For many years, the game of chess has provided an invaluable task environment for research on cognition, in particular on the differences between novices and experts and the learning that removes these differences, and upon the structure of human memory and its paramaters. The template theory presented by Gobet and Simon based on the EPAM theory offers precise predictions on cognitive processes during the presentation and recall of chess positions. This article describes the behavior of CHREST, a computer implementation of (...) the template theory, in a memory task when the presentation time is varied from one second to sixty, on the recall of game and random positions, and compares the model to human data. Strong players are better than weak players in both types of positions, especially with long presentation times, but even after brief presentations. CHREST predicts the data, both qualitatively and quantitatively. Strong players' superiority with random positions is explained by the large number of chunks they hold in LTM. Their excellent recall with short presentation times is explained by templates, a special class of chunks. CHREST is compared to other theories of chess skill, which either cannot account for the superiority of Masters in random positions or predict too strong a performance of Masters in such positions. (shrink)

This paper presents a computational model of the way humans inductively identify and aggregate concepts from the low-level stimuli they are exposed to. Based on the idea that humans tend to select the simplest structures, it implements a dynamic hierarchical chunking mechanism in which the decision whether to create a new chunk is based on an information-theoretic criterion, the Minimum Description Length (MDL) principle. We present theoretical justifications for this approach together with results of an experiment in which participants, exposed (...) to meaningless symbols, have been implicitly encouraged to create high-level concepts by grouping them. Results show that the designed model, called hereafter MDLChunker, makes precise quantitative predictions both on the kind of chunks created by the participants and also on the moment at which these creations occur. They suggest that the simplicity principle used to design MDLChunker is particularly efficient to model chunking mechanisms. The main interest of this model over existing ones is that it does not require any adjustable parameter. (shrink)

First, we discuss issues raised with respect to the Theory of Event Coding (TEC)'s scope, that is, its limitations and possible extensions. Then, we address the issue of specificity, that is, the widespread concern that TEC is too unspecified and, therefore, too vague in a number of important respects. Finally, we elaborate on our views about TEC's relations to other important frameworks and approaches in the field like stages models, ecological approaches, and the two-visual-pathways model. Footnotes1 We acknowledge the precedence (...) of both Freud¹s Instincts and Their Vicissitudes (1915) and Neisser¹s Stimulus Information and Its Vicissitudes (a term Neisser borrowed from Freud for his monograph “Cognitive psychology,” 1967). (shrink)

In the mid-1980s, Dario Donatelli participated in a laboratory study of the effects of around 800 h of practice on digit-span and increased his digit-span from 8 to 104 digits. This study assessed changes in the structure of his memory skill after around 30 years of essentially no practice on the digit-span task. On the first day of testing, his estimated span was only 10 digits, but over the following 3 days of testing it increased to 19 digits. Further analyses (...) of his recall performance and verbal reports identified which mechanisms of the original memory skill he could retrieve or reacquire over the 3 days of practice. We discuss theoretical implications for the retention of skilled memory performance, the effects of age-related changes in memory on it, and for the future study of the effects of disuse on exceptional performance and complex skill. (shrink)

In this study we use a computational model of language learning called model of syntax acquisition in children (MOSAIC) to investigate the extent to which the optional infinitive (OI) phenomenon in Dutch and English can be explained in terms of a resource-limited distributional analysis of Dutch and English child-directed speech. The results show that the same version of MOSAIC is able to simulate changes in the pattern of finiteness marking in 2 children learning Dutch and 2 children learning English as (...) the average length of their utterances increases. These results suggest that it is possible to explain the key features of the OI phenomenon in both Dutch and English in terms of the interaction between an utterance-final bias in learning and the distributional characteristics of child-directed speech in the 2 languages. They also show how computational modeling techniques can be used to investigate the extent to which cross-linguistic similarities in the developmental data can be explained in terms of common processing constraints as opposed to innate knowledge of universal grammar. (shrink)

