Further attempts to clarify the importance of category variability for categorisation

Perlman, Amotz, Hahn, Ulrike, Edwards, Darren J. and Pothos, Emmanuel M. 2012. Further attempts to clarify the importance of category variability for categorisation. Journal of Cognitive Psychology 24 (2) , pp. 203-220. 10.1080/20445911.2011.613818

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Abstract

The issue of how category variability affects classification of novel instances is an important one for assessing theories of categorisation, yet previous research cannot provide a compelling conclusion. In five experiments we reexamine some of the factors thought to affect participant performance. In Experiments 1 and 2, participants almost always classified the test item as belonging to the high variability category. By contrast, in Experiment 3 we employed an alternative experimental paradigm, where the difference in variability of the two categories was less salient. In that case, participants tended to classify a test item as belonging to the low variability category. Two additional experiments (4 and 5) explored in detail the differences between Experiments 1, 2 on the one hand, and 3 on the other. Some insight into the underlying psychological processes can be provided by computational models of categorisation, and we focus on the continuous version of Anderson's (1991) Rational Model, which has not been explored before in this context. The model predicts that test instances exactly halfway between the prototypes of two categories should be classified into the more variable category, consistent with the bulk of empirical findings. We also provided a comparison with a slightly reduced version of the Generalised Context Model (GCM) to show that its predictions are consistent with those from the Rational Model, for our stimulus sets.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Psychology
Subjects:	B Philosophy. Psychology. Religion > BF Psychology
Uncontrolled Keywords:	Bayesian models, Categorisation, Decision making, Generalised Context Model, Rational Model, Unsupervised categorisation
Publisher:	Taylor & Francis
ISSN:	2044-5911
Last Modified:	19 Mar 2016 22:53
URI:	https://orca.cardiff.ac.uk/id/eprint/30678

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