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An Operant Intra-/Extra-dimensional Set-shift Task for Mice

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Category selectivity as a window into behavioral relevance.

Leonard E van Dyck^1,2, Katharina Dobs^2,3

¹Department of Psychology, Justus Liebig University Giessen, Germany.

Cognitive Neuroscience

|November 23, 2025

Summary

This summary is machine-generated.

Understanding the occipitotemporal cortex (OTC) requires integrating category selectivity with behavioral relevance. This approach, supported by deep neural network modeling, offers a more comprehensive view of neural representations supporting goal-directed behavior.

Keywords:

Category selectivity behavioral relevance deep neural networks functional specialization occipitotemporal cortex

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Area of Science:

Neuroscience
Cognitive Science

Background:

The occipitotemporal cortex (OTC) is crucial for visual processing.
Current understanding often focuses on category selectivity (e.g., faces, scenes).
Ritchie et al. propose shifting focus to behavioral relevance for a deeper OTC understanding.

Purpose of the Study:

To explore the relationship between category selectivity and behavioral relevance in the OTC.
To argue for an integrated approach rather than opposing views.
To highlight the utility of computational modeling in understanding neural representations.

Main Methods:

Review of existing literature on occipitotemporal cortex function.
Discussion of category selectivity and behavioral relevance frameworks.
Emphasis on computational modeling, specifically deep neural networks.

Main Results:

Category selectivity offers valuable insights into the organization of cortical representations.
Behavioral relevance is essential for understanding flexible, goal-directed behavior.
Deep neural networks provide a powerful framework for investigating the link between neural representations and behavior.

Conclusions:

Category selectivity and behavioral relevance should be viewed as complementary, not opposing.
An integrated approach is necessary for a comprehensive understanding of the OTC.
Computational modeling advances facilitate the study of neural representations supporting behavior.