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Although human face recognition performance shows high selectivity, even for unfamiliar faces, the neuronal circuitry underlying this high performance is poorly understood. Two extreme alternatives can be considered: either a “labeled-line” principle, in which subtle changes in face images lead to activation of differently tuned neuronal populations, or a coarse coding principle, where the high face selectivity is coded by the relative activation of broadly tuned neurons. In this study, we set to parametrically examine the shape and selectivity profile of face-related visual areas. To that end, we applied the functional magnetic resonance (fMR)-adaptation paradigm. Unfamiliar face stimuli were morphed into sets ranging from identical faces, through subtle morphing, to completely different exemplars. The fusiform face area (FFA) revealed high face sensitivity, so that even facial images perceived as belonging to the same individual (&lt;35%) were sufficient to produce full recovery from adaptation. Interestingly, the psychophysical detectability of facial differences paralleled the release from fMR-adaptation. These results support the labeled-line model where high sensitivity to face changes is paralleled by narrow tuning of neuronal populations selective to each face image, and they suggest that fMR-adaptation is closely related to behavior. The results bear strong implications to the nature of face-related neuronal responses.

Sub-exemplar Shape Tuning in Human Face-Related Areas