the brain's cortex that do high-level information processing, regions that handle our senses' raw inputs, and a region called the medial temporal lobe that seems to help coordinate the process. One recent study found that at the moment when patients recalled newly formed memories, ripples of nerve activity in the medial temporal lobe synced up with ripples in the brain's cortex. Many mysteries of memory remain. How precisely are memories encoded within groups of neurons? How widely distributed in the brain are the cells that encode a given memory? How does our brain activity correspond to how we experience memories? These active areas of research may one day provide new insight into brain function and how to treat memory-related conditions . For instance, recent work has demonstrated that some memories must be “reconsolidated” each time they're recalled. If so, the act of remembering something makes that memory temporarily malleable—letting it be strengthened, weakened, or otherwise altered. Memories may be more easily targeted by medications during reconsolidation, which could help treat conditions such as post-traumatic stress disorder, or PTSD . Related Topics
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Human MemoryWhat is human memory. There are many types of memory, notably short and long-term memory. User experience (UX) designers cater to the limits of memory to make products easier to use.
All UX designers must know how memory works and how to design around it. This is particularly true for information visualization designers, who must ensure that the viewer readily understands their work for it to be immediately helpful, which results in a much more visually digestible overall user experience. Information architecture and clean layouts also help users identify and remember the essential pieces of information, a crucial element of interaction design. Types of MemoryHuman memory is a powerful mental process that has many implications in life and how you experience things, from remembering meaningful events to enabling you to execute tasks and achieve goals.  © Interaction Design Foundation, CC BY-SA 4.0 In essence, human memory has three facets: sensory memory, short-term memory and long-term memory. The designer is most concerned with the first two types and strategically designs to appeal to short-term and sensory memory. Designers often design around "Task load," the amount of information or choices a person can process simultaneously, also called "working memory." One of the most valuable pieces of information about task load is that humans have trouble remembering and engaging with anything with more than seven (give or take 2) task items. Designers consider this memory limitation when presenting information and wireframing products to provide the most memorable and efficient user experience. Types of MnemonicsMnemonics is the science of memory, and people use a few interesting mnemonic devices to "hack" our brain's programming to improve their memories. The brain is naturally unreliable at remembering abstract things like numbers, dates or concepts. However, it is naturally very good at remembering stories and remembering spaces. The first method is to turn abstract information into a story that is easier to remember, usually through memorable phrases that tell a short story. One common English-language mnemonic phrase to remember the colors of the rainbow is: "Richard Of York Gave Battle In Vain." It helps us remember the following: "Red, Orange, Yellow, Green, Blue, Indigo, Violet." The other method dates back to ancient Greece, called "the method of loci." These ancient thinkers noticed that it was much easier to remember the location of physical objects than abstract thoughts. However, with training, they could create a way to activate "spatial memory" to memorize abstract concepts or facts. They developed a technique called a "mind palace," where a person creates an imaginary version of a space and populates it with object versions of abstract thoughts. For example, a person might imagine their childhood bedroom and place a memory on a shelf in that imaginary bedroom. To remember the memory again, they imagine the bedroom and look on the shelf for the memory. There are memory competitions, and memory champions use techniques that combine or adapt this concept for more impressive feats of memory, like memorizing the order of a randomly shuffled deck of cards.  How to Boost User MemoryFor designers, this means we have a few ways to improve the memory of our users. Employ storytelling to make information more easily digestible and memorable. Have clean, logical menus and a visual hierarchy that is easy to understand and scan. Design for recognition vs. recall or interfaces with items people can quickly identify instead of recalling them from scratch. Leverage spatial memory . Augmented and virtual reality, in particular, can easily activate spatial memory to improve the amount of information users can store. Learn More About Human MemoryWatch journalist and US memory champion Josh Foer's Ted talk, Feats of memory anyone can do . For more on Josh Foer's Article on Mnemonic methods, read Forget Me Not: How to Win the U.S. Memory Championship . See how ancient Greeks used Mnemonics in Method of Loci: Ancient Mnemonic Technique Used by Greeks and Romans Effectively Double Brain's Memory Storage Skills . Questions related to Human MemoryCore memory isn't a scientific term in neuroscience or mental health. The concept originated from Pixar's movie "Inside Out." Core memory represents significant life moments and memories that hold more emotional value. Although not a real psychological phenomenon, core memories have gained cultural popularity. People often use the term to describe impactful life experiences. Discover more about how memory shapes our lives in our article How We Use Long-Term Memory and How it Informs Us Who We Are . In psychology, human memory is the mind's ability to store, retain, and recall information. It's a complex process that involves acquiring, storing, and retrieving data. Psychologists study three main types: sensory memory, short-term memory, and long-term memory. Sensory memory holds information briefly from our senses. Short-term memory keeps information for a short period, like a mental notepad. Long-term memory stores information for longer, from personal experiences to learned knowledge. Understanding these types helps psychologists and designers create more effective learning and user experiences. Due to its vast nature, we can't quantify the human brain's memory capacity. Scientists often compare it to digital storage. They estimate the storage could be around 2.5 petabytes or 2.5 million gigabytes. That is the equivalent of 8.5 years of 24/7, full HD video recording. This means your brain can hold a massive amount of information. However, the brain doesn't work like a computer. It prioritizes and stores information based on relevance and frequency of use. This huge capacity allows you to store a lifetime of memories and knowledge, from everyday facts to personal experiences. There are four main types of memory in the human brain: sensory, short-term, working, and long-term memory. Sensory memory holds information from your senses for a brief moment. Short-term memory keeps information for a short duration, like a temporary holding space. Working memory processes and manipulates information held in short-term memory. It retains information for longer than short-term memory and helps you with reasoning and decision-making. Long-term memory stores vast amounts of information for extended periods, from personal experiences to learned facts. Watch the video below, where Alan Dix discusses long-term memory in more detail. These types interact and contribute to your overall memory function. They play a crucial role in learning and recalling information. The human memory has immense capacity, often linked to an ocean's depth and breadth. Unlike computers, the brain doesn't store data in bytes but through connections and associations. Every new experience or piece of information creates new connections. This network of connections grows as you learn and experience more. Your brain filters and prioritizes this information. It focuses on essential or frequently used information. The brain can store astonishing data, but we don't know the exact limit. It reflects the richness and complexity of human experiences and knowledge. Human memory significantly impacts UX (User Experience) design . Designers must grasp how people remember and process information. This enhances information visualization by making images that are easier to remember. Awareness of human memory also leads to more effective, user-friendly products. Key considerations include the appropriate use of short-term and sensory memory, along with reliance on recognition rather than recall . (Recognition reduces the demands on long-term memory.) Such designs ensure users easily remember and navigate essential features. Effective UX design helps users develop strong, intuitive memories of product use. Using memory-friendly designs, UX designers enhance product accessibility, enjoyment, and efficiency. Thus, a deep understanding of human memory becomes the basis of successful UX design. This concept aligns with narrative sketching and drawing principles. The video, discussing the basics of visual representation and memory, complements this understanding of UX design and human memory interaction. In HCI (Human-Computer Interaction) , human memory refers to how users process, store, and recall information while interacting with computers. It's vital for designing interfaces that are easy to use and remember. HCI focuses on matching computer systems with human cognitive capabilities. This includes considering short-term and long-term memory in design. Designers ensure interfaces align with how users naturally remember tasks and information. They aim for designs that help users easily recall how to use a system without confusion. Understanding human memory in HCI leads to more intuitive and user-friendly technology. For a deeper insight, consider Alan Dix's video on short-term memory in HCI. You may find estimating human memory in gigabytes (GB) difficult. The brain stores information differently than a computer. It uses networks of connections, not digital bytes. Scientists suggest the capacity might be around 2.5 petabytes, equivalent to 2.5 million gigabytes. They did a rough estimate. The brain's storage focuses on connections and experiences more than a quantifiable byte count. The comparison highlights the vast capacity of our memory despite its different functioning from digital storage. The human brain can remember information for varying lengths of time, depending on the type of memory. Short-term memory holds information for seconds to a minute. Working memory retains information for 15 to 30 seconds, used during tasks. Long-term memory can keep information for years, even decades. This includes personal experiences, knowledge, and skills. Factors like attention, repetition, emotional impact, and relevance to the individual influence how long the brain retains information. Some memories last a lifetime, especially those with vital emotional or personal significance. Perception and Memory in HCI and UX Course : This comprehensive course offers insights into human perception and memory. You find them valuable to create effective user interfaces. It covers the role of perception in interaction, the relationship between sensation and perception, and the intricacies of designing for memory. HCI (Memory) Video by Alan Dix: This video provides a concise overview of human memory in the context of HCI. Alan Dix discusses the various types of memory and their importance in designing user interfaces. The Brain and Technology: Brain Science in Interface Design Course : Brian Whitworth created this advanced course that merges brain science with computer science. This course teaches you how to create technology that aligns with human psychology. Literature on Human MemoryHere’s the entire UX literature on Human Memory by the Interaction Design Foundation, collated in one place: Learn more about Human MemoryTake a deep dive into Human Memory with our course Information Visualization . Information visualization skills are in high demand, partly thanks to the rise in big data. Tech research giant Gartner Inc. observed that digital transformation has put data at the center of every organization. With the ever-increasing amount of information being gathered and analyzed, there’s an increasing need to present data in meaningful and understandable ways. In fact, even if you are not involved in big data, information visualization will be able to help in your work processes as a designer. This is because many design processes—including conducting user interviews and analyzing user flows and sales funnels—involve the collation and presentation of information. Information visualization turns raw data into meaningful patterns, which will help you find actionable insights. From designing meaningful interfaces, to processing your own UX research, information visualization is an indispensable tool in your UX design kit. This course is presented by Alan Dix, a former professor at Lancaster University in the UK. A world-renowned authority in the field of human-computer interaction, Alan is the author of the university-level textbook Human-Computer Interaction . “Information Visualization” is full of simple but practical lessons to guide your development in information visualization. We start with the basics of what information visualization is, including its history and necessity, and then walk you through the initial steps in creating your own information visualizations. While there’s plenty of theory here, we’ve got plenty of practice for you, too. All open-source articles on Human MemoryRecalling color theory keywords: a way to refresh your memories. 
