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Operant Protocols for Assessing the Cost-benefit Analysis During Reinforced Decision Making by Rodents

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Published on: September 10, 2018

Cost-Benefit Arbitration Between Multiple Reinforcement-Learning Systems.

Wouter Kool¹, Samuel J Gershman^1,2, Fiery A Cushman¹

¹1 Department of Psychology, Harvard University.

Psychological Science

|July 22, 2017

Summary

This summary is machine-generated.

Humans balance habitual actions and planned behavior by comparing the costs and benefits of each system. This adaptive control shifts towards planning when it

Keywords:

cognitive control decision making open data open materials reinforcement learning

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Area of Science:

Cognitive psychology
Neuroscience
Decision-making

Background:

Human behavior is influenced by both habitual responses and goal-directed planning.
Reinforcement-learning theories distinguish between model-free (habit) and model-based (planning) systems.
The mechanism for allocating control between these systems remains unclear.

Purpose of the Study:

To investigate how individuals arbitrate control between model-free and model-based systems.
To test the hypothesis that arbitration is based on comparing task-specific costs and benefits.
To understand the adaptive balance between habitual and planned actions.

Main Methods:

Two experiments were conducted to observe human decision-making.
Participants' allocation of control between model-based and model-free systems was measured.
The influence of accuracy and reward amplification on control allocation was manipulated.

Main Results:

Individuals increased model-based control when it offered higher accuracy than model-free control.
Reward amplification significantly boosted model-based control when accuracy differed between systems.
Reward amplification had no effect when both systems provided equivalent accuracy.

Conclusions:

Human arbitration between habitual and planned action is driven by an on-line cost-benefit analysis.
The decision to employ planning (model-based) or habit (model-free) is adaptive and context-dependent.
This adaptive mechanism optimizes behavior based on expected costs and benefits.