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Liuba Papeo; Seeing (social) relations: human visual specialization for dyadic interactions. Journal of Vision 2019;19(10):55b. doi: https://doi.org/10.1167/19.10.55b.
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© ARVO (1962-2015); The Authors (2016-present)
“Social perception” classically indicates the tuning of human vision to certain entities, such as faces and bodies, which turn out to have high social value. But, “social” is primarily a property of a relation that implies at least two entities. We demonstrate that visual perception is tuned to see that relation; namely, it is tuned to process multiple entities in spatial relations that facilitate social interactions. With functional MRI on healthy adults, we identified face-, body-, and place-preferring areas and early visual cortex. We then showed participants images featuring two human bodies facing one other (seemingly interacting), two bodies facing away from each other, and single bodies. Selectively, the body-preferring area in the extrastriate cortex showed stronger activity for facing dyads, than for nonfacing dyads and single bodies. Moreover, the same area showed greater sensitivity (classification accuracy) to differences between facing dyads than between non-facing dyads, suggesting greater involvement in representation of interactive scenarios. These fMRI results are complemented by behavioral studies. Using backward masking during a visual recognition task, we found that the inversion effect, a marker for the special visual sensitivity to faces and bodies, was larger for facing than for non-facing dyads. Finally, a visual search task showed that facing dyads were processed more efficiently than nonfacing dyads. These results demonstrate preparedness of visual perception to process socially relevant spatial configurations, larger and more complex than a single face/body. This mechanism for attaining a fast appraisal of relations in a scene may be critical to channel body perception into social inferences. It contributes to define the perceptual analysis as an integrative structuring of information for higher-level inferential operations, beyond independent shape recognition.
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