Preattentive Visual Properties and How to Use Them in Information Visualization
The Properties of Human Memory and Their Importance for Information Visualization
The Self-Generation Effect: How to Create More Memorable User Interfaces What Types of Memory do we Have? Learn the Role of Perception and Memory in HCI and UX How We Use Long-Term Memory and How it Informs Us Who We Are Open Access—Link to us!We believe in Open Access and the democratization of knowledge . Unfortunately, world-class educational materials such as this page are normally hidden behind paywalls or in expensive textbooks. If you want this to change , cite this page , link to us, or join us to help us democratize design knowledge ! Privacy SettingsOur digital services use necessary tracking technologies, including third-party cookies, for security, functionality, and to uphold user rights. Optional cookies offer enhanced features, and analytics. Experience the full potential of our site that remembers your preferences and supports secure sign-in. Governs the storage of data necessary for maintaining website security, user authentication, and fraud prevention mechanisms. Enhanced FunctionalitySaves your settings and preferences, like your location, for a more personalized experience. Referral ProgramWe use cookies to enable our referral program, giving you and your friends discounts. Error ReportingWe share user ID with Bugsnag and NewRelic to help us track errors and fix issues. Optimize your experience by allowing us to monitor site usage. You’ll enjoy a smoother, more personalized journey without compromising your privacy. Analytics StorageCollects anonymous data on how you navigate and interact, helping us make informed improvements. Differentiates real visitors from automated bots, ensuring accurate usage data and improving your website experience. Lets us tailor your digital ads to match your interests, making them more relevant and useful to you. Advertising StorageStores information for better-targeted advertising, enhancing your online ad experience. Personalization StoragePermits storing data to personalize content and ads across Google services based on user behavior, enhancing overall user experience. Advertising PersonalizationAllows for content and ad personalization across Google services based on user behavior. This consent enhances user experiences. Enables personalizing ads based on user data and interactions, allowing for more relevant advertising experiences across Google services. Receive more relevant advertisements by sharing your interests and behavior with our trusted advertising partners. Enables better ad targeting and measurement on Meta platforms, making ads you see more relevant. Allows for improved ad effectiveness and measurement through Meta’s Conversions API, ensuring privacy-compliant data sharing. LinkedIn InsightsTracks conversions, retargeting, and web analytics for LinkedIn ad campaigns, enhancing ad relevance and performance. LinkedIn CAPIEnhances LinkedIn advertising through server-side event tracking, offering more accurate measurement and personalization. Google Ads TagTracks ad performance and user engagement, helping deliver ads that are most useful to you. Share Knowledge, Get Respect!or copy link Cite according to academic standardsSimply copy and paste the text below into your bibliographic reference list, onto your blog, or anywhere else. You can also just hyperlink to this page. New to UX Design? We’re Giving You a Free ebook! Download our free ebook The Basics of User Experience Design to learn about core concepts of UX design. In 9 chapters, we’ll cover: conducting user interviews, design thinking, interaction design, mobile UX design, usability, UX research, and many more!  An official website of the United States government The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site. The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.
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Memory: Neurobiological mechanisms and assessmentSwaleha mujawar. Department of Psychiatry, Regional Mental Hospital, Nagpur, Maharashtra, India Jaideep Patil1 Department of Psychiatry, Dr. D. Y. Patil Medical College, Hospital and Research Center, Dr D Y Patil Vidyapeeth, Pune, Maharashtra, India Bhushan ChaudhariDaniel saldanha. Memory is the process of retaining of knowledge over a period for the function of affecting future actions. It can be divided into declarative and procedural types. The process of memory consolidation is done in the hippocampus. The long-term memories are spread among various areas of the cerebrum depending on the different perceptual properties. The process of long-term potentiation and molecular changes occurring during memory formation are discussed in detail below. The steps involved in memory formation include encoding, storage, and recall (retrieval) in that order. Amnesia is a phenomenon in which there is the problem in memory formation which can be due to trauma to the brain, certain diseases, or stressors. While the assessment of memory has greatly improved, we are only beginning to understand the underlying mechanisms. Memory is the process of retaining of knowledge over a period for the function of affecting future actions.[ 1 ] From a historical standpoint, the area of memory research from 1870 to 1920 was focused mainly on human memory.[ 2 ] The book: The Principles of Psychology written by famous psychologist William James suggested that there is a difference between memory and habit.[ 3 ] The case of Henry Molaison was first described as a result of research of two main researchers, namely William Beecher Scoville and Brenda Milner.[ 4 ] The findings have contributed a lot to our understanding of memory. Since then, a lot of research has been done in the field of memory and led to many advancements. The Nobel Prize in Physiology or Medicine in the year 2000 was given to Eric Richard Kandel for his contribution in understanding the physiological basis of memory storage in neurons. The prize was shared with Arvid Carlsson and Paul Greengard. CLASSIFICATION OF MEMORYMemory was classified into two types-declarative (explicit) and procedural (implicit) memories by Anderson.[ 5 ] Explicit memory can be defined as the information regarding places, things, people, and events, etc., It can be recollected by conscious effort. It is stored in the medial part of the temporal lobe of the cerebrum and hippocampus. It may be subdivided into episodic memory which is also called autobiographic memory and semantic memory. Episodic memory recalls remember personal events and experiences. Whereas semantic memory recalls facts which can be general or autobiographical. Since Implicit memory is recollected unconsciously it is called nondeclarative. It is stored in various regions of the brain like the cerebellum, the neocortex, the striatum, the amygdala, etc. NEUROBIOLOGYResearch suggests that the hippocampus plays an important role in memory consolidation. It was proved by experiments, wherein lesions were applied to rat hippocampi at various times after learning.[ 5 ] Looking from the molecular and cellular point of view two proteins involved are calcium calmodulin-dependent protein kinase II (CaMKII)[ 6 , 7 ] and protein phosphatase 1 (PP1). During the formation of memory, there is Ca2 + influx after which CaMKII goes through autophosphorylation process which changes it into an activated kinase. Since PP1 has an inhibitory effect on memory it returns the CaMKII to its resting state. This opposite relationship involving CaMKII and PP1 characterizes a push-pull system actually has an important part in memory formation. Hence, a balance maintained between remembering and forgetting the memories which are stored. Consolidation of short-term memories however requires a functional change after which gene transcription and protein synthesis should occur. During any learning process, persistent stimulation causes sustained activation of two pathways namely the protein kinase A (PKA) and MAP kinase Erk (MAPK) pathways. PKA causes phosphorylation as well as the activation of CREB1a which is a transcriptional activator, whereas MAPK causes phosphorylation and inactivation of CREB2, a transcriptional repressor. The most unique characteristic of our brain is the facility to acclimatize to the ever-changing surroundings and to increase its functioning by learning through experience. Learning and memory formation involves a remarkably interesting phenomena of neuroplasticity. During learning there occurs a structural change at the synapse which includes a change in the power of old synapses and changes in the quantity of synaptic connections in particular pathways. Synaptic associations that are less used become weaker over a period and ultimately perish. The ones which are used a lot become stronger with each use and in due course boost in number. Studies suggest that long-term memory (LTM) storage may be preserved by DNA methylation or prions.[ 8 , 9 ] MULTI-STORE MODEL OF MEMORYRichard Atkinson and Richard Shiffrin put forth a model of memory which is known as “The multi-store model or modal model.”[ 10 ] It states that memory consists of three distinct elements: “a sensory register, a short-term store, and a long-term store.” The data from the environment and our senses goes into the memory via the sensory register. The short-term store, otherwise known as working memory or short-term memory (STM), receives and holds input from both the sensory register and the long-term store. Finally, if the information is rehearsed in the short-term store then it goes in the long-term store, wherein it is held indefinitely. Memory consists of the following steps-encoding, storage, and recall (retrieval). Encoding is the process of altering the material reaching our nervous system into a mode that the system can manage so that it can be easily stored. There are various methods through which knowledge can be encoded is via visual, acoustic, semantic coding. The STM is encoded mainly via acoustic coding. The LTM however usually involves semantic coding. Nonetheless, data in LTM can also be encoded both via visual and acoustic coding. When it comes to acquiring data out of storage, the process of retrieval comes into the picture. Unable to remember information can be due to the inability to retrieve that piece of information. Retrieval helps us understand the dissimilarities among STM and LTM. STM is stored and retrieved chronologically. The storage and retrieved of LTM on the other hand occur via association. Thus, the organization of information can facilitate the process of retrieval. Sensory memory consists of three types. The first is the iconic memory. It is a quickly declining storage of visual data. It stores an image for a small duration which has been perceived by the person briefly. The second is the echoic memory. It is described as storage of sounds for short durations that have been heard briefly.[ 3 ] Moreover, haptic memory characterizes a database for touch stimuli.[ 11 ] WORKING MEMORY MODELThe “working memory model” was put forward by Baddeley and Hitch. According to them, working memory comprises the following: “the central executive, the phonological loop, and the visuospatial sketchpad” along with a multimodal episodic buffer.[ 12 ] Working memory and STM are often used interchangeably. In his paper, “the magical number 7 ± 2,” George A. Miller proved that the store of STM was 7 ± 2 items. However, contemporary approximations of the capability of STM are lesser, characteristically of the order of 4–5 items.[ 13 ] Nonetheless, this can be augmented using a method called chunking.[ 14 ] A few health behaviors like exercise can prevent forgetting from happening.[ 15 ] Forgetting was classified under various types by Paul Connerton: They are-prescriptive forgetting, planned obsolescence, formation of the new identity, repressive erasure, structural amnesia, annulment, and humiliated silence.[ 16 ] Retroactive interference can be defined as the phenomenon when new information or memories disturb the old information. Whereas proactive interference happens when old information disturbs the retrieval of new memories.[ 17 ] Trace decay elucidates memories that are stored in STM and LTM with the assumption that memories leave a trace in the central nervous system. Herman Ebbinghaus in 1913 proposed a forgetting curve. It theorizes the deterioration of memory retention over a period. It represents a curve showing how memory is lost over a period if there is no effort to preserve it. Amnesia is a phenomenon in which there is the problem in memory formation which can be due to trauma to the brain, certain diseases, or stressors.[ 18 ] It can be subdivided into retrograde amnesia and anterograde amnesia. Retrograde amnesia is the lack of ability to recover information that was attained before a specific time, typically before an accident or surgery.[ 19 ] Anterograde amnesia is the lack of ability to allocate new information from the short-term to the long-term store. It is observed that suffering from extended periods of amnesia after a trauma can be a prognostic indicator and that the improvement from the symptoms of concussion may take more than usual.[ 20 ] A scale consisting of 30 questions which is employed to gauge impairment inmemory is the Mini-Mental State Examination.[ 21 ] Another scale employed for detecting mild and early memory problems is the Montreal Cognitive Assessment.[ 22 ] Also utilized to detect memory issues is Addenbrooke's Cognitive Examination.[ 23 ] A scale consisting of 50 questions is the test of memory malingering. It is a visual memory recollection test that differentiates between true memory impairment and malingering. It has two learning trials and a retention trial after a delay which is optional.[ 24 ] The Wechsler Memory Scale segregates clinical categories. It can differentiate between the various clinical categories.[ 25 ] Advances in knowledge have resulted in the realization that memory is a very complex system. While the assessment of memory has greatly improved, we are only beginning to understand the underlying mechanisms. Financial support and sponsorshipConflicts of interest. There are no conflicts of interest. Our systems are now restored following recent technical disruption, and we’re working hard to catch up on publishing. We apologise for the inconvenience caused. Find out more: https://www.cambridge.org/universitypress/about-us/news-and-blogs/cambridge-university-press-publishing-update-following-technical-disruption We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings . Login Alert
