Abstract
We present a novel face adaptation paradigm that follows from Li and Atick's (1994) theory of efficient binocular encoding. In this theory, the inputs to the two eyes are combined using separately adaptable binocular summation and differencing channels. We showed previously that, if a binocular test stimulus is designed so that the summation and differencing channels see opposite directions of motion or tilt, then the perceived direction of motion or tilt can be manipulated by selectively adapting one of the binocular channels, even if the adaptor contains no motion or orientation signal. Here we extend this to face gender adaptation. Our test stimuli were made from male and female facial composite images. In what follows, uppercase letters M and F are the male and female composite images, and lowercase letters are scalar multipliers that control image contrast. On half the trials, the summation and differencing channels receive male image aM and female image bF, respectively, made by inputs (aM+bF)/2 to one eye and (aM–bF)/2 to the other eye. On the other half of the trials, the summation and differencing channels receive female image aF and male image bM, respectively. The probability of perceiving the gender corresponding to the summation channel is influenced not only by the ratio a/b, but also by the contrast sensitivities of the summation and differencing channels. We manipulated channel sensitivities using adaptation stimuli that were low-pass filtered noise. In correlated adaptation, each eye received the same adaptation image, which selectively adapted the summation channel; in anticorrelated adaptation, the adaptor contrast was reversed between the eyes, selectively adapting the differencing channel. Despite being random noise, the adaptors influenced perceived gender: The probability of perceiving the gender corresponding to the summation channel increased after anticorrelated adaptation and decreased after correlated adaptation. The results support Li and Atick's theory.
Meeting abstract presented at VSS 2016