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J. Liu, A. Harris, N. Kanwisher; What makes a face a face: an MEG and fMRI study. Journal of Vision 2001;1(3):340. doi: https://doi.org/10.1167/1.3.340.
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© ARVO (1962-2015); The Authors (2016-present)
Previous studies using MEG and fMRI have isolated face-selective neural responses known as the M170 and Fusiform Face Area (FFA), respectively. Here we asked which aspects of a face stimulus are important in eliciting these responses: the presence of face parts (eyes, nose, mouth), face configuration, or external face features. We measured the amplitude and latency of the M170 (with MEG) and the activity in the FFA (with fMRI) for stimuli in which we orthogonally varied whether the “face” contained (I) real face parts versus solid black ovals in the corresponding locations; (II) veridical face configurations versus rearranged nonface configurations; and (III) intact external features versus a square cutout showing the central face region. Our MEG data indicate that while the M170 is sensitive to all three face components, the external features appear to play a special role: not only are external features with only a nonface configuration of ovals inside sufficient for the maximal face-selective response, but external face features also exhibit their effects at the earliest latency. On the other hand, the neural activity of the FFA, which is summed over all latencies of face processing by fMRI, shows a strikingly different response profile from that seen with MEG. Different face components appear to contribute in a roughly equal and additive fashion to the FFA response: only the veridical face elicited a full-amplitude response, and with fewer components the response decreased in amplitude. The contrast of response profiles measured by MEG and fMRI suggest multiple mechanisms involved in face categorization: a quick-and-dirty mechanism for detecting faces that weighs external face features heavily, and subsequent stages which integrate more fine-grained information about face parts and configuration.
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