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Pavlovian Conditioned Approach Training in Rats
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Reward-Mediated, Model-Free Reinforcement-Learning Mechanisms in Pavlovian and Instrumental Tasks Are Related.

Neema Moin Afshar1, François Cinotti2, David Martin3

  • 1Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut 06511.

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|October 10, 2022
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Summary

Reinforcement learning mechanisms in rats show conserved model-free computations across Pavlovian and instrumental tasks. Sign-tracking behavior is linked to model-free learning, suggesting common pathways for associative learning.

Keywords:
computational psychiatrydecision-makingincentive saliencemodel-based learningmodel-free learning

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Area of Science:

  • Neuroscience
  • Computational Psychiatry
  • Behavioral Economics

Background:

  • Model-free and model-based computations distinctly update action values in decision-making.
  • It remains unclear if these reinforcement learning (RL) mechanisms are conserved across Pavlovian and instrumental tasks.
  • Individual differences in incentive salience attribution (sign-tracking vs. goal-tracking) may relate to RL variations.

Purpose of the Study:

  • To investigate if Pavlovian and instrumental learning processes are driven by common RL mechanisms.
  • To assess the conservation of model-free and model-based RL systems across different behavioral tasks in individual animals.
  • To examine the association between sign-tracking/goal-tracking behaviors and RL strategies.

Main Methods:

  • A within-subject design was employed in male rats.
  • Sign-tracking and goal-tracking behaviors were assessed using a Pavlovian conditioned approach task.
  • Instrumental behavior was characterized using a multistage decision-making (MSDM) task.

Main Results:

  • Sign-tracking behavior was significantly associated with greater model-free RL.
  • Model-free RL in the Pavlovian task correlated with model-free RL in the MSDM task.
  • No significant association was found between sign-tracking and model-based RL.

Conclusions:

  • Pavlovian and instrumental learning processes appear to be driven by conserved reinforcement-learning mechanisms, particularly model-free computations.
  • Sign-tracking behavior is a reliable indicator of heightened model-free RL.
  • These findings offer insights into the computational underpinnings of associative learning with implications for understanding normal and abnormal states.