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Keypoint-based modeling reveals fine-grained body pose tuning in superior temporal sulcus neurons
Rajani Raman1,2, Anna Bognár1,2, Ghazaleh Ghamkhari Nejad1,2
1Department of Neurosciences, KU Leuven, Leuven, Belgium.
Nature Communications
|July 2, 2025
Summary
Researchers used deep learning to analyze monkey body poses and viewpoint, revealing how the Superior Temporal Sulcus (STS) brain region processes social signals. This deep learning approach enhances understanding of primate non-verbal communication.
Area of Science:
- Neuroscience
- Primate behavior
- Computer vision
Background:
- Body pose and orientation are crucial for primate social communication.
- The Superior Temporal Sulcus (STS) is implicated in processing visual social cues.
- Understanding neural representations of body information is key.
Purpose of the Study:
- Investigate neural tuning for body pose and viewpoint in the primate STS.
- Utilize deep learning and fMRI data to model neural responses.
- Identify effective body parts and views for STS neurons.
Main Methods:
- Applied deep learning algorithms to extract body poses from monkey videos and a monkey avatar.
- Used keypoint-based principal component regression with cross-validation to model neural selectivity.
- Employed model inversion to determine influential body parts and viewpoints.
Main Results:
- Mid-STS units responded to view-dependent 2D keypoint representations, with neuron-specific body part tuning.
- Anterior STS units showed better performance with view-dependent 3D models and mirror-symmetric viewpoint tuning.
- View-independent models performed poorly, indicating the importance of viewpoint in STS processing.
Conclusions:
- The primate visual system encodes socially relevant body cues using keypoint-based representations.
- STS neurons exhibit complex tuning for body pose and viewpoint.
- This study advances our understanding of body pose representation in the STS.