Increasing working memory (WM) capacity is often cited as a major influence on children's development and yet WM capacity is difficult to examine independently of long‐term knowledge. A computational model of children's nonword repetition (NWR) performance is presented that independently manipulates long‐term knowledge and WM capacity to determine the relative contributions of each in explaining the developmental data. The simulations show that (a) both mechanisms independently cause the same overall developmental changes in NWR performance, (b) increase in long‐term knowledge provides (...) the better fit to the child data, and (c) varying both long‐term knowledge and WM capacity adds no significant gains over varying long‐term knowledge alone. Given that increases in long‐term knowledge must occur during development, the results indicate that increases in WM capacity may not be required to explain developmental differences. An increase in WM capacity should only be cited as a mechanism of developmental change when there are clear empirical reasons for doing so. (shrink)

We have developed a process model that learns in multiple ways while finding faults in a simple control panel device. The model predicts human participants' learning through its own learning. The model's performance was systematically compared to human learning data, including the time course and specific sequence of learned behaviors. These comparisons show that the model accounts very well for measures such as problem‐solving strategy, the relative difficulty of faults, and average fault‐finding time. More important, because the model learns and (...) transfers its learning across problems, it also accounts for the faster problem‐solving times due to learning when examined across participants, across faults, and across the series of 20 trials on an individual participant basis. The model shows how learning while problem solving can lead to more recognition‐based performance, and helps explain how the shape of the learning curve can arise through learning and be modified by differential transfer. Overall, the quality of the correspondence appears to have arisen from procedural, declarative, and episodic learning all taking place within individual problem‐solving episodes. (shrink)

Deep Neural Networks (DNNs) are popular for classifying large noisy analogue data. However, DNNs suffer from several known issues, including explainability, efficiency, catastrophic interference, and a need for high‐end computational resources. Our simulations reveal that psychologically‐inspired symbolic deep networks (SDNs) achieve similar accuracy and robustness to noise as DNNs on common ML problem sets, while addressing these issues.

This article presents a process account of some typicality effects and related similarity-dependent accuracy and response time phenomena that arise in the context of supervised concept acquisition. We describe Symbolic Concept Acquisition (SCA), a computational system that acquires and activates category prediction rules. In contrast to gradient representations, SCA performs by probing for prediction rules in a series of discrete steps. For learning new rules, it acquires general rules but then incrementally learns more specific ones. In describing SCA, we emphasize (...) its functionality in terms of accuracy and efficiency and motivate its design within the set of symbolic mechanisms and memory structures defined by the Soar architecture (Laird, Newell & Rosenbloom, 1987). For replicating human behavior, we first show how SCA exhibits some typicality effects in the course of learning responding faster and more accurately to more typical test examples. Then, using data from human experiments, we evaluate SCA's qualitative predictions on accuracy and response time on individual dataset instances. We show how SCA's predictions correlate with human data across three experimental conditions concerning the effect of instruction on learning strategy. (shrink)

Human and machine discovery are gradual problem-solving processes of searching large problem spaces for incompletely defined goal objects. Research on problem solving has usually focused on search of an instance space (empirical exploration) and a hypothesis space (generation of theories). In scientific discovery, search must often extend to other spaces as well: spaces of possible problems, of new or improved scientific instruments, of new problem representations, of new concepts, and others. This paper focuses especially on the processes for finding new (...) problem representations and new concepts, which are relatively new domains for research on discovery.Scientific discovery has usually been studied as an activity of individual investigators, but these individuals are positioned in a larger social structure of science, being linked by the blackboard of open publication (as well as by direct collaboration). Even while an investigator is working alone, the process is strongly influenced by knowledge and skills stored in memory as a result of previous social interaction. In this sense, all research on discovery, including the investigations on individual processes discussed in this paper, is social psychology, or even sociology. (shrink)

Several authors have hailed intuition as one of the defining features of expertise. In particular, while disagreeing on almost anything that touches on human cognition and artificial intelligence, Hubert Dreyfus and Herbert Simon agreed on this point. However, the highly influential theories of intuition they proposed differed in major ways, especially with respect to the role given to search and as to whether intuition is holistic or analytic. Both theories suffer from empirical weaknesses. In this paper, we show how, with (...) some additions, a recent theory of expert memory (the template theory) offers a coherent and wide-ranging explanation of intuition in expert behaviour. It is shown that the theory accounts for the key features of intuition: it explains the rapid onset of intuition and its perceptual nature, provides mechanisms for learning, incorporates processes showing how perception is linked to action and emotion, and how experts capture the entirety of a situation. In doing so, the new theory addresses the issues problematic for Dreyfus’s and Simon’s theories. Implications for research and practice are discussed. (shrink)