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From Amateur to Professional: A Neuro-cognitive Model of Categories and Expert Development

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Abstract

The ability to group perceptual objects into functionally relevant categories is vital to our comprehension of the world. Such categorisation aids in how we search for objects in familiar scenes and how we identify an object and its likely uses despite never having seen that specific object before. The systems that mediate this process are only now coming to be understood through considerable research efforts combining neurological, psychological and behavioural studies. What is much less well understood are the differences between the categories, how they are formed and how they are used by experts and non-experts in a complex task that can take decades to master. In a quite different direction to previous studies, this work infers the different categorical structures that might be used by amateurs and professionals in the oriental game of Go. This is achieved by using a newly developed combination of artificial neural networks (Self-organising Maps) and perceptual inference to show that categories of strategic scenes can be learned while playing games using a model of ‘conditional perceptual learning’. Applying this technique to two databases of games, one of amateurs and one of professionals, shows that a structural hierarchy of scene information develops that can be readily incorporated into traditional psychological models of decisions and readily implemented in computational systems. The results are discussed in terms of the heuristics and biases literature, emphasising where the significant similarities and differences lie between this work and previous work.

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Notes

  1. A full set of rules and a complete description can be found at http://senseis.xmp.net/.

  2. From the KGS Go server: http://www.gokgs.com.

  3. Taken from the commercial GoGod database, Winter 2009 version: http://www.gogod.co.uk.

  4. The unique function both finds the unique vectors in S a and S p (and thereby generates \(S^{\tilde{a}}\) and \(S^{\tilde{p}}\)) and also sorts them in ascending order.

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Acknowledgments

This work was supported by US Airforce Grant AOARD 104116.

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  1. Complex Systems Group, School of Engineering and Information Technologies, The University of Sydney, Sydney, NSW, 2006, Australia

    Michael S. Harré

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Correspondence to Michael S. Harré.

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Harré, M.S. From Amateur to Professional: A Neuro-cognitive Model of Categories and Expert Development. Minds & Machines 23, 443–472 (2013). https://doi.org/10.1007/s11023-013-9305-7

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