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Abstract:
High-level aftereffects have previously been reported for the recognition of static faces. We present an experiment showing for the first time high-level aftereffects for dynamic facial expressions. Facial expressions were generated as a morph animation based on a weighted sum of 3-D shapes derived from scans of facial action units [Curio et al 2006, in Proceedings of the 3rd Symposium on Applied Perception in Graphics and Visualization (New York: ACM Press) pp 77 - 84]. With this technique we produced dynamic happy and disgust expressions. By changing the sign of the morph weights we were able to obtain lsquo;anti-expressionslsquo;. Participants observed dynamic anti-expressions for 8 s. Immediately after each adaptation phase, recognition performance was tested for the original expressions (2AFC, reduced expression strength). Adaptation stimuli were chosen from two identities and were shown either in forward or reverse time order. We found strong expression-related af
tereffec
ts (increased recognition for matching expression stimuli, plt;0.05, N=13), which depended also on the match between the identities of adaptation and test face. We are currently investigating the influence of static vs dynamic representations in the observed aftereffect.
