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Related Experiment Videos

Beyond Reward Prediction Errors: Human Striatum Updates Rule Values During Learning.

Ian Ballard1, Eric M Miller2, Steven T Piantadosi3

  • 1Stanford Neurosciences Graduate Training Program, Stanford University, Stanford, CA, USA.

Cerebral Cortex (New York, N.Y. : 1991)
|October 18, 2017
PubMed
Summary
This summary is machine-generated.

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The brain

Area of Science:

  • Neuroscience
  • Cognitive Psychology
  • Decision Making

Background:

  • Human categorization relies on learned rules.
  • Rule learning can occur implicitly via reinforcement learning or explicitly via reasoning.
  • The striatum's role in explicit rule learning is not well understood.

Purpose of the Study:

  • Investigate striatal involvement in explicit rule-based categorization.
  • Determine if striatal signals reflect reinforcement learning prediction errors or a Bayesian surprise signal during explicit rule learning.
  • Identify brain regions involved in updating explicit categorization rules.

Main Methods:

  • Functional magnetic resonance imaging (fMRI) during a categorization task.
  • Analysis of striatal and caudal inferior frontal sulcus (cIFS) activity.

Related Experiment Videos

  • Modeling of feedback using a Bayesian rule-learning framework and reinforcement learning.

    • Main Results:

    • Striatal activation to feedback scaled with a Bayesian "surprise" signal, not a reinforcement learning prediction error.
    • Striatal and cIFS activity correlated with updating the probability of discriminative rules.
    • Evidence suggests explicit rule learning involves updating rule values.

    Conclusions:

    • The striatum, alongside the cIFS, plays a role in updating categorization rule values during explicit reasoning.
    • Striatal responses during explicit rule learning are better explained by Bayesian surprise than reinforcement learning prediction errors.
    • This research sheds light on the neural mechanisms of explicit rule-based learning.