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Mice decision-making reveals how compressed internal representations, balancing accuracy and efficiency, shape reward expectations and behavior. This impacts understanding cognitive processes in dynamic environments.

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Cognitive Science

Background:

  • Adaptive behavior relies on reward expectations derived from internal environmental knowledge.
  • Sensory measurement errors or representational cost minimization can lead to inaccurate internal knowledge.
  • Understanding how internal representations influence reward-based computations is crucial.

Purpose of the Study:

  • To investigate how features of internal representations affect reward expectations.
  • To study mouse behavior and dopaminergic activity during time-based decision-making tasks.
  • To explore the impact of representational efficiency on cognitive variables.

Main Methods:

  • Utilized a reinforcement learning agent to model animal performance.
  • Analyzed behavioral choices and dopaminergic activity in mice.
  • Examined the relationship between compressed internal representations and observed data.

Main Results:

  • Several representations modeled overall task performance.
  • Highly compressed representations accurately reproduced choice behavior and dopaminergic activity co-variability.
  • These representations predicted response time distributions matching animal behavior.

Conclusions:

  • Representational efficiency constraints influence the encoding of dynamic cognitive variables.
  • Compressed internal representations are key to understanding reward-based computations.
  • The study provides insights into the neural basis of decision-making under uncertainty.