A pattern-recognition theory of search in expert problem solving

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

Visual search in ecological and non-ecological displays: Evidence for a non-monotonic effect of complexity on performance

Copyright @ 2013 PLoSThis article has been made available through the Brunel Open Access Publishing Fund.Considerable research has been carried out on visual search, with single or multiple targets. However, most studies have used artificial stimuli with low ecological validity. In addition, little is known about the effects of target complexity and expertise in visual search. Here, we investigate visual search in three conditions of complexity (detecting a king, detecting a check, and detecting a checkmate) with chess players of two levels of expertise (novices and club players). Results show that the influence of target complexity depends on level of structure of the visual display. Different functional relationships were found between artificial (random chess positions) and ecologically valid (game positions) stimuli: With artificial, but not with ecologically valid stimuli, a “pop out” effect was present when a target was visually more complex than distractors but could be captured by a memory chunk. This suggests that caution should be exercised when generalising from experiments using artificial stimuli with low ecological validity to real-life stimuli.This study is funded by Brunel University and the article is made available through the Brunel Open Access Publishing Fund

Discovering predictive variables when evolving cognitive models

A non-dominated sorting genetic algorithm is used to evolve models of learning from different theories for multiple tasks. Correlation analysis is performed to identify parameters which affect performance on specific tasks; these are the predictive variables. Mutation is biased so that changes to parameter values tend to preserve values within the population's current range. Experimental results show that optimal models are evolved, and also that uncovering predictive variables is beneficial in improving the rate of convergence

Fractional smoothness and applications in finance

This overview article concerns the notion of fractional smoothness of random variables of the form $g(X_T)$, where $X=(X_t)_{t\in [0,T]}$ is a certain diffusion process. We review the connection to the real interpolation theory, give examples and applications of this concept. The applications in stochastic finance mainly concern the analysis of discrete time hedging errors. We close the review by indicating some further developments.Comment: Chapter of AMAMEF book. 20 pages

Structural variant calling: the long and the short of it.

Recent research into structural variants (SVs) has established their importance to medicine and molecular biology, elucidating their role in various diseases, regulation of gene expression, ethnic diversity, and large-scale chromosome evolution-giving rise to the differences within populations and among species. Nevertheless, characterizing SVs and determining the optimal approach for a given experimental design remains a computational and scientific challenge. Multiple approaches have emerged to target various SV classes, zygosities, and size ranges. Here, we review these approaches with respect to their ability to infer SVs across the full spectrum of large, complex variations and present computational methods for each approach

The Euler-Maruyama approximation for the absorption time of the CEV diffusion

A standard convergence analysis of the simulation schemes for the hitting times of diffusions typically requires non-degeneracy of their coefficients on the boundary, which excludes the possibility of absorption. In this paper we consider the CEV diffusion from the mathematical finance and show how a weakly consistent approximation for the absorption time can be constructed, using the Euler-Maruyama scheme

Expert chess memory: Revisiting the chunking hypothesis

After reviewing the relevant theory on chess expertise, this paper re-examines experimentally the finding of Chase and Simon (1973a) that the differences in ability of chess players at different skill levels to copy and to recall positions are attributable to the experts' storage of thousands of chunks (patterned clusters of pieces) in long-term memory. Despite important differences in the experimental apparatus, the data of the present experiments regarding latencies and chess relations between successively placed pieces are highly correlated with those of Chase and Simon. We conclude that the 2-second inter-chunk interval used to define chunk boundaries is robust, and that chunks have psychological reality. We discuss the possible reasons why Masters in our new study used substantially larger chunks than the Master of the 1973 study, and extend the chunking theory to take account of the evidence for large retrieval structures (templates) in long-term memory

The relationship between cognitive ability and chess skill: a comprehensive meta-analysis

Why are some people more skilled in complex domains than other people? Here, we conducted a meta-analysis to evaluate the relationship between cognitive ability and skill in chess. Chess skill correlated positively and significantly with fluid reasoning (Gf) (r−=0.24), comprehension-knowledge (Gc) (r−=0.22), short-term memory (Gsm) (r−=0.25), and processing speed (Gs) (r−=0.24); the meta-analytic average of the correlations was (r−=0.24). Moreover, the correlation between Gf and chess skill was moderated by age (r−=0.32 for youth samples vs. r−=0.11 for adult samples), and skill level (r−=0.32 for unranked samples vs. r−=0.14 for ranked samples). Interestingly, chess skill correlated more strongly with numerical ability (r−=0.35) than with verbal ability (r−=0.19) or visuospatial ability (r−=0.13). The results suggest that cognitive ability contributes meaningfully to individual differences in chess skill, particularly in young chess players and/or at lower levels of skill

The cognitive and academic benefits of Cogmed: a meta-analysis

Cogmed Working Memory Training (CWMT) is a commercial cognitive-training program designed to foster working-memory capacity. Enhanced working-memory capacity is then supposed to increase one's overall cognitive function and academic achievement. This meta-analysis investigates the effects of CWMT on cognitive and academic outcomes. The inclusion criteria were met by 50 studies (637 effect sizes). Highly consistent near-zero effects were estimated in far-transfer measures of cognitive ability (e.g., attention and intelligence) and academic achievement (language ability and mathematics). By contrast, slightly heterogeneous small to medium effects were observed in memory tasks (i.e., near transfer). Moderator analysis showed that these effects were weaker for near-transfer measures not directly related to the trained tasks. These results highlight that, while near transfer occurs regularly, far transfer is rare or, possibly, inexistent. Transfer thus appears to be a function of the degree of overlap between trained tasks and outcome tasks