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Related Experiment Video

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A Fully Automated Rodent Conditioning Protocol for Sensorimotor Integration and Cognitive Control Experiments

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Published on: April 15, 2014

Cognitive control predicts use of model-based reinforcement learning.

A Ross Otto¹, Anya Skatova, Seth Madlon-Kay

¹New York University.

Journal of Cognitive Neuroscience

|August 30, 2014

Summary

Cognitive control mechanisms influence reinforcement learning (RL) strategies. Individual differences in using contextual information predict model-based RL behavior, suggesting shared underlying processes for controlled choices.

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Area of Science:

Neuroscience
Cognitive Psychology
Computational Psychiatry

Background:

Decision-making research often assumes separate, competing valuation systems.
Habitual (automatic) and goal-directed (controlled) choices may stem from model-free and model-based reinforcement learning (RL).
The dominance of one system over the other in behavior remains under investigation.

Purpose of the Study:

To investigate the cognitive and computational processes governing the interplay between model-free and model-based RL.
To explore the role of cognitive control in modulating RL strategy selection.
To determine if individual differences in cognitive control predict model-based RL behavior.

Main Methods:

Leveraged the theoretical framework of cognitive control.
Assessed individual differences in utilizing goal-related contextual information in cognitive control tasks.
Examined the relationship between these individual differences and model-based RL behavior in a sequential choice task.

Main Results:

Individual differences in cognitive control, specifically the use of contextual information to override habitual responses, predicted model-based RL behavior.
A significant behavioral correspondence was observed between cognitive control performance and model-based RL strategy.
This suggests shared underlying computational mechanisms.

Conclusions:

Cognitive control processes are implicated in the selection of reinforcement learning strategies.
Mechanisms underlying controlled behavior may explain the interaction between model-based and model-free RL.
This research bridges cognitive control and computational approaches to decision-making.