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Operant Procedures for Assessing Behavioral Flexibility in Rats
Published on: February 15, 2015
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Humans forage for reward in reinforcement learning tasks
Meriam Zid1, Veldon-James Laurie1, Jorge Ramírez-Ruiz1
1Department of Neuroscience, University of Montreal, Montreal, QC , H3T 1J4, Canada.
Biorxiv : the Preprint Server for Biology
|July 19, 2024
Summary
Humans make decisions by comparing options to a threshold, not just by comparing alternatives. This new "foraging" model better explains decision-making and choice repetition in uncertain environments.
Area of Science:
- Cognitive Neuroscience
- Decision Science
- Behavioral Economics
Background:
- Classic models propose comparing option values to choose.
- Ethological views suggest committing to an option until its value decreases.
Purpose of the Study:
- To investigate which decision-making model (compare-alternative vs. compare-to-threshold) better describes human behavior in uncertain environments.
- To develop and validate a novel reinforcement-learning (RL) model based on compare-to-threshold computations.
Main Methods:
- Participants engaged in classic compare-alternative decision tasks.
- A new computational model, termed "foraging," was developed based on compare-to-threshold logic.
- Model performance was evaluated against participant behavior and compared to existing RL models.
Main Results:
- Human decision-making in compare-alternative tasks aligns with compare-to-threshold computations.
- The "foraging" model demonstrated superior fit to participant behavior compared to previous RL models.
- The foraging model accurately predicted choice repetition and held-out participant data.
Conclusions:
- Human sequential decision-making in uncertain environments relies on compare-to-threshold mechanisms.
- The foraging model offers a more accurate computational framework for understanding these decisions than traditional compare-alternative models.
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