29,156 research outputs found

    Chess Practice and Executive Functioning in a Post-Secondary Student Diagnosed with ADHD: A Single Case Study

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    This single-case study explored how the influence of chess practice on working memory and other executive functions was perceived by an adult diagnosed with attention deficit hyperactivity disorder (ADHD). Cognitive science has used chess in the study of memory, concentration, attention and expertise (Charness, 1992; Gobet, 1998). The game of chess has also been used in clinical and educational contexts both to enhance cognitive abilities and to change academic outcomes (Hong, 2005). The chess program I designed consisted of a weekly, one hour chess practice for ten weeks during which the participant solved chess puzzles. The selected participant underwent a semi-structured interview pre- and post- the chess intervention and answered the Barkley Adult ADHD Rating Scale (BAARS-IV) and the Barkley Deficit in Executive Function Scale (BDEFS) at the beginning and end of the chess program. Furthermore, the participant answered opened-ended questions about her perceptions of the effects of the chess program after each of eight training sessions. Thematic analysis was performed in an inductive search for general descriptors within the data. The chess training intervention resulted in the participant’s perception of an overall decrease in ADHD symptoms, especially inattentiveness, and improvement in working memory and other executive functions. Implications for further research and practice are identified

    An investigation into the effect of ageing on expert memory with CHREST

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    CHREST is a cognitive architecture that models human perception, learning, memory, and problem solving, and which has successfully simulated numerous human experimental data on chess. In this paper, we describe an investigation into the effects of ageing on expert memory using CHREST. The results of the simulations are related to the literature on ageing. The study illustrates how Computational Intelligence can be used to understand complex phenomena that are affected by multiple variables dynamically evolving as a function of time and that have direct practical implications for human societies

    Attention mechanisms in the CHREST cognitive architecture

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    In this paper, we describe the attention mechanisms in CHREST, a computational architecture of human visual expertise. CHREST organises information acquired by direct experience from the world in the form of chunks. These chunks are searched for, and verified, by a unique set of heuristics, comprising the attention mechanism. We explain how the attention mechanism combines bottom-up and top-down heuristics from internal and external sources of information. We describe some experimental evidence demonstrating the correspondence of CHREST’s perceptual mechanisms with those of human subjects. Finally, we discuss how visual attention can play an important role in actions carried out by human experts in domains such as chess

    Checking chess checks with chunks: A model of simple check detection

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    The procedure by which humans identify checks in check positions is not well understood. We report here our experience in modelling this process with CHREST, a general-purpose cognitive model that has previously successfully captured a variety of attention- and perception-related phenomena. We have attempted to reproduce the results of an experiment investigating the ability of humans to determine checks in simple chess positions. We propose a specific model of how humans perform this experiment, and show that, given certain reasonable assumptions, CHREST can follow this model to create a good reproduction of the data

    The CHREST architecture of cognition : the role of perception in general intelligence

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    Original paper can be found at: http://www.atlantis-press.com/publications/aisr/AGI-10/ Copyright Atlantis Press. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited.This paper argues that the CHREST architecture of cognition can shed important light on developing artificial general intelligence. The key theme is that "cognition is perception." The description of the main components and mechanisms of the architecture is followed by a discussion of several domains where CHREST has already been successfully applied, such as the psychology of expert behaviour, the acquisition of language by children, and the learning of multiple representations in physics. The characteristics of CHREST that enable it to account for empirical data include: self-organisation, an emphasis on cognitive limitations, the presence of a perception-learning cycle, and the use of naturalistic data as input for learning. We argue that some of these characteristics can help shed light on the hard questions facing theorists developing artificial general intelligence, such as intuition, the acquisition and use of concepts and the role of embodiment

    The emergence of choice: Decision-making and strategic thinking through analogies

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    Consider the chess game: When faced with a complex scenario, how does understanding arise in one’s mind? How does one integrate disparate cues into a global, meaningful whole? how do humans avoid the combinatorial explosion? How are abstract ideas represented? The purpose of this paper is to propose a new computational model of human chess intuition and intelligence. We suggest that analogies and abstract roles are crucial to solving these landmark problems. We present a proof-of-concept model, in the form of a computational architecture, which may be able to account for many crucial aspects of human intuition, such as (i) concentration of attention to relevant aspects, (ii) \ud how humans may avoid the combinatorial explosion, (iii) perception of similarity at a strategic level, and (iv) a state of meaningful anticipation over how a global scenario \ud may evolve

    A pattern-recognition theory of search in expert problem solving

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    Understanding how look-ahead search and pattern recognition interact is one of the important research questions in the study of expert problem-solving. This paper examines the implications of the template theory (Gobet & Simon, 1996a), a recent theory of expert memory, on the theory of problem solving in chess. Templates are "chunks" (Chase & Simon, 1973) that have evolved into more complex data structures and that possess slots allowing values to be encoded rapidly. Templates may facilitate search in three ways: (a) by allowing information to be stored into LTM rapidly; (b) by allowing a search in the template space in addition to a search in the move space; and (c) by compensating loss in the "mind's eye" due to interference and decay. A computer model implementing the main ideas of the theory is presented, and simulations of its search behaviour are discussed. The template theory accounts for the slight skill difference in average depth of search found in chess players, as well as for other empirical data

    Chunks hierarchies and retrieval structures: Comments on Saariluoma and Laine

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    The empirical results of Saariluoma and Laine (in press) are discussed and their computer simulations are compared with CHREST, a computational model of perception, memory and learning in chess. Mathematical functions such as power functions and logarithmic functions account for Saariluoma and Laine's (in press) correlation heuristic and for CHREST very well. However, these functions fit human data well only with game positions, not with random positions. As CHREST, which learns using spatial proximity, accounts for the human data as well as Saariluoma and Laine's (in press) correlation heuristic, their conclusion that frequency-based heuristics match the data better than proximity-based heuristics is questioned. The idea of flat chunk organisation and its relation to retrieval structures is discussed. In the conclusion, emphasis is given to the need for detailed empirical data, including information about chunk structure and types of errors, for discriminating between various learning algorithms

    Expertise and intuition: A tale of three theories

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    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
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