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Intuitive Expertise and Perceptual Templates

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Abstract

We provide the first demonstration of an artificial neural network encoding the perceptual templates that form an important component of the high level strategic understanding developed by experts. Experts have a highly refined sense of knowing where to look, what information is important and what information to ignore. The conclusions these experts reach are of a higher quality and typically made in a shorter amount of time than those of non-experts. Understanding the manifestation of such abilities in terms of both the psychology of expert performance and the underlying neural mechanisms constitutes one of the most challenging problems in the cognitive sciences. Using perceptual templates we show how the amount of contextual information can change significantly even within a given task, the relationship between local and non-local contexts and finally why there is very little correlation between measures of intelligence and level of expertise in many of the most complex tasks performed by humans.

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Notes

  1. See http://senseis.xmp.net/ for an introduction to the game.

  2. There are 19 × 19 = 361 positions on the board, the training vectors and the neurons represent a board as a vector of 361 elements and there are 50 × 50 = 2,500 neurons in each SoM. There is one SoM trained for each of the 361 positions.

  3. Taken from the commercial GoGod database, Winter 2009 version.

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Acknowledgments

This work was supported by ARC grant DP0881829 and by US Air Force grant AOARD104116.

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Authors and Affiliations

  1. Centre for the Mind, The University of Sydney, Sydney, 2006, Australia

    Michael Harré & Allan Snyder

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  1. Michael Harré
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  2. Allan Snyder
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Correspondence to Michael Harré.

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Harré, M., Snyder, A. Intuitive Expertise and Perceptual Templates. Minds & Machines 22, 167–182 (2012). https://doi.org/10.1007/s11023-011-9264-9

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  • DOI: https://doi.org/10.1007/s11023-011-9264-9

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