Accurate statistical tests for smooth classification images.

Alan Chauvin (1) , Keith J. Worsley (2) , Philippe G. Schyns (3) , Martin Arguin (4) , Frédéric Gosselin (5)

1 LPNC - Laboratoire de Psychologie et NeuroCognition
2 Montreal Neurological Institute and Hospital
3 CCNI - CENTRE FOR COGNITIVE NEUROIMAGING IN GLASGOW
4 CERNEC - Centre de Recherche en Neuropsychologie et Neurocognition
5 CERNEC

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.

Mots clés: classification images reverse correlation Bubbles random field theory

Domaines: Psychologie et comportements; Sciences cognitives; Psychologie; Applications [stat.AP]

Alan Chauvin, Keith J. Worsley, Philippe G. Schyns, Martin Arguin, Frédéric Gosselin. Accurate statistical tests for smooth classification images.. Journal of Vision, 2005, 5 (9), pp.659-667. ⟨10.1167/5.9.1⟩. ⟨hal-00806723⟩