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Humans Adaptively Select Different Computational Strategies In Different Learning Environments.

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Humans Adaptively Select Different Computational Strategies In Different Learning Environments.

Related Experiment Video

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Humans adaptively select different computational strategies in different learning environments.

Pieter Verbeke¹, Tom Verguts¹

¹Department of Experimental Psychology, Ghent University.

Psychological Review

|April 15, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

Humans adapt their learning strategies based on environmental complexity. Simple environments use flat learning, while complex ones employ hierarchical models for optimal performance in reinforcement learning tasks.

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Area of Science:

Cognitive Science
Computational Neuroscience
Behavioral Economics

Background:

The Rescorla-Wagner rule is a dominant model for human reinforcement learning but fails in complex settings.
Hierarchical extensions exist, but conditions for their adaptive use and human implementation are unclear.

Purpose of the Study:

To determine when flat versus hierarchical learning strategies are adaptive.
To investigate which learning strategies humans implement in varying environments.

Main Methods:

A nested modeling approach was used to evaluate multiple computational models.
10 empirical datasets (N=407) across three distinct reinforcement learning environments were analyzed.
Models were assessed computationally for performance and empirically for human data fit.

Main Results:

Different environments necessitate different learning strategies for optimal performance.
Humans adaptively select learning strategies that align with environmental complexity.
Flat learning models best explained behavior in simple, stable environments.
Hierarchical learning models were superior in more complex environments.

Conclusions:

Human learning strategies are flexible and adapt to environmental demands.
The choice between flat and hierarchical reinforcement learning is driven by environmental complexity.
Findings support the adaptive selection of learning strategies by humans in reinforcement learning.