 Article contentsThe new reality of memory, who made the past toxic, the past that never existed is here, ai renders the past conversational, ai agents of memory and the deadbot memory boom, glitch memories, conclusion: can the past be saved, ai and memory. Published online by Cambridge University Press: 11 September 2024 This paper is written at a tipping point in the development of generative AI and related technologies and services, which heralds a new battleground between humans and computers in the shaping of reality. Large language models (LLMs) scrape vast amounts of data from the so called ‘publicly available' internet, enabling new ways for the past to be represented and reimagined at scale, for individuals and societies. Moreover, generative AI changes what memory is and what memory does, pushing it beyond the realm of individual, human influence, and control, yet at the same time offering new modes of expression, conversation, creativity, and ways of overcoming forgetting. I argue here for a ‘third way of memory’, to recognise how the entanglements between humans and machines both enable and endanger human agency in the making and the remixing of individual and collective memory. This includes the growth of AI agents, with increasing autonomy and infinite potential to make, remake, and repurpose individual and collective pasts, beyond human consent and control. This paper outlines two key developments of generative AI-driven services: firstly, they untether the human past from the present, producing a past that was never actually remembered in the first place, and, secondly, they usher in a new ‘conversational’ past through the dialogical construction of memory in the present. Ultimately, developments in generative AI are making it more difficult for us to recognise the human influence on, and pathways from, the past, and that human agency over remembering and forgetting is increasingly challenged. Video AbstractThe advent of OpenAI's ChatGPT chatbot in 2022, and the recent rapid development and accessibility of AI Footnote 1 and related technologies and services, heralds a new battleground between humans and computers in the shaping of reality. This article asks, at this moment, what does AI's shaping of a new reality mean for what memory is and what memory does? The 2020s are marked by a convergence of huge computing power with the greatest memory dump in history. The digital participation of billions – producing, exposing, and sharing data and information about personal and public selves and experiences – has forged an astonishing shadow archive of us (Hoskins Reference Hoskins, Garde-Hansen, Hoskins and Reading 2009a ; Lagerkvist Reference Lagerkvist 2014 ). This shadow archive has been waiting for something to render it accessible, meaningful, and usable, on a planetary scale. That time has come, and that something is generative artificial intelligence. My central concern here is with the advent of generative AI. This is a step change in the creation of ‘new’ high-quality text, images, and other content such as voice recordings, based on the data they were trained on, via easily usable interfaces between humans and machines. Examples of generative AI include Open AI's Chat GPT, Footnote 2 Google's Gemini (formerly Bard) Footnote 3 , and Meta's Llama 3. Footnote 4 OpenAI's DALL-E2 Footnote 5 and Stability AI's Stable Diffusion Footnote 6 are specifically developed for generating images and art from text prompts. The sudden pervasiveness of generative AI services in the form of chatbots is utterly transforming the current and future relationship between humans, technologies, and the past, forging a new AI memory ecology. Footnote 7 Chatbots are computer programmes that respond to users' prompts with human-like replies, as though the individual is engaged in conversation. It is this process of prompting the creation of something in the present in relation to things written, spoken, experienced, and recorded in the past, that sounds like a useful and well-established definition of how human memory works. Many influential approaches in Memory Studies treat memory not as a fixed or static entity, but rather as an active process, whereby the past is reconstructed in the present. What is remembered is not some more-or-less accurate trace of the past, but rather a remaking, reimagining, or revisioning of past events that are significantly shaped by the context of recall (Bartlett Reference Bartlett 1932 ; Middleton and Brown Reference Middleton and Brown 2005 ; Schacter Reference Schacter 1996 ; Wertsch Reference Wertsch 2002 ). Yet today, it is increasingly AI that generates the context in which memory is produced, and even the memory itself. Virtual assistants, memory apps, and chatbots build on all the fragments of the past that have fed and trained large language models (LLMs) to offer a humanly intelligible response to questions or instructions in a new moment. Further exchanges in turn train or guide AI systems to offer answers more attuned to the prompts they are fed. Generative AI, and related technologies and services, both enable and endanger human agency in the making and the remixing of individual and collective memory . To help understand these transformations, I draw on and connect approaches from the cognitive and the social sciences to explore two key interrelated features of this remaking and the erasure of the past, that inform my overarching claim here as to a third way of memory Footnote 8 : (1) AI untethers the human past from the present. It produces a past that was never encoded into memory (never experienced) in the first place. We are now entangled in and confronted by a past that never existed (retrospective) and a future that never will exist (prospective). Through generating a past that never existed, AI breaks the relationship between the encoding of the past into memory, its storage, and its later retrievability. By ‘encoding’ I mean the ways in which humans perceive, get, and learn information, so that they can store and then later retrieve it as memory (McDermott and Roediger Reference McDermott, Roediger and Butler 2018 ). If information about an event or experience (an episode) is never encoded in the first place, then it will not be retrievable as memory. The stuff that AI generates from learning through discerning patterns in LLMs fed with huge amounts of data was never perceived or encoded as something intended to be retrievable as memory in the future. AI-prompted memories are generated rather than retrieved. The result is the creation of a new kind of past that never really existed before. As Bowker puts it, ‘It is the pleats and the folds of our data rather than their number that constitute their texture’ ( Reference Bowker and Karaganis 2007 , 24). We can no longer believe our own eyes when confronted by that which seems strangely familiar yet unreal: a kind of uncanny memory. (2) The ideal of a dialogical construction of memory is appropriated by AI's promise of enabling an eternal conversation with the past you, and the past others. The smartphone, as both connective and computational, as both portal and archive, continuously scaffolds our lives and memories (Barnier Reference Barnier 2010 ). A new generation of AI scaffolds memory in more immediate and personal ways through conversation. Whereas many digital memory apps and services focus on the taking, collecting, and repurposing of images and videos, the AI past feels eminently sociable as we can chat with it. Generative AI, through a range of apps and services, enables the living to ‘speak’ with the dead, including in the creation of a chatbot of you. This enables others to have ‘conversations' with you reimagining and remaking your memory beyond the grave. (3) Through newly creative modes of expression and formation of the past, AI creates a third way of memory , mixing the machinic and the human in new ways. AI overcomes unwanted forgetting, giving memory new hope, yet through its production of a past that never existed, it makes forgetting impossible; the AI agentic past is one without parameters in the machine's new capacity for forging and remaking long-term memory. Moreover, the third way of memory is to recognise the potential of AI to consort with, challenge, and also replace the agency of human remembering and forgetting. By human agency over memory, I mean an active, willed, functional, deliberating memory, seen as cognitive and as fundamentally part of human identity, that evolves with time and context in and of the present. The human production of the past is changed and threatened through the spread of AI agents, namely ‘AI models and algorithms that can autonomously make decisions in a dynamic world’ (Heikkilä Reference Heikkilä 2024 ). Zittrain ( Reference Zittrain 2024 ) uses the term ‘AI agents’ to describe ‘AIs that act independently on behalf of humans’ and cautions that the ‘routinization of AI that doesn't simply talk with us, but also acts out in the world, is a crossing of the blood–brain barrier between digital and analog, bits and atoms’. Although the term AI agents is not new, it is generative AI (using foundation-based models) that makes agents more universal in that they can learn from the world that humans interact with (Heikkilä Reference Heikkilä 2024 ). In this article, I present some of the rapidly emergent and experimental uses of AI which are collapsing the boundaries of memory in the head and memory in the wild (Barnier and Hoskins Reference Barnier and Hoskins 2018 ), making the past seem strange and uncanny. I first turn to explore key definitions and recent trends in the nature, uses, and effects of AI on human intelligence and experience, including the (toxic) kind of past that is being created. I then address the nature and consequences of the AI creation of a past that never existed and explore the emergent conversational means of memory. Next, I advance my argument of a third way of memory, examining the potential for human agency (consent and control) faced with a past increasingly made and remade through AI agents (services and bots). Finally, to offer some hope for the third way of memory, I consider ‘glitch memories’, as a case of the use of generative AI in overcoming forgetting and in giving human remembering new vitality and new hope. The Association for the Advancement of Artificial Intelligence defines AI as ‘the scientific understanding of the mechanisms underlying thought and intelligent behaviour and their embodiment in machines’. Footnote 9 AI represents a capacity for machines to solve problems and make rational decisions following procedures akin to human processes of learning by repetition and recognition (de-Lima-Santos and Ceron Reference de-Lima-Santos and Ceron 2021 , 14). Broadly speaking, it is ‘the tangible real-world capability of non-human machines or artificial entities to perform, task solve, communicate, interact, and act logically as occurs within biological humans’ (de Zúñiga et al. Reference de Zúñiga, Goyanes and Durotoye 2023 , 318). With the advent of ChatGPT and other chatbots and interfaces, there is a hugely advanced capacity for human-like interaction with these machines, to prompt and produce something that seems like human memory (above). This then begs the question as to the character, function, and finitude of this interactive or conversational production of the third way of memory. This is the production of a past somewhere between, as well as within, the human and the machine, with the latter possessing an increasingly powerful, complex, and opaque memory. At the same time, there is a form of past forged from an incredible archive accumulated through all our