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Stable Representations of Decision Variables for Flexible Behavior.

Bilal A Bari1, Cooper D Grossman1, Emily E Lubin1

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The brain uses stable neural signals in the medial prefrontal cortex to represent decision variables, enabling flexible choices in dynamic environments. This contrasts with reward prediction errors, which update these variables.

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Behavioral Neuroscience

Background:

  • Decisions are made in dynamic environments, with action probabilities influenced by decision variables in reinforcement learning.
  • Reward prediction errors update these variables, but the neural representation of the variables themselves remains unclear.
  • While reward prediction errors are linked to dopamine neurons, the brain's mechanism for representing decision variables is not well understood.

Purpose of the Study:

  • To investigate how the brain represents decision variables crucial for flexible behavior in dynamic environments.
  • To identify the neural substrates responsible for maintaining and utilizing decision variables over time.

Main Methods:

  • Mice were trained on a dynamic foraging task with changing reward probabilities for different actions.
  • Neural activity was recorded in medial prefrontal cortex (mPFC) and anterolateral motor cortex (ALM) during task performance.
  • Firing rates of neurons were analyzed to assess the representation of relative and total action values.

Main Results:

  • Neurons in the medial prefrontal cortex (including projections to the dorsomedial striatum) showed persistent firing rate changes over long timescales.
  • These persistent changes stably represented relative action values (influencing choices) and total action values (influencing response times) with slow decay.
  • Decision variables were poorly represented in the anterolateral motor cortex, despite its necessity for choice generation.

Conclusions:

  • A stable neural mechanism in the medial prefrontal cortex underlies the representation of decision variables.
  • This mechanism supports flexible behavioral adaptation in environments with changing reward contingencies.
  • The findings differentiate the neural coding of stable decision variables from transient reward prediction errors.