Journal of Vision - Accurate statistical tests for smooth classification images, by Chauvin, Worsley, Schyns, Arguin, & Gosselin

Volume 5, Number 9, Article 1, Pages 659-667

doi:10.1167/5.9.1

http://journalofvision.org/5/9/1/

ISSN 1534-7362

Accurate statistical tests for smooth classification images

Alan Chauvin	Département de Psychologie, Université de Montréal, Montréal, QC, Canada
Keith J. Worsley	Department of Mathematics and Statistics, McGill University, Montréal, QC, Canada
Philippe G. Schyns	Department of Psychology, University of Glasgow, Glasgow, United Kingdom
Martin Arguin	Département de Psychologie, Université de Montréal, Montréal, QC, Canada
Frédéric Gosselin	Département de Psychologie, Université de Montréal, Montréal, QC, Canada

Abstract

Despite an obvious demand for a variety of statistical tests adapted to classification images, few have been proposed. We argue that two statistical tests based on random field theory (RFT) satisfy this need for smooth classification images. We illustrate these tests on classification images representative of the literature from F. Gosselin and P. G. Schyns (2001) and from A. B. Sekuler, C. M. Gaspar, J. M. Gold, and P. J. Bennett (2004). The necessary computations are performed using the Stat4Ci Matlab toolbox.

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History

Received October 25, 2004; published October 5, 2005

Citation

Chauvin, A., Worsley, K. J., Schyns, P. G., Arguin, M., & Gosselin, F. (2005). Accurate statistical tests for smooth classification images. Journal of Vision, 5(9):1, 659-667, http://journalofvision.org/5/9/1/, doi:10.1167/5.9.1.

Keywords

classification images, reverse correlation, Bubbles, random field theory

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