Sherlock Holmes: An expert’s view of expertise

In recent years, there has been an intense research effort to understand the cognitive processes and structures underlying expert behaviour. Work in different fields, including scientific domains, sports, games, and mnemonics, has shown that there are vast differences in perceptual abilities between experts and novices, and that these differences may underpin other cognitive differences in learning, memory, and problem solving. In this article, we evaluate the progress made in the last years through the eyes of an outstanding, albeit fictional, expert: Sherlock Holmes. We first use the Sherlock Holmes character to illustrate expert processes as described by current research and theories. In particular, the role of perception, as well as the nature and influence of expert knowledge, are all present in the description of Conan Doyle’s hero. In the second part of the article, we discuss a number of issues that current research on expertise has barely addressed. These gaps include, for example, several forms of reasoning, the influence of emotions on cognition, and the effect of age on experts’ knowledge and cognitive processes. Thus, although nearly 120 years old, Conan Doyle’s books show remarkable illustrations of expert behaviour, including the coverage of themes that have mostly been overlooked by current research

Evolving structure-function mappings in cognitive neuroscience using genetic programming

A challenging goal of psychology and neuroscience is to map cognitive functions onto neuroanatomical structures. This paper shows how computational methods based upon evolutionary algorithms can facilitate the search for satisfactory mappings by efficiently combining constraints from neuroanatomy and physiology (the structures) with constraints from behavioural experiments (the functions). This methodology involves creation of a database coding for known neuroanatomical and physiological constraints, for mental programs made of primitive cognitive functions, and for typical experiments with their behavioural results. The evolutionary algorithms evolve theories mapping structures to functions in order to optimize the fit with the actual data. These theories lead to new, empirically testable predictions. The role of the prefrontal cortex in humans is discussed as an example. This methodology can be applied to the study of structures or functions alone, and can also be used to study other complex systems. (This article does not exactly replicate the final version published in the Journal of Swiss Psychology. It is not a copy of the original published article and is not suitable for citation.

Mental Imagery and Chunks: Empirical and Computational Findings

To investigate experts’ imagery in chess, players were required to recall briefly-presented positions in which the pieces were placed on the intersections between squares (intersection positions). Position types ranged from game positions to positions where both the piece distribution and location were randomized. Simulations were run with the CHREST model (Gobet & Simon, 2000). The simulations assumed that pieces had to be centered back one by one to the middle of the squares in the mind’s eye before chunks could be recognized. Consistent with CHREST’s predictions, chess players (N = 36), ranging from weak amateurs to grandmasters, exhibited much poorer recall on intersection positions than on standard positions (pieces placed on centers of squares). On the intersection positions, the skill difference in recall was larger on game positions than on the randomized positions. Participants recalled bishops better than knights, suggesting that Stroop-like interference impairs recall of the latter. The data supported both the time parameter in CHREST for shifting pieces in the mind’s eye (125 ms per piece) and the seriality assumption. In general, the study reinforces the plausibility of CHREST as a model of cognition

Recall of rapidly presented random chess positions is a function of skill.

A widely cited result asserts that experts’ superiority over novices in recalling meaningful material from their domain of expertise vanishes when random material is used. A review of recent chess experiments where random positions served as control material (presentation time between 3 and 10 seconds) shows, however, that strong players generally maintain some superiority over weak players even with random positions, although the relative difference between skill levels is much smaller than with game positions. The implications of this finding for expertise in chess are discussed and the question of the recall of random material in other domains is raised

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)

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

Computational Modelling of Mental Imagery in Chess: A Sensitivity Analysis

An important aim of cognitive science is to build computational models that account for a large number of phenomena but have few free parameters, and to obtain more veridical values for the models’ parameters by successive approximations. A good example of this approach is the CHREST model (Gobet & Simon, 2000), which has simulated numerous phenomena on chess expertise and in other domains. In this paper, we are interested in the parameter the model uses for shifting chess pieces in its mind’s eye (125 ms per piece), a parameter that had been estimated based on relatively sparse experimental evidence. Recently, Waters and Gobet (2008) tested the validity of this parameter in a memory experiment that required players to recall briefly presented positions in which the pieces were placed on the intersections between squares. Position types ranged from game positions to positions where both the piece distribution and location were randomised. CHREST, which assumed that pieces must be centred back to the middle of the squares in the mind’s eye before chunks can be recognized, simulated the data fairly well using the default parameter for shifting pieces. The sensitivity analysis presented in the current paper shows that the fit was nearly optimal for all groups of players except the grandmaster group for which, counterintuitively, a slower shifting time gave a better fit. The implications for theory development are discussed

Stochastic methods for solving high-dimensional partial differential equations

We propose algorithms for solving high-dimensional Partial Differential Equations (PDEs) that combine a probabilistic interpretation of PDEs, through Feynman-Kac representation, with sparse interpolation. Monte-Carlo methods and time-integration schemes are used to estimate pointwise evaluations of the solution of a PDE. We use a sequential control variates algorithm, where control variates are constructed based on successive approximations of the solution of the PDE. Two different algorithms are proposed, combining in different ways the sequential control variates algorithm and adaptive sparse interpolation. Numerical examples will illustrate the behavior of these algorithms

Recall of random and distorted positions: Implications for the theory of expertise.

This paper explores the question, important to the theory of expert performance, of the nature and number of chunks that chess experts hold in memory. It examines how memory contents determine players' abilities to reconstruct (a) positions from games, (b) positions distorted in various ways and (c) and random positions. Comparison of a computer simulation with a human experiment supports the usual estimate that chess Masters store some 50,000 chunks in memory. The observed impairment of recall when positions are modified by mirror image reflection, implies that each chunk represents a specific pattern of pieces in a specific location. A good account of the results of the experiments is given by the template theory proposed by Gobet and Simon (in press) as an extension of Chase and Simon's (1973a) initial chunking proposal, and in agreement with other recent proposals for modification of the chunking theory (Richman, Staszewski & Simon, 1995) as applied to various recall tasks

The Roles of recognition processes and look-ahead search in time-constrained expert problem solving: Evidence from grandmaster level chess.

Chess has long served as an important standard task environment for research on human memory and problem-solving abilities and processes. In this paper, we report evidence on the relative importance of recognition processes and planning (look-ahead) processes in very high level expert performance in chess. The data show that the rated skill of a top-level grandmaster is only slightly lower when he is playing simultaneously against a half dozen grandmaster opponents than under tournament conditions that allow much more time for each move. As simultaneous play allows little time for look-ahead processes, the data indicate that recognition, based on superior chess knowledge, plays a much larger part in high-level skill in this task than does planning by looking ahead