Applying psychophysical reverse correlation to high dimensional natural stimuli

Abstract

Psychophysics with high dimensional stimuli (e.g. video fragments) can be challenging. Typically, a stimulus is presented with a noise mask but for high res stimulus displays, the number of free parameters increases exponentially inducing the curse of dimensionality. Another question is how such noise embeddings and stimulus manipulations affect the observers’ strategy [Macke &Wichmann 2010]. This research aims to investigate the errors we make in every day life in detecting emotional expressions. Opposed to noise based experiments, in every day life, faces are often fully visible and errors emerge rather from the misinterpretation of ambiguous but visible components of the face. Macke and Wichmann (2010) investigated a similar problem in gender classification. They presented static face images in a parameterized morph-space [Blanz & Vetter 1999]. We applied the same idea to classification of dynamic emotional expressions but we used Active Appearance Models [Cootes et al. 1998] to parameterize shape variations by low dimensional vectors of facial components. Via staircase procedures, we varied the amount of information in the different components. Participants detected dynamic emotional expressions ranging from no ambiguity at all, towards a static image with neutral expression. We determined separate thresholds for different facial components and found that participants were equally accurate in detecting emotional expressions as long as the eyes were informative.