December 4, 2025PLoS Computational BiologyOpen Access

Neural mechanisms of flexible perceptual inference

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Authors

JSJohn Schwarcz RRRobert C. ReinerUniversity of Washington

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Implication

Observational analysis reveals that reinforcement learning methods enable adaptive behavior in mice, indicating cognitive flexibility in uncertain environments.

Key Points

Mice demonstrate first-trial behavioral adaptation driven by inference rather than reward feedback, indicating rapid response to context shifts.
The change-detection task requires tracking latent context while adapting to implicit shifts in the environment.
Artificial neural networks trained via reinforcement learning achieve near-optimal performance incorporating Bayesian inference-like mechanisms.
These findings underscore cognitive flexibility as essential in enabling adaptive behavior under uncertainty.

Cite This Study

Schwarcz et al. (2025) studied this question.

synapsesocial.com/papers/6930e8b6ea1aef094cca3045 https://doi.org/10.1371/journal.pcbi.1013675

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Implication

Observational analysis reveals that reinforcement learning methods enable adaptive behavior in mice, indicating cognitive flexibility in uncertain environments.