Intelligent problem-solvers externalize cognitive operations

The use of forward models (mechanisms that predict the future state of a system) is well established in cognitive and computational neuroscience. We compare and contrast two recent, but interestingly divergent, accounts of the place of forward models in the human cognitive architecture. On the Auxiliary Forward Model (AFM) account, forward models are special-purpose prediction mechanisms implemented by additional circuitry distinct from core mechanisms of perception and action. On the Integral Forward Model (IFM) account, forward models lie at the heart of all forms of perception and action. We compare these neighbouring but importantly different visions and consider their implications for the cognitive sciences. We end by asking what kinds of empirical research might offer evidence favouring one or the other of these approaches

Failure of Working Memory Training to Enhance Cognition or Intelligence

Fluid intelligence is important for successful functioning in the modern world, but much evidence suggests that fluid intelligence is largely immutable after childhood. Recently, however, researchers have reported gains in fluid intelligence after multiple sessions of adaptive working memory training in adults. The current study attempted to replicate and expand those results by administering a broad assessment of cognitive abilities and personality traits to young adults who underwent 20 sessions of an adaptive dual n-back working memory training program and comparing their post-training performance on those tests to a matched set of young adults who underwent 20 sessions of an adaptive attentional tracking program. Pre- and post-training measurements of fluid intelligence, standardized intelligence tests, speed of processing, reading skills, and other tests of working memory were assessed. Both training groups exhibited substantial and specific improvements on the trained tasks that persisted for at least 6 months post-training, but no transfer of improvement was observed to any of the non-trained measurements when compared to a third untrained group serving as a passive control. These findings fail to support the idea that adaptive working memory training in healthy young adults enhances working memory capacity in non-trained tasks, fluid intelligence, or other measures of cognitive abilities.National Institutes of Health (U.S.) (Blueprint for Neuroscience Research (T90DA022759/R90DA023427)United States. Defense Advanced Research Projects Agency (government contract no. NBCHC070105)United States. Dept. of Defense (National Defense Science and Engineering Fellowship)Massachusetts Institute of Technology (Sheldon Razin (1959) Fellowship

Exploring the relationship between video game expertise and fluid intelligence

Hundreds of millions of people play intellectually-demanding video games every day. What does individual performance on these games tell us about cognition? Here, we describe two studies that examine the potential link between intelligence and performance in one of the most popular video games genres in the world (Multiplayer Online Battle Arenas: MOBAs). In the first study, we show that performance in the popular MOBA League of Legends' correlates with fluid intelligence as measured under controlled laboratory conditions. In the second study, we also show that the age profile of performance in the two most widely-played MOBAs (League of Legends and DOTA II) matches that of raw fluid intelligence. We discuss and extend previous videogame literature on intelligence and videogames and suggest that commercial video games can be useful as 'proxy' tests of cognitive performance at a global population level

An N-methyl-d-aspartate receptor agonist facilitates sleep-independent synaptic plasticity associated with working memory capacity enhancement

Working memory (WM) capacity improvement is impacted by sleep, and possibly by N-methyl-D-aspartate (NMDA) agonists such as D-cycloserine (DCS), which also affects procedural skill performance. However, the mechanisms behind these relationships are not well understood. In order to investigate the neural basis underlying relationships between WM skill learning and sleep, DCS, and both sleep and DCS together, we evaluated training-retest performances in the n-back task among healthy subjects who were given either a placebo or DCS before the task training, and then followed task training sessions either with wakefulness or sleep. DCS facilitated WM capacity enhancement only occurring after a period of wakefulness, rather than sleep, indicating that WM capacity enhancement is affected by a cellular heterogeneity in synaptic plasticity between time spent awake and time spent asleep. These findings may contribute to development, anti-aging processes, and rehabilitation of higher cognition

Working Memory Underpins Cognitive Development, Learning, and Education

Working memory is the retention of a small amount of information in a readily accessible form. It facilitates planning, comprehension, reasoning, and problem-solving. I examine the historical roots and conceptual development of the concept and the theoretical and practical implications of current debates about working memory mechanisms. Then I explore the nature of cognitive developmental improvements in working memory, the role of working memory in learning, and some potential implications of working memory and its development for the education of children and adults. The use of working memory is quite ubiquitous in human thought, but the best way to improve education using what we know about working memory is still controversial. I hope to provide some directions for research and educational practice

From short-term store to multicomponent working memory: The role of the modal model

The term “modal model” reflects the importance of Atkinson and Shiffrin’s paper in capturing the major developments in the cognitive psychology of memory that were achieved over the previous decade, providing an integrated framework that has formed the basis for many future developments. The fact that it is still the most cited model from that period some 50 years later has, we suggest, implications for the model itself and for theorising in psychology more generally. We review the essential foundations of the model before going on to discuss briefly the way in which one of its components, the short-term store, had influenced our own concept of a multicomponent working memory. This is followed by a discussion of recent claims that the concept of a short-term store be replaced by an interpretation in terms of activated long-term memory. We present several reasons to question these proposals. We conclude with a brief discussion of the implications of the longevity of the modal model for styles of theorising in cognitive psychology

Working memory is (almost) perfectly predicted by g

This article analyzes if working memory (WM) is especially important to understand g. WM comprises the functions of focusing attention, conscious rehearsal, and transformation and mental manipulation of information, while g reflects the component variance that is common to all tests of ability. The centrality of WM in individual differences in information processing leads to some cognitive theorists to equate it with g. There are several studies relating WM with psychometric abilities like reasoning, fluid intelligence, spatial visualization, spatial relations, or perceptual speed, but there are very few studies relating WM with g, defined by several diverse tests. In three studies, we assessed crystallised intelligence (Gc), spatial ability (Gv), fluid intelligence (Gf), and psychometric speed (Gs) using various tests from the psychometric literature. Moreover, we assessed WM and processing speed (PS). WM tasks involve storage requirements, plus concurrent processing. PS tasks measure the speed by which the participants take a quick decision about the identity of some stimuli; 594 participants were tested. Confirmatory factor analyses yielded consistently high estimates of the loading of g over WM (96 on average). WM is the latent factor best predicted by g. It is proposed that this is so because the later has much in common with the main characteristic of the former. </p