digital trails. How did we get to this point at which a technology can appropriate such an infinite memory? I argue that the relationship between media and memory fundamentally changed in the 2010s (Hoskins Reference Hoskins 2011 , Reference Hoskins 2013 , Reference Hoskins and Meade 2017a , Reference Hoskins and Hoskins 2017b ). A digital tsunami upended any sense of certainty once afforded by the trend in the more predictable ‘decay time’ (Hoskins Reference Hoskins 2013 ) of modern media. Today, it is clear there is a new monster of memory. Nothing is left alone anymore! Much of life is augmented, no encounter or experience seems unrecorded or unshared. And the services which promise instant or reliable deletion or erasure of sent messages, images, or videos, are often compromised by the fluidity and easy reproducibility of digital data and information (Hoskins Reference Hoskins, Hajek, Lohmeier and Pentzold 2015 ). Silence and contemplation are the enemies of the digitisation and datafication of everything (Lagerkvist Reference Lagerkvist 2022 ). Digital devices, apps, and services, increasingly penetrate and constitute everyday experience. Never have billions of individuals instantly produced, recorded, and shared so much data and information about themselves, their experiences, thoughts, preferences, and relationships. All of this then results in the most massive and complex record of the human past ever accumulated. As James Bridle ( Reference Bridle 2023 ) explains, The big tech companies have spent 20 years harvesting vast amounts of data from culture and everyday life, and building vast, energy-hungry data centres filled with ever more powerful computers to churn through it. What were once creaky old neural networks have become super-powered, and the gush of AI we're seeing is the result. To ask then about what memory is or does, especially of and from the 2010s, requires attention to the phenomenon of individual digital participation, for it is us that feeds the ghost that haunts us. The AI memory monster requires huge amounts of data scraped from the web for AI models that feed chatbots. However, when machines consume material made by other machines, what was once a more discernible human past becomes warped. For instance, M. Wong ( Reference Wong 2023 ) explains, ‘The problem with using AI output to train future AI is straightforward. Despite stunning advances, chatbots and other generative tools such as the image-making Midjourney and Stable Diffusion remain sometimes shockingly dysfunctional – their outputs filled with biases, falsehoods, and absurdities’. This is a matter of the further automated poisoining of the past. Thus, ‘model collapse’ according to Shumailov et al . is a ‘degenerative learning process where models start forgetting improbable events over time, as the model becomes poisoned with its own projection of reality’ ( Reference Shumailov, Shumaylov, Zhao, Gal, Papernot and Anderson 2023 , 2). They argue, therefore, that ‘the value of data collected about genuine human interactions with systems will be increasingly valuable in the presence of content generated by LLMs in data crawled from the Internet’ (Shumailov et al. Reference Shumailov, Shumaylov, Zhao, Gal, Papernot and Anderson 2023 , 1). Generative AI does not produce some kind of neutral or idealised past but instead accentuates inequalities, seeds disinformation, and violates personal privacy. Vallor for instance, writes of ‘The AI Mirror’, which renders ‘an image of our humanity arbitrarily both sanitized and polluted’ ( Reference Vallor 2024 , 35). Datasets of the required scale for LLMs to function efficiently can reproduce (and further hide) the processes of the reproduction of explicit images, violence, rape, pornography, sexism, racist, and ethnic slurs (Birhane et al. Reference Birhane 2021 ), as well as correcting towards heteronormativity (Zawacki Reference Zawacki 2023 ). For instance, as Bridle ( Reference Bridle 2023 ) suggests: AI image generators, in their attempt to understand and replicate the entirety of human visual culture, seem to have recreated our darkest fears as well. Perhaps this is just a sign that these systems are very good indeed at aping human consciousness, all the way down to the horror that lurks in the depths of existence: our fears of filth, death and corruption. We have never much liked or understood the generation that went before ours or why they did what they did (Lowenthal Reference Lowenthal 2012 ). As well as aiding memory, media through rotting, decay, and obsolescence, have assisted in the obscuring and denial of the worst of human nature, helping to smooth over the now unpalatable acts of our forebears (Hoskins Reference Hoskins, Wang and Hoskins in press ). It is digital participation, however, which produces a more unpredictable and stickier past, despite the creation of new rules and systems (‘moderation’) to filter, erase, and forget that which we cannot – or refuse to – confront in the present (Merrin and Hoskins Reference Merrin and Hoskins 2024 ). This product of mass digital participation is now feeding the training datasets for foundation models for an array of AI applications and services. Footnote 10 To attempt to stem the flow of LLM's generation of toxic and harmful content requires human input, for individuals to look at (and remember) the very worst of ‘the horror that lurks in the depths of existence’ (Bridle, above) so you don't have to. Low-paid workers in Kenya, for instance, were employed to screen out violence and sexual abuse in the development of OpenAI's ChatGPT. They claimed to have suffered trauma, anxiety, and depression in the process. Footnote 11 The AI generation or processing of this past requires a new layer of human screening. The more machines push humans out of the memory loop, the more humans are needed to make the past tolerable and sanctionable, within the mores of the present. This emergent battle over what the past means in the present, however, is fundamentally different from the long history of the conflict over memory. This is because generative AI is not only, or even mostly, representing or producing a past (good or bad) that was once lived, experienced, and shared. The AI past is, rather, being rendered through that collected, aggregated, mined, sifted, and sanitised, which has not been formed and made accessible in such a way before. In this way, I now turn to address the nature and consequences of the creation of a past that never existed. AI untethers the human past from the present; it produces a past never encoded into memory in the first place, so that we are now entangled in and confronted by a past that never existed. Individual digital participation is amassing an astonishing record of human experience, action, and movement, a fusion of communication and archive, used to watch, identify, monitor, exploit, persecute, target, and kill (Hoskins and Illingworth Reference Hoskins and Illingworth 2020 ). Our personal data are used increasingly without our consent. We do not and cannot possess a full grasp of the future uses and abuses our digital trails, seemingly private and public. Our own digitally enabled production of information and data about ourselves and others has potentially profound impacts on memory, consciousness, privacy, and agency. Fed by huge amounts of data – much of this our personal data – AI models are increasingly able not only to aggregate inputs at a scale beyond the capacity of a human mind but to generate novel artefacts from these aggregations (Magni et al. Reference Magni, Park and Manchi Chao 2023 , 2). And it is through individual and mass digital living in this century, through all our visible and invisible digital trails, that we have seeded (and conceded) the basis from which a past that never existed could be created. The way personal data about individuals through their uses of digital apps and platforms, is harvested, stored, reused, and sold, in often opaque ways, has been largely accepted as an unwanted but unavoidable trade-off between access to essential services, and a loss of privacy. ChatGPT's rolling out of ‘memory Footnote 12 ’ in early 2024 Footnote 13 is in effect a personalisation of this kind of surveillance, in the name of even more convenience, and enhanced memory. ChatGPT offers two kinds of memory service. The first is where you tell it to remember something specific about you, the second is where ChatGPT learns from you as you interact with it, ‘converse’ with it. Footnote 14 In this way, Chatbots and other AI-driven apps and platforms offer both ‘explicit’ and ‘implicit’ (Erll Reference Erll 2022 ; Schacter Reference Schacter 1987 ; Schacter and Graf Reference Schacter and Graf 1986 ) personal memory services. Many of the explicit memory services of the digital era are focused on the recording, storing, editing, organising, aggregating, and sharing of your photographs and videos. Some are more implicit, in that they offer an always-on service that both continuously records information about your actions and experience and render this accessible to you. Sensecam – a small sensor-equipped camera worn around the neck, which takes point-of-view images every time the camera moves, or every 30 seconds, was seen as pioneering in the digital era. For instance, Martin Conway, Shona Illingworth, and Catherine Loveday have experimented with Sensecam to help an amnesiac patient – Claire – to remember (Albano Reference Albano 2022 ; Illingworth Reference Illingworth 2015 ). AI platforms go further than digital devices (such as Sensecam) by increasingly blurring what might have been once thought of as implicit or explicit, unconscious or conscious, individual or collective, in the recording, remediating, and repurposing of experience. They do this in two related ways. The first is in delivering on what was once only an imaginary or even fantasy of total memory (which I return to consider below). The second, is in producing a past we don't need to humanly remember. For example, Personal.AI Footnote 15 offers a ‘digital version of you’, a kind of personal memory machine. It works through creating a time-bound structured dataset called a ‘memory stack’, made up of ‘memory blocks’. Each block is a unit of data in your life that is associated with a specific time, certain people, certain context, certain emotion, which are connected in the stack. I spoke with Suman Kanuganti, Personal.AI's founder and CEO, who explained: ‘It's almost like an automated database that is spinning for you, behind the scenes. The goal was to automate it to a degree where people don't have to worry about it’. Footnote 16 Personal.AI is built on the idea of an AI of you that is created from data and information that you feed it or allow for it to be fed. To grasp the transformation of AI's production of a past that never existed, it is useful to consider approaches to forgetting in the human sciences. A key distinction is made between forgetting owing to a failure to encode information in the mind into long-term memory, and forgetting due to a failure to retrieve from long-term memory (Erdelyi Reference Erdelyi 2006 ). In the first case, the past remains unencoded, so information about an event or experience never made it into memory in the first place. Perhaps this could include the unremarkable, the unnoticed, or the unrecorded. This might happen owing to a lack of attention. In the second case, forgetting occurs owing to a failure to retrieve information. This might happen due to the degrading of memory over time, motivated forgetting, silencing, or suppression. Generative AI turns this formula of forgetting on its head. Firstly, it enables the extraction and reinvention of a past that was never noticed, experienced, or initially encoded into memory. It does this by hoovering up our digitally scattered lives, including personal data, information, and images, and aggregating these inputs to shape novel artefacts. This is a kind of new, ‘new memory’ (Hoskins Reference Hoskins, Prescott and Wiggins 2023 ). Just a note on this idea of new memory. This is something that I have written about for over two decades. It highlights that remembering is a process that is inevitably shaped in and through the present and is thus entangled with the nature, forms, and control of the technologies, media, and institutions of the day. New memory also signals that the value afforded by individuals and societies to remembering and forgetting changes over time in relation to these same entanglements. AI-driven chatbots and services force a deeper entanglement of human memory in the tech of the day. But this memory is also new in that it is made in part at least from an unlived past, one that was never experienced to be humanly remembered in the first place. It is no wonder then today it feels uncanny. Secondly, digital participation leads to an overproduction and oversharing of information. Merrin ( Reference Merrin 2021 , 18) for example, argues: ‘Today, almost nothing escapes potential capture, shareability, and being added to the pornographic, hypervisible, hyperintimate collection of the museum of the real’. This feeds the