The representation of shape and texture in category‐selective regions of ventral‐temporal cortex

Abstract

Neuroimaging studies using univariate and multivariate approaches have shown that the fusiform face area (FFA) and parahippocampal place area (PPA) respond selectively to images of faces and places. The aim of this study was to determine the extent to which this selectivity to faces or places is based on the shape or texture properties of the images. Faces and houses were filtered to manipulate their texture properties, while preserving the shape properties (spatial envelope) of the images. In Experiment 1, multivariate pattern analysis (MVPA) showed that patterns of fMRI response to faces and houses in FFA and PPA were predicted by the shape properties, but not by the texture properties of the image. In Experiment 2, a univariate analysis (fMR‐adaptation) showed that responses in the FFA and PPA were sensitive to changes in both the shape and texture properties of the image. These findings can be explained by the spatial scale of the representation of images in the FFA and PPA. At a coarser scale (revealed by MVPA), the neural selectivity to faces and houses is sensitive to variation in the shape properties of the image. However, at a finer scale (revealed by fMR‐adaptation), the neural selectivity is sensitive to the texture properties of the image. By combining these neuroimaging paradigms, our results provide insights into the spatial scale of the neural representation of faces and places in the ventral‐temporal cortex.

Cortical topographic motifs emerge in a self-organized map of object space

“…Although different regions of the brain are systematically activated by images of animals or objects of either big or small sizes, this result does not therefore directly imply that these map units are driven by something very abstract about what it means to be animate or inanimate, big or small. Rather, increasing empirical evidence indicates that responses along this purportedly “high-level” visual cortex have a substantial degree of tuning at a more primitive visuo-statistical level ( 11 – 13 , 55 ). To this end, the next signature of ventral stream topography that we probed is its sensitivity to images with more primitive “mid-level” image statistics preserved ( 11 ).…”

“…At a macro-scale, there are two major object dimensions that have been shown to elicit systematic large-scale response topographies, related to the distinction between animate and inanimate objects (4)(5)(6)(7) and the distinction between objects of different real-world sizes (8)(9)(10). Further research has shown that these seemingly high-level animacy and object size distinctions are primarily accounted for by differences in tuning along more primitive visuo-statistical features that meaningfully covary with these high-level properties [e.g., at the level of localized texture and coarse form information; (11)(12)(13)].…”

Visual object topographic motifs emerge from self-organization of a unified representational space

“…Even though different regions of the brain are systematically activated by images of animals or objects of either big or small sizes, this result does not therefore directly imply that these map units are driven by something very abstract about what it means to be animate or inanimate, big or small. Rather, increasing empirical evidence indicates that responses along this purportedly “high-level” visual cortex have a significant degree of tuning at a more primitive visuo-statistical level ( 17 – 19 , 17 ). To this end, the next signature of ventral stream topography that we probed is its sensitivity to images with more primitive “mid-level” image statistics preserved ( 17 ).…”

“…Further research has shown that these seemingly high-level animacy and object size distinctions are in fact primarily accounted for by differences in tuning along more primitive visuo-statistical features that meaningfully co-vary with these high-level properties (e.g. at the level of localized texture and coarse form information; Long et al, 2018, Jagadeesh & Gardner, 2022, Coggan et al, 2022).…”

Coherent categorical information triggers integration-related alpha dynamics

“…This sweet spot might be the one also used by the brain when implementing integration. However, our study cannot entirely clarify whether the integration-related alpha activity is indeed triggered by a more abstract coherence in basic-level category, presumably coded in high-level visual cortex (Walther et al, 2009;Proklova et al, 2016) or by the spatiotemporal coherence of visual features associated with a category (Coggan et al, 2019(Coggan et al, , 2022Robert et al, 2023). More research is needed to clarify this question.…”