The Development of Human Expertise in a Complex Environment

Michael Harre, Terence Bossomaier, Allan Snyder

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

We introduce an innovative technique that quantifies human expertise development in such a way that humans and artificial systems can be directly compared. Using this technique we are able to highlight certain fundamental difficulties associated with the learning of a complex task that humans are still exceptionally better at than their computer counterparts. We demonstrate that expertise goes through significant developmental transitions that have previously been predicted but never explicated. The first signals the onset of a steady increase in global awareness that begins surprisingly late in expertise acquisition. The second transition, reached by only a very few experts in the world, shows a major reorganisation of global contextual knowledge resulting in a relatively minor gain in skill. We are able to show that these empirical findings have consequences for our understanding of the way in which expertise acquisition may be modelled by learning in artificial intelligence systems. This point is emphasised with a novel theoretical result showing explicitly how our findings imply a non-trivial hurdle for learning for suitably complex tasks.
Original languageEnglish
Pages (from-to)449-464
Number of pages16
JournalMinds and Machines
Volume21
Issue number3
DOIs
Publication statusPublished - Aug 2011

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Harre, Michael ; Bossomaier, Terence ; Snyder, Allan. / The Development of Human Expertise in a Complex Environment. In: Minds and Machines. 2011 ; Vol. 21, No. 3. pp. 449-464.
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The Development of Human Expertise in a Complex Environment. / Harre, Michael; Bossomaier, Terence; Snyder, Allan.

In: Minds and Machines, Vol. 21, No. 3, 08.2011, p. 449-464.

Research output: Contribution to journalArticle

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AU - Snyder, Allan

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AB - We introduce an innovative technique that quantifies human expertise development in such a way that humans and artificial systems can be directly compared. Using this technique we are able to highlight certain fundamental difficulties associated with the learning of a complex task that humans are still exceptionally better at than their computer counterparts. We demonstrate that expertise goes through significant developmental transitions that have previously been predicted but never explicated. The first signals the onset of a steady increase in global awareness that begins surprisingly late in expertise acquisition. The second transition, reached by only a very few experts in the world, shows a major reorganisation of global contextual knowledge resulting in a relatively minor gain in skill. We are able to show that these empirical findings have consequences for our understanding of the way in which expertise acquisition may be modelled by learning in artificial intelligence systems. This point is emphasised with a novel theoretical result showing explicitly how our findings imply a non-trivial hurdle for learning for suitably complex tasks.

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