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bioRxivNeurosciencePreprint

Object dimensions underlying food representations in visual cortex

Davide CortinovisiD, Giulia Orlandi, Lotte van Campenhout, Stefania BracciiD

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Abstract

Recent work has revealed two food-selective areas in the lateral and ventromedial occipitotemporal cortex (OTC). These studies have shown that food selectivity in these regions cannot be explained by mid-level features like shape, texture, or colour but differences in their representational content remain unclear. Across two fMRI experiments conducted in the same group of participants, we characterized the dimensions underlying food representations in lateral and ventral OTC by examining the contribution of action-related object properties, such as manipulability, relevant to object interaction, and visual features, such as colour and ensemble statistics, relevant to object recognition. Our results reveal a clear dissociation between lateral and ventral OTC, indicating that food representations in these regions reflect distinct computational constraints. In lateral OTC, food representations were primarily associated with action-related properties shared between food and other graspable objects, whereas in ventral OTC, food representations were sensitive to surface object properties, such as colour and ensemble configuration. Consistent with this distinction, lateral OTC showed greater sensitivity to individual objects against distinctive background and responded equally to colour and greyscale stimuli, while ventral OTC exhibited greater sensitivity to coloured stimuli and ensembles with no distinctive background. Finally, topographic artificial neural networks implementing architectural constraints meant to capture OTC spatial organization similarly exhibited two dissociable clusters of food-selective units based on sensitivity to ensemble statistics. Together, these findings suggest that lateral food representations reflect action-relevant properties shared with other manipulable objects, whereas ventral food representations arise from surface-based visual features critical for food identification.

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