long-established idea that if we collect and combine as much representational, archival, and circulatory technologies, discourses, and witnesses of the day, then this in some way secures the past. But the belief in, or fantasy of, the digital recording of everything, begs the question, of what exactly will be humanly accessible, by whom, and for how long? The answer lies in the fact that a new curator – AI – has arrived to take charge at the ‘museum of the real’. AI has reversed the formula of forgetting by both feeding off, as well as offering the reality of, a total memory. This is precisely what AI-enabled memory apps such as Rewind.AI and Personal.AI offer – to host or create your past so that it becomes something that you do not need to remember. A more provocative view is found in those who see scale as a solution to securing the future memory of today. Van der Werf and Van der Werf ( Reference van Der Werf and van der Werf 2022 , 987) for instance, write: ‘we need to embrace digital society and understand that information overload is a prerequisite for it to thrive’ and provide future generations with large quantities of data that they can then deliberate over and make decisions about. They see the potential for future generations to use ‘AI-based tools to construct multiple collective and individualised memories, histories, and truths. The more data we leave behind the richer their storytelling and memorialising will be’ (ibid). This view suggests a panacea of total memory, a free-for-all of infinite wisdom, where all lessons are learnt in the museum of the real. But having or seeming to possess the memory, for instance on digital devices, in the cloud, or in archives, is very different from accessing the memory, in other words being able to find, retrieve, understand, and use it. The past is surely as abundant as it has ever been but availability at scale is no guarantor of memory's security. Rather, it is overproduction and the complexity of the digital archive, personal or collective, that hampers accessibility (Hoskins Reference Hoskins, Wang and Hoskins in press ). AI challenges what accessibility means by providing a proxy accessible ‘memory’ in place of that which is unavailable. Google, for instance, in its privacy policy, states that it uses ‘information that's publicly available online or from other public sources to help train Google's AI models and build products and features using these foundational technologies. Footnote 17 ’ All of our public pasts are suddenly vulnerable at scale, the entire history of our digitally entangled selves, to the forging of a new and novel memory, a memory trained and extracted from the archive of us. The idea of a dialogical construction of memory is appropriated by AI's promise of enabling an eternal conversation with the past you, and the past others. There are many AI services designed to enable your loved ones to converse with your chatbot, from your memory, once you are no longer alive or able to communicate. The potential of AI has caught the attention of those keen to secure the end of living memory of a generation, seen as significant for having lived through or experienced an event of historical importance. For example, the testimony and memories of survivors of the atomic bombings on Hiroshima and Nagasaki, known as hibakusha, are seen in this way, not least for their capacity to warn of the horrific consequences of the use of nuclear weapons. As the average age of hibakusha is now over 85, museums, policy makers, and community organisations are actively considering the use of AI to extend the virtual presence of survivors, as this precious living collective memory vanishes. For example, Japan's national broadcaster NHK developed a project which in early 2022 recorded the testimony of Mrs. Yoshiko Kajimoto, a survivor aged 14 and 2.3 km from the hypocentre of the atomic bomb dropped by the US on Hiroshima on the 6th of August 1945. Mrs. Kajimoto had to answer 900 questions. Footnote 18 This database of recordings was developed through the AI system being taught the terms and historical background used in the war topics discussed in Mrs. Kajimoto's answers, including food, clothing, and shelter, to build a network of memories. Participants who come to the museum (or wherever the system is installed) can then pick from 99 selected questions to ask the life-size image projected in front of them which responds with the pre-recorded answers, as though they were engaging in a natural and live conversation with Mrs. Kajimoto. This service claims to offer a new, sustainable way to convey the experience of meeting an atomic bomb victim in person, to hear their testimonies directly. Footnote 19 Mrs. Kajimoto explained to me that in addition to the tiring five full days for a then 91-year-old interviewed so that her answers could be recorded, another principal challenge for this project was in attaining the approval of her sceptical family for her taking part. Footnote 20 In addition to her welfare, Mrs. Kajimoto family's concerns included the matter of how and by whom the recording might be used in the future. The ‘AI-based Testimony Response Device’ appears to be owned by the broadcaster NHK who commissioned the project, without Mrs. Kajimoto having any specific ownership of the recorded content. Footnote 21 This example is a tentative first step in Japan's official exploration of the use of AI to preserve the testimony of a generation that possesses the living experience and memory of the defining catastrophe of the US atomic bombing of the country almost 80 years ago. But the critical juncture and decision the country now faces is whether it will take the leap to using generative AI in its cause of preserving memory. The NHK project was conceived before the emergence of the creation of foundation models, which offer new kinds of creativity from training on a broad set of unlabeled data, with much wider application (Murphy Reference Murphy 2022 ). The latest generation of ‘Expressive AI Avatars’, for example, according to the company Synthesia, offer ‘dynamic and lifelike digital personas that blend the best of human and artificial intelligence into one seamless experience’. Footnote 22 Synthesia claims: Expressive Avatars don't just mimic human speech; they understand its context using our custom built EXPRESS-1 model. Whether the conversation is cheerful or sombre, our avatars adjust their performance, accordingly, displaying a level of empathy and understanding that was once the sole domain of human actors. Footnote 23 These features help deliver a convincing conversational, human-like, and trustworthy interaction. This all begs the question, what would happen if the living memory of a generation, seen as vital for their carriage of the first-hand witnessing and survival of a nodal event in defining a group or nation's history and identity, was suddenly unleashed to being remade through such rapidly accelerating reality-morphing technological change? A fear of this risk is causing stasis (in deploying AI at any kind of scale) in the institutions charged with preserving and protecting the collective memory of the hibakusha. Beyond the constrained responses of the avatar of Mrs. Kajimoto (above), advanced generative AI models trained to capture human-like features and open to more creative utterances and responses, would render the words and voices of a group of survivors defining a generation, open to a greatly uncertain fate and future. Certainly, generative AI adds to the capacity of digital networks and tools to transform the lifespan as well as the nature of memory of an individual and a society. The idea of the living memory of a generation is influential in the study of collective memory, especially entangled in interpretations of the work of Halbwachs, who argued ‘Our hold on the past … never exceeds a certain boundary, which itself shifts as the groups to which we belong enter a new phase of their existence. It is as if the memory needed to be unburdened of the growing mass of events that it must retain’ ( Reference Halbwachs 1980 , 120). The past used to dissipate, decline, and decay, in and through the media of the time, in mostly comprehensible ways and at predictable rates (Hoskins Reference Hoskins 2013 ). Today's AI memory is instead burdened anew in its infinite potential to be remade and repurposed. This creates a new impossibility of human forgetting, which I return to consider below. At the same time, given the seamless human and machinic way, promised above, who will be able to ascertain what was ever real or intended or consented to as a form of the remembering or remembered human subject? The hibakusha example is a case of a prospective form of arrangement, under more pressing consideration at the end of the living memory – of a person or a generation. However, it is AI's retrospective force on the past that is being used to an array of new ends, busily resurrecting the voices and images of the dead. For instance, the deepfaking of the voices of children killed in shootings in the United States (see below), is a new kind of AI-driven memory activism, part of a wider trend in the radicalisation of memory. AI cloning is not something restricted to those professionals trained in the technology but rather is moving rapidly into use by anyone, including those suffering from grief. Madeline de Figueiredo, a woman widowed in her twenties, used AI voice cloning on the digital recordings of her dead husband, Eli, to enable her to have one last ‘impossible conversation’ with him (de Figueiredo Reference De Figueiredo 2024 ). She writes of her experience: It's hard to explain the feeling that came with hearing Eli's voice speak the novel language after nearly two years of his absence … In some ways, it was worse than reality, and in other ways, it was better. I felt as though I had been knocked into a different dimension that was simultaneously disorienting and blissful. I wanted to linger forever in its potential and immediately eject myself from the self-deception. (ibid.) The astonishing potential of generative artificial intelligence, then, to create human-like features of interaction, including conversation, in the creation of forms and presence of those no longer living, jars against an underlying sense of reality of the present. At the same time, there is a seduction in the promise of media, accelerated in the digital era, that if only we can record everything then this will afford a future of greater access to and control over the past. However, the benefits of the amassing of a total memory also involve the creation of a memory that is no longer your own. These ideas of the creation and uses of an all-encompassing version of you appear to have become the reality of memory in the AI era. For example, apps like Heyday record everything we read online, then sort the information into accessible categories, allowing us to ‘outsource’ our memories to the AI (Agarwal Reference Agarwal 2023 ). Similarly, MindOS Memory Twin Footnote 24 lets us export our memories and thoughts into an ‘AI powered companion’, so you can ‘look back on any moment or even collaborate with your Twin using their unique memory of you'. Furthermore, the Personal.AI Footnote 25 app can be employed to create a ‘unique model that truly represents who you are’, including voice cloning features, allowing users to create a ‘complete’ version of themselves in the form of bespoke AI. The emergence of both the conversational past and the past that never existed not only reshapes and remixes individual human and collective memory but reconstitutes what it means and what is possible (and impossible) to forget. Who has access and control over this new relationship between human and machine, and its messy imbrication of a given individual's personal information and history in the computer model, as revealed through their conversation, is part of a new battle over the future of memory. I now turn to address the role of AI in transforming the relationship between remembering and forgetting, and the uncertain space in-between. The third way of memory recognises how the related growing autonomy and infinite potential of AI agents to be remade and repurposed, and to remember, semi or fully independently of human control and oversight, smashes the once more distinct individual, social, archival, and generational boundaries of the past (and influential on the conceptual and structural basis of the field of Memory Studies). AI agents are redefining the memory of a lifespan of an individual and a society, messing up ideas and assumptions as to the finitude of media and of memory, including notions of decay, obsolescence, and an array of established ‘forms of forgetting’ (Assmann Reference Assmann 2014 ). For instance, as Zittrain ( Reference Zittrain 2024 ) puts it: ‘There's simply no way to know what mouldering agents might stick around as circumstances change’. A striking trend in only the past two years to this end is in the deadbot Footnote 26 memory boom. Footnote 27 This is how chatbots and other AI-driven systems in enabling new forms of, and communication with, increasingly