What is it about?
What if some signatures of bias are actually signs of optimal learning? We demonstrate that behavioral signatures in a simple reinforcement learning task (bandit task) attributed to biased inference also appear when the agent is an optimal Bayesian learner. We pinpoint why bias and optimality can look the same—and suggest tests to tell them apart.
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Why is it important?
The work presents two important advances. On the scientific front, the confound between optimal and biased learning re-frames what counts as evidence of bias. This has clear consequences for the research methodology we employ. On the technical front, our analytic treatment of the time-evolution of key quantities (beyond simulation alone) offers a transparent way to understand the learning dynamics of reinforcement learning algorithms.
Perspectives
My goal to write this paper was to help build mathematically robust theories of behavior. I believe that the analytical tools I use in this paper can be very broadly used in cognitive science to help us better understand the dynamics of learning algorithms.
Prakhar Godara
New York University
Read the Original
This page is a summary of: Apparent learning biases emerge from optimal inference: Insights from master equation analysis, Proceedings of the National Academy of Sciences, October 2025, Proceedings of the National Academy of Sciences,
DOI: 10.1073/pnas.2502761122.
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