Expertise effects in memory recall: A reply to Vicente and Wang

This article may not exactly replicate the final version published in the APA journal. It is not the copy of record.In the January 1998 Psychological Review, Vicente and Wang propose a "constraint attunement hypothesis" to explain the large effects of domain expertise upon memory recall observed in a number of task domains. They claim to find serious defects in alternative explanations of these effects which their theory overcomes. Re-examination of the evidence shows that their theory is not novel, but has been anticipated by those they criticize, and that other current published theories of the phenomena do not have the defects Vicente and Wang attribute to them. Vicente and Wang's views reflect underlying differences (a) about emphasis upon performance versus process in psychology, and (b) about how theories and empirical knowledge interact and progress with the development of a science

The role of constraints in expert memory

A great deal of research has been devoted to developing process models of expert memory. However, K. J. Vicente and J. H. Wang (1998) proposed (a) that process theories do not provide an adequate account of expert recall in domains in which memory recall is a contrived task and (b) that a product theory, the constraint attunement hypothesis (CAH), has received a significant amount of empirical support. We compared 1 process theory (the template theory; TT; F. Gobet & H. A. Simon, 1996c) with the CAH in chess. Chess players (N = 36) differing widely in skill levels were required to recall briefly presented chess positions that were randomized in various ways. Consistent with TT, but inconsistent with the CAH, there was a significant skill effect in a condition in which both the location and distribution of the pieces were randomized. These and other results suggest that process models such as TT can provide a viable account of expert memory in chess

How active perception and attractor dynamics shape perceptual categorization: A computational model

We propose a computational model of perceptual categorization that fuses elements of grounded and sensorimotor theories of cognition with dynamic models of decision-making. We assume that category information consists in anticipated patterns of agent–environment interactions that can be elicited through overt or covert (simulated) eye movements, object manipulation, etc. This information is firstly encoded when category information is acquired, and then re-enacted during perceptual categorization. The perceptual categorization consists in a dynamic competition between attractors that encode the sensorimotor patterns typical of each category; action prediction success counts as ‘‘evidence’’ for a given category and contributes to falling into the corresponding attractor. The evidence accumulation process is guided by an active perception loop, and the active exploration of objects (e.g., visual exploration) aims at eliciting expected sensorimotor patterns that count as evidence for the object category. We present a computational model incorporating these elements and describing action prediction, active perception, and attractor dynamics as key elements of perceptual categorizations. We test the model in three simulated perceptual categorization tasks, and we discuss its relevance for grounded and sensorimotor theories of cognition.Peer reviewe

Five seconds or sixty? Presentation time in expert memory

The template theory presented in Gobet and Simon (1996a, 1998) is based on the EPAM theory (Feigenbaum & Simon, 1984; Richman et al., 1995), including the numerical parameters that have been estimated in tests of the latter; and it therefore offers precise predictions for the timing of cognitive processes during the presentation and recall of chess positions. This paper describes the behavior of CHREST, a computer implementation of the template theory, in a task when the presentation time is systematically varied from one second to sixty seconds, on the recall of both game and random positions, and compares the model to human data. As predicted by the model, strong players are better than weak players with both types of positions. Their superiority with random positions is especially clear with long presentation times, but is also present after brief presentation times, although smaller in absolute value. CHREST accounts for the data, both qualitatively and quantitatively. Strong players’ superiority with random positions is explained by the large number of chunks they hold in LTM. Strong players’ high recall percentage with short presentation times is explained by the presence of templates, a special class of chunks. The model is compared to other theories of chess skill, which either cannot account for the superiority of Masters with random positions (models based on high-level descriptions and on levels of processing) or predict too strong a performance of Masters with random positions (long-term working memory)