interactional representations of the dead, transform how individuals and societies are remembered and (not) forgotten. What will the past look like when deadbot memories both outnumber and outlive the human? Deadbots are AI-driven systems which emulate the personality, behaviour, voice, and/or appearance of persons already deceased, or created with the intent of emulating someone once they are dead. Hollanek and Nowaczyk-Basińska ( Reference Hollanek and Nowaczyk-Basińska 2024 ) define a deadbot as ‘an AI-enabled digital representation of a deceased individual created by a re-creation service’. Moreover, they are designed to be interactive with the living in a way that mimics how the deceased might have communicated. These include chatbots of you , be these audio or avatars created by consent, which may offer comfort to the bereaved and assuage trauma, Footnote 28 or deepfakes that are not, all bringing the living and the dead into new relationships. There has long been a concern with death in the digital age from the risks of inaccessibility (or obsolescence) of our digital selves (social media, images, messages, connections, and so on) for all those we leave behind who want to remember us. The so called ‘digital afterlife industry’ (Bassett Reference Bassett 2022 ; Kasket Reference Kasket 2019 ; Lagerkvist Reference Lagerkvist and Hoskins 2017 ; Öhman and Floridi Reference Öhman and Floridi 2018 ; Sisto Reference Sisto 2020 ; W.H. Wong Reference Wong 2023 ) became firmly established in the 2010s. This includes a huge array of services and platforms devoted to digital forms of memorialisation, and the persistence and preservation of the digital you, after your death. Footnote 29 Yet AI agents blur the boundaries between the living and the dead in new ways. In ‘the post-mortal condition’ as Öhman calls it, ‘the past and its dead have once again become present to us’ ( Reference Öhman 2024 , 15). The nature and finitude of living memory as it is embedded in the media of the day is rendered forever uncertain. It is important to also recognise the ease today through which any individual can create a deadbot of themselves or of a loved one, with minimal technical knowledge, through readily available and useable tools (De Figueiredo Reference De Figueiredo 2024 ). Deadbots created for memory preservation, are seen as different from ‘deepfakes’ (Meikle Reference Meikle 2022 ) which tend to refer to non-interactive synthetic media created for the purposes of misinformation or entertainment. Yet deepfakes also contribute to the deadbot memory boom, in the mass proliferation of convincing audio/visual versions of individuals, for instance, in reshaping a memory of them through putting new words into their mouth, from a past that never existed. There are two principal forms of deadbot, prospective and retrospective. The retrospective is how your digital participation has left an astonishing array of images, audio, and video of you scattered, to be found, connected, and used to feed generative AI transformer models, once you are no longer alive. The prospective, in contrast, requires us to focus on the living, in how and why individuals are training an avatar or chatbot to simulate them once they are dead. An example of a prospective deadbot platform is Hereafter.AI, which promises to ‘Let loved ones hear meaningful stories by chatting with the virtual you’. Footnote 30 Such services are increasingly app-based, with the user training the AI in response to prompts and questions from a ‘virtual interviewer’, as well as uploading images and videos. These services are often marketed as offering a kind of total memory and the idea of an eternal you (Lagerkvist Reference Lagerkvist and Hoskins 2017 ). An example of a retrospective form of deadbot is how some parents, who have lost their children in gun shootings in the United States, have used an AI voice generator to deepfake their dead children's voices for use in automated telephone calls to lawmakers as part of a campaign to push for greater gun control (Stern Reference Stern 2024 ). Although these cases may sound like science fiction, it is the reality of generative AI that is being deployed to weaponize memory today, of past voices and images of the dead, remade for the ends of the living. The deadbot memory boom is just one feature of a trend towards the end of human forgetting, in terms of the proliferation of versions of ourselves, and their potential to persist and change, after our death. The latest developments in generative AI suggest that models are forging a long-term memory, rather than just remembering exchanges within a given conversation. ‘ChatGPT Memory’ is a feature which stores a long-term memory of personal details that you share and so will be able to ‘personalise’ the conversation that you have with it. Footnote 31 This memory boom raises a huge number of legal, ethical, moral, social, technological, and political questions, including: Who might be responsible for your deadbot and for how long? What rights do you have and conversely how accountable are you – or at least your remaining family – to what the deadbot might say or reveal? And relatedly, how secure is your deadbot – is it vulnerable to perpetual hacking and inserting you in a past that never existed? Furthermore, as Kneese ( Reference Kneese 2023 ) argues, these services come at a cost to the living, in the human labour required for their operation, and the wider maintenance and the environmental costs in all forms of digital production. AI services not only render the boundaries of individual human memory uncertain, but they also reimagine and remix the relationship between individual and collective memory. For example, Wired magazine advises that if you share a ChatGPT account with friends or family, then you should turn off the Memory option, as: ‘With Memory activated, the chatbot might blend all the details from multiple interactions into one composite understanding of who the user is’ (Rogers Reference Rogers 2024 ). In this way, AI deepens the risks associated with what I call ‘grey memory’ (Hoskins and Halstead Reference Hoskins and Halstead 2021 ). Grey memory is how contemporary technologies push out of individual human reach a conscious, active, willed memory, through obscuring the risks of the ownership, use, access, costs, and finitude of digital data. The present and the future forged from all of the pasts of ours and other's digital participation and trails seem incredibly uncertain and unpredictable, despite the immediacy and convenience of our continuous use of apps, services, and platforms affording a sense of agency and control over our proliferating digital selves. The new conversational past (above) feels benign in its sociable affordances. To adopt the perspective of the third way of memory is to recognise how AI offers human memory new liberating imaginaries, forms, and horizons. The displaced, denied, and precious past can be revisioned, remade, and re-experienced, with astonishing ease, rejoining individual and collective memory. For instance, oral testimony and storytelling about past experiences, events, and relationships, vital to the formation of identity, belonging, the assuaging of trauma, ‘moving on’, as well as sheer nostalgia, can be translated into a new kind of anchoring vision and record. Footnote 32 This retrospective use of AI in shaping memory is also joined by a new prospective use of AI to generate imaginaries of what events and experiences might or should look like. There is a body of work in media and communication studies that considers how media (images, templates, narratives) are used to ‘premediate’ (Brown and Hoskins Reference Brown and Hoskins 2010 ; Erll Reference Erll, Erll and Nünning 2008 ; Grusin Reference Grusin 2004 , Reference Grusin 2010 ; Hoskins and O'Loughlin Reference Hoskins and O'Loughlin 2009 ) what is to come, to help make the future plausible and thus more manageable and controllable. Premediation in essence protects from future shocks through the development of a greater preparedness for what might come based on previous experience. The idea of retrospective and prospective memory also has a long tradition of work in the cognitive sciences. Footnote 33 Prospective memory is remembering to undertake a future task, whereas retrospective memory is the capacity to recall something that was previously learned (Shum and Fleming Reference Shum, Fleming, Kreutzer, DeLuca and Caplan 2011 ). Furthermore, Conway et al . ( Reference Conway, Loveday and Cole 2016 , 257) identify a ‘human remembering imagining system’. This is an extended form of consciousness that consists of memories of the recent past and images and expectations of the near future. Thus, memory, society, and culture constrain the range of possible futures by providing the context in which the future will most probably occur. Generative AI goes beyond premediating the future, or offering a context for it, by creating a record, a memory of it, in the wild. This includes the use of AI to generate photographs depicting future events such as weddings and birthdays with individuals who are unlikely to live to experience them because of their suffering from incurable illnesses (Bryan Reference Bryan 2024 ). In this way, human imaginaries are translated into human-machinic visionaries, outside the head and beyond the lifespan. I now turn to address a key example of this translation in the retrospective use of AI to generate photographs of a lost and fading past. In recent years, there has been increasing interest in how external stimuli in the form of digital tech are messing with the cognitive system. There has been a significant acceleration in psychological work testing the influence of media forms, technologies, and practices, including internet usage, on the capacity and reliability of human memory (Fawns Reference Fawns 2022 ; Marsh and Rajaram Reference Marsh and Rajaram 2019 ; Rajaram Reference Rajaram, Wang and Hoskins in press ; Risko et al. Reference Risko, Kelly, Lu, Pereira, Wang and Hoskins in press ; Storm et al. Reference Storm, Bittner, Yamashiro, Wang and Hoskins in press ; Stone and Zwolinski Reference Stone and Zwolinski 2022 ; Wang Reference Wang 2022 ). Experimental work in psychology by Henkel ( Reference Henkel 2014 ) for instance, shows that the act of photographing objects in a museum adversely affected the individual memory of the objects. Schacter ( Reference Schacter 2021 ) provides a commanding review of psychological work on the impact of media and technology on four of his ‘sins’ of memory, developing from his famous ‘Seven Sins of Memory’, notably seven categories of error or distortion of individual human remembering. The first three sins are ones of omission, so: transience, absentmindedness, and blocking. The next four are sins of commission: suggestibility, bias, persistence, and misattribution. Schacter ( Reference Schacter 2021 ), however, is sceptical as to the broader effects of shifts in media and technologies to change our general outlook of reality and the shape of our memories in what he labels as the ‘domain general’. In a Reference Schacter 2021 interview with me for Memory, Mind & Media , Schacter explained that there is no evidence to indicate that a broad effect of ‘engaging with and frequently experiencing social media, eventually fundamentally alter[ing] the underlying experience of memory in multiple domains’ is happening at this stage, although he also stated that it is not ‘inconceivable’ that this domain general effect could ‘eventually happen’. Footnote 34 Since this interview, as I remark above, it is generative AI which marks a step change, in the creation of high-quality text, images, and other content such as voice recordings, based on data they were trained on. Surely, this marks a transformation in the ‘domain general’ of memory, in the process of prompting the creation of something new, in relation to things written, spoken, experienced, and recorded in the past. The inputs of this new memory are our scattered ‘public’ selves as an astonishing archive that AI transformer models are ‘transforming’ into an emergent blended human-machinic version of what was, in the present. Another way to approach this question of the impact of external stimuli on human memory is to consider how media in a more fundamental way re-orient and distort our sense of reality. For example, Scott ( Reference Scott 2015 ) argues that digital technologies are reshaping what it is to be human to the extent that we are now ‘four dimensional’: The fourth dimension doesn't sit neatly above or on the other side of things. It isn't an attic extension. Rather, it contorts the old dimensions. And so it is with digitization, which is no longer a space in and out of which we clamber, via the phone lines. The old world itself has taken on, in its essence, a four-dimensionality … Increasingly, the moments of our lives audition for digitization. A view from the window, a meeting with friends, a thought, an instance of leisure of exasperation – they are all candidates, contestants even, for a dimensional upgrade ( Reference Scott 2015 : xv). This