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Updated: Feb 7, 2026

Experimental Methods to Study Human Postural Control
Published on: September 11, 2019
View-invariant representation of hand postures in the human lateral occipitotemporal cortex
Stefania Bracci1, Alfonso Caramazza2, Marius V Peelen3
1Center for Mind/Brain Sciences, University of Trento, Rovereto, Italy; Laboratory of Biological Psychology, University of Leuven, Leuven, Belgium.
Abstract:
Understanding other people's actions and mental states includes the interpretation of body postures and movements. In particular, hand postures are an important channel to signal both action and communicative intentions. Recognizing hand postures is computationally challenging because hand postures often differ only in the subtle configuration of relative finger positions and because visual characteristics of hand postures change across viewpoints. To allow for accurate interpretation, the brain needs to represent hand postures in a view-invariant but posture-specific manner. Here we test for such representations in hand-, body-, and object-selective regions of the lateral occipitotemporal cortex (LOTC). We used multivariate pattern analysis of fMRI data to test for view-specific and view-invariant representations of individual hand postures, separately for two domains: action-related postures (e.g., a precision grasp) and communicative postures (e.g., thumbs up). Results showed that hand-selective LOTC, but not nearby body- and object-selective LOTC, represented hand postures in a view-invariant manner, with relatively similar activity patterns to the same hand posture seen from different viewpoints. View invariance was equally strong for action and communicative postures. By contrast, object-selective cortex represented hand postures in a view-specific manner. These results indicate a role for hand-selective LOTC in solving the view-invariance problem for individual hand postures. View-invariant representations of hand postures in this region may then be accessed and further interpreted by multiple downstream systems to inform high-level judgments related to action understanding, emotion recognition, and non-verbal communication.
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