A Highly Selective Neural Response to Food in Human Visual Cortex Revealed by Hypothesis-Free Voxel Decomposition
Meenakshi Khosla, Apurva Ratan Murty, Elizabeth Mieczkowski, Nancy Kanwisher, Massachusetts Institute of Technology, United States
Posters 2 Poster
Pacific Ballroom H-O
Fri, 26 Aug, 19:30 - 21:30 Pacific Time (UTC -7)
Prior work has identified regions of high-level visual cortex selectively responsive to faces, places, bodies, and words. However, this largely hypothesis-driven work cannot reveal how prominent these category selectivities are in the overall functional organization of the visual cortex, or what other un-hypothesized selectivities exist. Further, standard methods cannot detect selective neural populations that coexist with functionally distinct populations within voxels. To overcome these limitations, we applied a data-driven voxel decomposition analysis to identify a robust set of component response profiles consistent across subjects in a recently released public data set of fMRI responses to thousands of complex photographic stimuli (Allen et al., 2021). Four of the five top components revealed by our analysis were clearly selective for people, faces, scenes, and words. The analysis also revealed a novel component with a distinct anatomy that responded highly selectively to images of food. Alternative accounts based on lower-level visual features like color, shape or texture failed to account for the high-level category selectivity of this component. Analyses of independent data revealed the same top components, replicating these dominant dimensions, including a food-selective component, in new subjects.