utter revisioning and reimagining of the world includes the past itself now susceptible to a participative ‘multitude’ (Hoskins Reference Hoskins and Hoskins 2017b ) continually remaking it. This is both in relation to the digital connectivity of the 2010s, and the emergent chatbot equipped, more sharded (Merrin and Hoskins Reference Merrin and Hoskins 2024 ) sense of the relationship between individuals and AI in the mid-2020s. The past is scraped, mined, and used to train AI models: an almighty aggregation, yes, but it is also splintered, fractured, and personalised, through millions of chatbot interactions. Perhaps connective memory (Hoskins Reference Hoskins 2011 ) has given way to sharded memory in the AI era. Both (connecting and sharding) trends contrast with an earlier (‘broadcast’) media era: ‘Whilst broadcast era production was standardised, uniform and finished, production in the post-broadcast era is marked by the rise of customisation, personalisation and “the perpetual beta”’ (Merrin Reference Merrin 2014 , 1). In the same way that memory is considered an ‘ongoing process’, Footnote 35 rather than something fixed or finite, it is equally productive to see media and communication as processual, as never-ending. This allows us to grasp the radical effects of AI more easily in giving the past an entirely new ‘dimension’ (in Scott's terms) or even ‘domain’ (in Schacter's terms) that remake the (ongoing) relationship between humans and machines. For me, this contributes to a third way of memory, that is, to realise how AI offers new contexts, new forms, and new imaginaries of the past at that intersection between human and machine. The faded, the fading, the blocked, the lesioned, the traumatised, the displaced, all the ways in which individuals and communities struggle to recover a lost past, to reimagine that for which there were no records, or which have been lost or destroyed. AI gives new hope to memory, to overcoming forgetting, but it also gives the past a new (synthetic) shape. For instance, the Barcelona-based design studio Domestic Data Streamers has run their ‘Synthetic Memories’ project since 2022. Footnote 36 Using generative AI image models, the studio works with displaced and immigrant communities to recreate photographs lost when families moved, or even of experiences and events that were never visually documented. A person describes an experience or event, and an engineer draws on each recollection to write a prompt for a model, which generates an image (Heaven Reference Heaven 2024 ), But unlike AI tools such as Google Magic Erasure Footnote 37 , the aim is not to create a sharply focused or idealised image of a memory. Rather, the studio used Stability AI generative image models DALL-E 2 and Stable Diffusion, from 2022, which can produce glitchy images, with misshapen faces and not quite formed bodies. The resulting images are more like the blurred imaginaries of individual memory in the head, instead of fully formed and fixed vision, often associated with the photograph. Pau Garcia, director of Domestic Data Streamers, kindly spoke with me and explained: What is important is not the clarity and realism, but the emotional truth that is embedded into it..it's got this more blurry, undefined quantum imaginary where things are transforming all the time. I think memory works a bit like this. It's not like it is fixed into something, it is something that is changing. You look at it and it has one shape, but you look again and it's a bit different. I think this glitchiness that is quite evident in the models of artificial intelligence is very helpful. Footnote 38 A good example is the experience of Carmen, aged 94 when she spoke to Pau Garcia in 2023. She recounted her story that when she was a six-year-old her mother used to pay another family, so they could enter a house in Barcelona and go up to its balcony. This balcony was important as it had a view facing la Modelo prison. During that time of the Spanish dictatorship, it was a political prison. Carmen's father was a doctor for the anti-fascist front and was being held there. The only way that they could see each other was from the balcony and the window of the prison. In response, Domestic Data Streamers generated a description, which prompted an old photograph of a mother and a daughter on a balcony in Barcelona. But it was not until Carmen saw a blurry, glitch version of this scene ( Figure 1 , below) that she could really recognise what was before her as triggering the memory.  Figure 1. Image of Carmen and her mother at a balcony looking across to la Modelo prison, Barcelona, as recreated by Domestic Data Streamers (image reproduced here with kind permission of Domestic Data Streamers Footnote 39 ). AI-produced glitch memories are like a reversal of ‘flashbulb memories’ (Brown and Kulik Reference Brown and Kulik 1977 ; Conway Reference Conway 1995 ; Reference Neisser Neisser 1982/2000 ), namely human memories recalled so vividly and with such clarity, they are said to possess a ‘photographic’ quality (Hoskins Reference Hoskins 2009b ). Glitch memories instead are rendered through the natural flaws and decline of human memory in interaction with generative image models’ emergent and messy translation of human prompts. The result looks like a distorted photograph, as though key identifying features have been smudged. Entangling the human and the technology in new relations to produce a negotiated and humanly recognisable vision of the past, is an example of the third way of memory, in overcoming human forgetting and giving remembering new potential and new hope. Generative AI changes what memory is and what memory does, pushing it beyond the realm of individual, human, influence, and control, yet at the same time offering new modes of expression, conversation, creativity, and ways of overcoming forgetting. This is part of a battleground between humans and computers in the shaping of reality. I have argued that it becomes increasingly important in the AI-defined era of the 2020s and beyond to see how individuals and societies, through new interfaces, are suddenly being confronted with an uncanny past, one that is familiar yet strange. This third way of memory of new human-machinic and individual-collective conflagrations offers an apparent panacea for new imaginaries of what was and what could have been, vital for new ways of putting the past to rest, for moving on, and for (re)discovering and remaking all that was precious yet since blocked or lost. Yet, these developments come at a profound human cost. The same technologies, which affect the workings of a conscious, active, willed memory, also obscure the risks of the ownership, use, access, costs, and finitude of the past, a greying of memory. Furthermore, the third way of memory involves a rapidly accumulating force of AI agents, with increasing autonomy and infinite potential to remake and repurpose individual and collective pasts, beyond human consent and control. An emergent retrospective and prospective deadbot memory boom, as I have called it, is collapsing the traditional individual, social, archival, and generational boundaries of the past, generating a new conflict over living memory. In very recent years, the battleground between humans and computers in the shaping of the reality of memory, is evident in policy, as well as in individual attempts, to wrest back human control. On the one hand, there are proponents of a ‘right to be forgotten’ Footnote 40 (we are haunted by our digital traces) and on the other those who fear a ‘digital Dark Age’ Footnote 41 (obsolescence of software and hardware rendering the digital past inaccessible). The European 2012 legislation on Right to Be Forgotten (RtbF), which was once considered to be a breakthrough in the preservation of personal information online, is no longer adequate, if it ever was (Hoskins Reference Hoskins, Ghezzi, Guimarães and Vesnić-Alujević 2014 ). This right (later enshrined in Article 17 of the EU General Data Protection Regulation) gives a person the right to have their personal data deleted in certain circumstances. Footnote 42 But in the AI era, it is difficult to imagine an effective form of a right to be forgotten. This is owing to the ambiguous status of ownership of digital content, including that which has been published or shared on public platforms with a limited grasp of consent for or the nature of its future use, including in its amalgamation with other content and in the feeding of AI's memory. There is an anti -autobiographical future in which it is impossible to extract your/self from the chatbot of you, and the chatbots of others. This begs the question – what can be done to protect and preserve all our pasts and our past selves increasingly vulnerable to AI's rendering available to all at a new scale? For instance, generative AI threatens the collective memory of the hibakusha (above) with the risks of impersonation, deep fakes, and testimony put to new ends, at a critical stage at the end of the life of a generation of survivors of atomic bombings. I have proposed that special status be afforded through Japanese legislation to protect the hibakusha and their living memory, so that their words, voices, and images are not endlessly remade, for all kinds of purposes, including those that they never intended or imagined. Footnote 43 Yet the AI past is fast outpacing the capacity of policymakers and regulators to legislate to offer some kind of human-scale stability and security to how and why individuals remember and forget. Moreover, the emergent third way of memory, I have argued, makes for an irresistible past, whose shape, provenance, agency, ownership, uses, and abuses, are wildly at stake. AcknowledgmentsI am very grateful for the constructive and detailed help and advice on this article from three anonymous reviewers, and I am indebted to Amanda Barnier, for her meticulous steering of this work through the reviewing process and for her own extensive feedback and advice. I am also grateful for the generous detailed feedback and advice I have received from Katerina Linden, Anthony Downey, Danny Pilkington, William Merrin, Amanda Lagerkvist, Martin Pogacar, Leighton Evans, Tim Peacock, and Geoffrey C. Bowker. Thank you also to those who gave up their time to speak with me about their work and experiences, including Suman Kanuganti, CEO of Personal.AI, Pau Garcia (Founder and Director) and Self Else (AI researcher) at Domestic Data Streamers, Mrs. Yoshiko Kajimoto, and Dan Schacter. This work has also benefitted from feedback from many talks and keynotes I have given over the past few years, including most recently: Keynote ‘AI & Memory’: Achievements and Perspectives of Cultural and Social Memory Research Conference of the Research Network ‘Handbuch Sozialwissenschaftliche Gedächtnisforschung’ and the Working Group ‘Social Memory, Remembering and Forgetting’ of the German Sociological Association (DGS), Technical University of Berlin, 28 September 2023; Public Lecture: ‘A-bombings Memory and Peace’: The Hiroshima Peace Memorial Museum, 15 March 2024, and ‘AI & Memory’ presentation to the AI Horizons: Navigating the Intersection of Artificial Intelligence and the Humanities, Symposium, Swansea University, 20 June 2024. Thank you to Gerd Sebald, Luli van der Does, and to Leighton Evans, respectively, for organising these events and for inviting me. This work has not received any specific grant from any funding agency, commercial or not-for-profit sectors. As Co-Editor-in-Chief of the journal in which this article appears, I declare that I was not involved in the selection of, or communication with, the three peer reviewers for the external double-blind peer review of this research article, which was managed by Professor Amanda Barnier. Andrew Hoskins is a Professor of Global Security at the University of Glasgow, UK. From January 2025 he will take up a Chair in AI, Memory and War, at the University of Edinburgh, UK. He is the founding Co-Editor-in-Chief of the Journal of Memory, Mind & Media. He is the author/editor of 10 books, including Radical War: Data, Attention & Control in the Twenty-First Century (Hurst/OUP 2022, with Matthew Ford) and The Remaking of Memory in the Age of the Internet and Social Media (OUP 2024, co-edited with Qi Wang). 1 The term ‘artificial intelligence’ (AI) many trace to the US computer scientist John McCarthy (1927–2011) and his 1955 definition of AI as ‘the science and engineering of making intelligent machines, especially intelligent computer programs’ (McCarthy, Reference McCarthy 2007 ). 2 https://openai.com/ 3 https://gemini.google.com/ 4 https://llama.meta.com/ 5 https://openai.com/dall-e-2 6 Stable diffusion online ( stablediffusionweb.com ) 7 I use term ‘memory ecology’ to emphasise how remembering and forgetting are processes entangled with the technologies and media of a given time and environment (Brown and Hoskins, Reference Brown and Hoskins 2010 ; Hoskins, Reference Hoskins and Meade 2017a , Reference Hoskins, Wang and Hoskins in press ). 