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Related Experiment Video

Updated: May 4, 2026

The HoneyComb Paradigm for Research on Collective Human Behavior

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

Expertise and processing distorted structure in chess.

James C Bartlett¹, Amy L Boggan², Daniel C Krawczyk³

¹Program in Cognition and Neuroscience, School of Behavioral and Brain Sciences, The University of Texas at Dallas Richardson, TX, USA.

Frontiers in Human Neuroscience

|December 19, 2013

Summary

Expert chess players actively search for structure in distorted chess positions, engaging brain networks related to attention and working memory. This exploratory chunking helps them process familiar but jumbled stimuli.

Keywords:

chess chunking consciousness expertise meaning prefrontal-parietal network structure

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Area of Science:

Cognitive Neuroscience
Human Expertise Research

Background:

Experts exhibit superior memory for domain-specific stimuli.
The processing of degraded or distorted within-domain stimuli by experts remains poorly understood.
Understanding expert processing of distorted stimuli is crucial for cognitive science.

Purpose of the Study:

To investigate how chess experts process distorted chess stimuli.
To identify the neural mechanisms underlying expert processing of familiar yet distorted information.
To explore the role of attention and working memory in expert perception.

Main Methods:

Functional magnetic resonance imaging (fMRI) was used to study brain activity.
Participants included expert chess players.
Stimuli consisted of distorted chess positions and control stimuli.

Main Results:

fMRI data revealed engagement of a prefrontal-parietal network in experts viewing distorted stimuli.
This network is associated with consciousness, attention, and working memory.
Experts demonstrated an active search for structure, termed exploratory chunking.

Conclusions:

Experts actively reconstruct or find patterns in distorted domain-specific stimuli.
The prefrontal-parietal network plays a key role in this active processing.
This finding sheds light on the dynamic nature of expert knowledge representation and processing.