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Chess players eye movements reveal rapid recognition of complex visual patterns

Session: Eye movements: Cognition

Heather Sheridan1, Eyal Reingold2; 1University of Southampton, UK, 2University of Toronto, Canada

A key component of chess expertise is the ability to efficiently encode domain-related perceptual configurations. To explore this perceptual component of chess expertise, we monitored the eye movements of expert and novice chess players while they engaged in a chess-related "visual search" task that was designed to test anecdotal reports that a key differentiator of chess skill is the ability to visualize the complex moves of the knight piece. Specifically, the chess players viewed an array of four minimized chessboards, and they were asked to rapidly locate the target board that allowed a knight piece to reach a target square in three moves. On each trial, there was only one target board, and the remaining "lure" boards blocked the knights path on either the first move or on the second move. The chess experts displayed longer first-fixation durations on the target board than on the lure boards, which suggests that chess experts can rapidly differentiate complex chess-related visual patterns. Interestingly, this first-fixation effect was driven by a subset of trials in which the experts displayed a single dwell on the target, and it was absent on trials in which the experts showed multiple dwells on the target board. Unlike the experts, the novices first-fixation durations did not differentiate between targets and lures, and the novices displayed higher numbers of dwells and fixations than the experts. As hypothesized, the task differentiated chess skill, such that reaction times were more than four times faster for the experts relative to novices, and the reaction times of the experts were strongly correlated with their official chess ratings. These results indicate that visual expertise in chess involves the ability to rapidly recognize complex perceptual patterns, and to process chess stimuli in terms of larger patterns (i.e., holistic processing) rather than individual features.

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