The Role of Background Statistics in Face Adaptation.
12035 - 12044.
Cross-adaptation is widely used to probe whether different stimuli share common neural mechanisms. For example, that adaptation to second-order stimuli usually produces little aftereffect on first-order stimuli has been interpreted as reflecting their separate processing. However, such results appear to contradict the cue-invariant responses of many visual cells. We tested the novel hypothesis that the null aftereffect arises from the large difference in the backgrounds of first- and second-order stimuli. We created second-order faces with happy and sad facial expressions specified solely by local directions of moving random dots on a static-dot background, without any luminance-defined form cues. As expected, adaptation to such a second-order face did not produce a facial-expression aftereffect on the first-order faces. However, consistent with our hypothesis, simply adding static random dots to the first-order faces to render their backgrounds more similar to that of the adapting motion face led to a significant aftereffect. This background similarity effect also occurred between different types of first-order stimuli: real-face adaptation transferred to cartoon faces only when noise with correlation statistics of real faces or natural images was added to the cartoon faces. These findings suggest the following: (1) statistical similarities between the featureless backgrounds of the adapting and test stimuli can influence aftereffects, as in contingent adaptation; (2) weak or null cross-adaptation aftereffects should be interpreted with caution; and (3) luminance- and motion-direction-defined forms, and local features and global statistics, converge in the representation of faces.
|Title:||The Role of Background Statistics in Face Adaptation|
|Keywords:||HUMAN VISUAL-SYSTEM, INFORMATION-TRANSMISSION, 2ND-ORDER MOTION, IMAGE STATISTICS, NATURAL IMAGES, FACIAL MOTION, CORTEX, 1ST-ORDER, PSYCHOPHYSICS, RECOGNITION|
|UCL classification:||UCL > School of Life and Medical Sciences > Faculty of Life Sciences > Gatsby Computational Neuroscience Unit|
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