Efficient bubbles for visual categorization tasks

Wang, H.F., Friel, N., Gosselin, F. and Schyns, P.G. (2011) Efficient bubbles for visual categorization tasks. Vision Research, 51(12), pp. 1318-1323. (doi: 10.1016/j.visres.2011.04.007)

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Publisher's URL: http://dx.doi.org/10.1016/j.visres.2011.04.007


Bubbles is a classification image technique that randomly samples visual information from input stimuli to derive the diagnostic features that observers use in visual categorization tasks. To reach statistical significance, Bubbles performs an exhaustive and repetitive search in the stimulus space. To reduce the search trials, we developed an adaptive method that uses reinforcement learning techniques to optimize sampling by exploiting the observer's history of categorization. We compared the performance of the original and the adaptive Bubbles algorithms in a model observer and eight human adults who all resolved the same visual categorization task (i.e., five facial expressions of emotion). We demonstrate the feasibility of a substantial reduction (by a factor of similar to 2) in the number of search trials required to locate the same diagnostic features with the adaptive method, but only when the observer reaches a performance threshold of 50% correct for each expression category. When this threshold is not reached, both the original and adaptive algorithms converge in the same number of trials. (C) 2011 Elsevier Ltd. All rights reserved

Item Type:Articles
Glasgow Author(s) Enlighten ID:Wang, Dr Hong Fang and Schyns, Professor Philippe
Authors: Wang, H.F., Friel, N., Gosselin, F., and Schyns, P.G.
College/School:College of Medical Veterinary and Life Sciences > School of Psychology & Neuroscience
Journal Name:Vision Research
ISSN (Online):1878-5646
Published Online:16 April 2011

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
446721Adaptive sampling algorithms for cognitive neuroscience applications using bubblesPhilippe SchynsEngineering & Physical Sciences Research Council (EPSRC)EP/E038492/1INP - CENTRE FOR COGNITIVE NEUROIMAGING