8 I first used this term in an invited paper: Andrew Hoskins (2019) Public Lecture, ‘The Algorithmic Past: The Third Way of Memory’, POEM Network, University of Glasgow, UK, 26 March, https://www.poem-horizon.eu/public-talk-the-algorithmic-past-the-third-way-of-memory/ . 9 https://aaai.org/about-aaai/ 10 https://www.accessnow.org/what-you-need-to-know-about-generative-ai-and-human-rights/ 11 https://www.wsj.com/articles/chatgpt-openai-content-abusive-sexually-explicit-harassment-kenya-workers-on-human-workers-cf191483 12 https://openai.com/blog/memory-and-new-controls-for-chatgpt 13 https://openai.com/blog/memory-and-new-controls-for-chatgpt 14 https://www.theverge.com/2024/2/13/24071106/chatgpt-memory-openai-ai-chatbot-history 15 https://www.personal.ai/ 16 Suman Kanuganti, CEO of Personal.AI, interviewed by Andrew Hoskins, 20 May 2023. 17 https://policies.google.com/privacy (version effective 28 March 2024). 18 https://hiroshimaforpeace.com/en/passing-down-the-memories-of-that-day-to-countless-generations-to-come-an-ai-based-atomic-bomb-testimony-response-device/ 19 https://hiroshimaforpeace.com/en/passing-down-the-memories-of-that-day-to-countless-generations-to-come-an-ai-based-atomic-bomb-testimony-response-device/ 20 Mrs. Yoshiko Kajimoto interviewed by Andrew Hoskins with Luli van der Does, 14 March 2024, Hiroshima. This research project is a collaboration with Dr. van der Does, The Center for Peace, Hiroshima University exploring remembering and forgetting of the atomic bombings of Japan. 21 Footnote Ibid . 22 https://www.synthesia.io/post/expressive-avatars-powered-by-synthesias-new-express1-model-are-here 23 Footnote Ibid . 24 https://www.producthunt.com/products/mindos 25 https://www.personal.ai/your-true-personal-ai 26 There are a range of alternative terms for ‘deadbot’ being used in different disciplines and in news stories (see also Hollanek and Nowaczyk-Basińska ( Reference Hollanek and Nowaczyk-Basińska 2024 ). Savin-Baden ( Reference Savin-Baden 2022 , 143–144), for example, uses the term ‘griefbot’, which she defines as that which is ‘created using a person's digital legacy from social media content, text messages and emails’. My defining and using of ‘deadbot’ here are to highlight the conditions and potential consequences following the generative AI turn, including in the rapidly developing potential for AI agents to make and remake memory today and in the future. 27 For an overview of work on ‘memory booms’, see Hoskins and Halstead ( Reference Hoskins and Halstead 2021 ). 28 https://www.everly.care/ 29 https://www.thedigitalbeyond.com/online-services-list/ 30 https://www.hereafter.ai/ 31 https://openai.com/blog/memory-and-new-controls-for-chatgpt 32 See, for example, ‘Replika’ a personal AI ‘companion’, https://replika.com . 33 See also Tenenboim-Weinblatt's ( Reference Tenenboim-Weinblatt 2013 ) essay in Communication Studies on ‘mediated prospective memory’ exploring news media and journalism's memory work. 34 Dan Schacter interviewed by Andrew Hoskins for the Journal of Memory, Mind & Media , 29 June 2021 https://www.youtube.com/watch?v=KRnV4WiadqA&t=4s 35 https://www.istorex.org/post/jeffrey-k-olick-memory-is-not-a-thing-it-is-not-an-object-memory-is-an-ongoing-process-1 36 https://www.domesticstreamers.com/art-research/work/synthetic-memories/ 37 https://www.google.com/intl/en_uk/photos/editing/ 38 Pau Garcia, interviewed by Andrew Hoskins, 28 May 2024. 39 https://www.domesticstreamers.com/art-research/work/synthetic-memories/ 40 https://gdpr.eu/right-to-be-forgotten/ 41 https://www.bbc.co.uk/news/science-environment-31450389 42 https://gdpr.eu/article-17-right-to-be-forgotten/ 43 Andrew Hoskins (2024) Public Lecture: ‘Forgetting Hiroshima: The crisis of living memory in the AI era. Hiroshima Peace Memorial Museum, Hiroshima, Japan, 15 March.  Figure 1. Image of Carmen and her mother at a balcony looking across to la Modelo prison, Barcelona, as recreated by Domestic Data Streamers (image reproduced here with kind permission of Domestic Data Streamers 39 ).  No CrossRef data available. View all Google Scholar citations for this article. Save article to KindleTo save this article to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle . 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Human Memory V. Computer Memory 3 Pages 817 WordsThe mental ability of humans is a characteristic unparalleled in the natural world. We are alienated from other organisms by our complex and multifaceted thought process. In fact, humans are so unique in the animal kingdom that our greatest intellectual rival may be a nonliving object of our own creation. Since the conception of the computer, an atmosphere of competition has surrounded the machine. Having had no clear opposition to the excellence of human design for thousands of years, the notion of a machine carrying out similar functions can be quite frightening. Countless images of destructive supercomputers have been conjured up in books and movies. The popularity of such ideas may have something to do with a real human fear and uncertainty. Are we, as humans, still smarter than our creation? Are we still special? The primary purpose of computers has always been memory. Thus, their design tends to mimic the human brain. Facts and data are stored in chips that perform the same tasks as cells. These chips or circuits store information and allow the computer to carry out vital operations. The wires in a computer act like the neurons in a brain. They are the pathways for electrical impulses and are therefore responsible for communication between regions. For each circuit or wire in a computer, there are millions of cells or neurons in a brain. The complicated system of computer components can be viewed as a simplified mock-up of the even more complex human brain. Computers also have a system for storing memory similar to humans. The first step in the process is short-term storage. For a person, this stage occurs the instant new information is perceived. For a short time after hearing or seeing something, the knowledge will be retained whether or not the individual has a desire to remember it. For a computer, this is the time when data is first entered by a human. This data is recorded and maintained until it is either f... Continue reading this essay Continue reading Page 1 of 3 More Essays: |
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Human Memory Compared to Computer. Human brains' cognitive functions and abilities of the neural system are enormous comparing to the majority of other mammals. Myriads of intricate connections of neurons and the plasticity of various brain structures underline humankind's theoretical and practical achievements.
In some ways, human memory and computer memory are similar. For example, some general characteristics of human short-term memory resemble those of a computer's random access memory (RAM). As discussed elsewhere on this site, human short-term memory is volatile and has a limited capacity. Computer RAM has essentially the same characteristics.
A typical computer runs on about 100 watts of power. A human brain, on the other hand, requires roughly 10 watts. That's right, your brain is ten times more energy-efficient than a computer. The brain requires less power than a lightbulb. We may not be the brightest bulbs in the box, but then again, we don't have to be.
Somehow or another, human memory and PC memory are comparative. For instance, some broad qualities of human transient's memory take after those of a PC's arbitrary access memory (Smash). Human transient memory is unstable and has a restricted limit. PC Smash has basically similar qualities.
Human memories are stretchier, less reliable, and generally weirder than your computer may lead you to believe, as Shohamy and her peers in psychology and neuroscience have found. Here are a few of the ways: There are different types of memories. When you talk about your memories, it's likely in the sense of a flashback — a sensory-based ...
Both the memory chips on a computer and the human brain store information which makes them operate. For a single wire in a memory chip, millions of neutrons exist to carry pulses in the brain. Computer memory is measured in megabytes but human memory is estimated to be 2.5 million gigabytes (Nishihara et al., 2016).
memory" proposed herein is ofa unique design that could be physically built to give a computer a "human-like" memory, and furthermore, it is intended to serve as a behavioral model for human and animal memory. A simplified block diagram ofa cognitive memory system is shown in Fig. 1. The memory of Fig. 1 is divided into segments. Each
1. Introduction. The human brain is like a power ful computer tha t stores. our memory and controls how we as humans think and. react. It has evolved over time and features some. incredibly ...
Introduction. Human memory has long been a subject of research and scientific debates, and biology, psychology, and neuroscience are still reaching new frontiers in studying this phenomenon. The development of computer technology in the 1950s and 1960s has advanced scientific understanding and drew a parallel between computer and brain processes.
As you can see in Figure 2, long-term memory can be divided into two major categories of memory types: explicit memory and implicit memory, which can be further divided into multiple sub-types: semantic, episodic, procedural, priming, and conditioning memory. EXPLICIT MEMORY. The first form of long-term memory we will discuss is explicit memory.
Metrics. In this special issue of Nature Neuroscience, we feature an assortment of reviews and perspectives that explore the topic of learning and memory. Learning new information and skills ...
Dr. Ninad shows you what makes a human's memory different from a computer's memory.Visit the Inner Labs website https://www.innerlabs.org/Follow us on Face...
The goal of the present ar cle is to present and discuss a. series of open ques ons relat ed to major topics on human memory research that can be addressed by future research. The. topics covered ...
There are three main processes that characterize how memory works. These processes are encoding, storage, and retrieval (or recall). Encoding. Encoding refers to the process through which information is learned. That is, how information is taken in, understood, and altered to better support storage (which you will look at in Section 3.1.2).
What is Working Memory? An Introduction and Review. Working memory is the small amount of information that can be held in mind and used in the execution of cognitive tasks, in contrast with long-term memory, the vast amount of information saved in one's life. Working memory is one of the most widely-used terms in psychology. It has often been connected or related to intelligence, information ...
2. Memory as adaptive-reconstructive. Memory represent an information processing system, that we often compare to a computer .However, despite the analogy of human memory to a computer, evidence from neuroscience has shown that a highly complex and interconnected neural network that relies on synaptic activation and modification is involved in memory .
And now scientists are finally zeroing in on why. A recent study in. Frontiers in Psychology. monitored brain activity in students taking notes and found that those writing by hand had higher ...
Abstract. This paper explores memory from a cognitive neuroscience perspective and examines associated neural mechanisms. It examines the different types of memory: working, declarative, and non-declarative, and the brain regions involved in each type. The paper highlights the role of different brain regions, such as the prefrontal cortex in ...
Short-term memories last seconds to hours, while long-term memories last for years. We also have a working memory, which lets us keep something in our minds for a limited time by repeating it ...
Types of Memory. Human memory is a powerful mental process that has many implications in life and how you experience things, from remembering meaningful events to enabling you to execute tasks and achieve goals. In essence, human memory has three facets: sensory memory, short-term memory and long-term memory.
Memory is the process of retaining of knowledge over a period for the function of affecting future actions.[] From a historical standpoint, the area of memory research from 1870 to 1920 was focused mainly on human memory.[] The book: The Principles of Psychology written by famous psychologist William James suggested that there is a difference between memory and habit.[]
The new reality of memory. The advent of OpenAI's ChatGPT chatbot in 2022, and the recent rapid development and accessibility of AI Footnote 1 and related technologies and services, heralds a new battleground between humans and computers in the shaping of reality. This article asks, at this moment, what does AI's shaping of a new reality mean for what memory is and what memory does?
APA MLA Chicago. Human Memory V. Computer Memory essays The mental ability of humans is a characteristic unparalleled in the natural world. We are alienated from other organisms by our complex and multifaceted thought process. In fact, humans are so unique in the animal kingdom that our greatest intellectual r.