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The successor representation in human reinforcement learning.

I Momennejad1, E M Russek2, J H Cheong3

  • 1Department of Psychology, Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA. idam@princeton.edu.

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Summary

The successor representation offers a balance between flexible and efficient decision-making in humans. This computational model explains semi-flexible choice by predicting future events, impacting habit formation.

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Area of Science:

  • Neuroscience
  • Cognitive Science
  • Computational Neuroscience

Background:

  • Reward learning theories distinguish between deliberative (model-based) and habitual (model-free) choice algorithms.
  • Model-based algorithms are flexible but computationally expensive; model-free algorithms are efficient but rigid.

Purpose of the Study:

  • To investigate the successor representation (SR) as an intermediate algorithmic family balancing flexibility and efficiency in choice.
  • To examine the unique behavioral signature predicted by the SR's reliance on stored predictions of future states.

Main Methods:

  • Computational modeling of reward learning algorithms.
  • Behavioral experiments with human participants to test predictions of the SR.
  • Analysis of choice adjustments following changes in task sequence and reward structure.

Main Results:

  • Human participants demonstrated insensitivity to changes in task event sequences, consistent with SR predictions.
  • Participants showed flexible adjustment to changes in reward outcomes, also aligning with SR.
  • The SR model successfully predicted a unique pattern of differential sensitivity to task changes.

Conclusions:

  • The successor representation serves as a computational substrate for semi-flexible choice in humans.
  • This framework introduces a more nuanced, cognitive perspective on habit formation.
  • The SR bridges the gap between purely deliberative and purely habitual decision-making processes.