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Constantin Rezlescu, Tirta Susilo, Angus Chapman, Alfonso Caramazza; Large inversion effects are not specific to faces and do not vary with object expertise. Journal of Vision 2017;17(10):250. doi: 10.1167/17.10.250.
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© ARVO (1962-2015); The Authors (2016-present)
Visual object recognition is impaired when stimuli are shown upside-down. This phenomenon is known as the inversion effect, and a substantial body of evidence suggests it is much larger for faces than non-face objects. The large inversion effect for faces has been widely used as key evidence that face processing is special, and hundreds of studies have used it as a tool to investigate face-specific processes. Here we show that large inversion effects are not specific to faces. We tested two groups of participants, over the web (n=63) and in the laboratory (n=57), with two car tasks tapping basic object recognition and within-class recognition. Both car tasks generated large inversion effects which were identical to those produced by parallel face tasks. For basic-level recognition, the car inversion effects were 28% (SD=12%) and 27% (SD=13%), while the face inversion effects were 28% (SD=13%) and 26% (SD=10%) (web and lab samples, respectively). For within-class recognition, the car inversion effects were 23% (SD=9%) and 26% (SD=10%), while the face inversion effects were 25% (SD=13%) and 25% (SD=10%). Additional analyses showed that the car inversion effects did not vary with car expertise. Our findings demonstrate that non-face object recognition can depend on processes that are highly orientation-specific, challenging a critical behavioral marker of face-specific processes. We suggest that, rather than being face-specific, inversion effects are the result of a special type of processing engaged in recognition of exemplars from complex but highly homogeneous sets of objects with a canonical orientation. Studies which claimed to measure face-specific mechanisms did not control for this type of processing and so will need reexamination.
Meeting abstract presented at VSS 2017
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