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Perspectives of probabilistic inferences: Reinforcement learning and an adaptive network compared
1Max Planck Institute for Human Development, Berlin, Germany. rieskamp@mpib-berlin.mpg.de
Journal of Experimental Psychology. Learning, Memory, and Cognition
|November 8, 2006
Summary
People learn to make better inferences by selecting strategies based on expected success, updated through reinforcement learning. This strategy selection learning theory also explains why people persist with previously successful strategies, even when ineffective.
Area of Science:
- Cognitive Psychology
- Computational Neuroscience
Background:
- Individuals possess a repertoire of strategies for making inferences.
- Strategy selection learning theory posits that strategy choice is based on subjective expectations of success, updated via reinforcement learning.
Purpose of the Study:
- To compare strategy selection learning theory with an adaptive network model for inference.
- To investigate how individuals adapt their strategy use in dynamic environments.
Main Methods:
- Two experimental studies were conducted to test the predictive power of the two theories.
- Study 1 involved feedback-based learning to assess inference improvement.
- Study 2 introduced a dynamic environment where strategy performance varied.
Main Results:
- Strategy selection learning theory accurately predicted human inference improvement with feedback in Study 1.
- In Study 2, individuals exhibited an "inertia effect," persisting with previously successful strategies.
- The "inertia effect" was best explained by the strategy selection learning theory.
Conclusions:
- Strategy selection learning theory provides a robust framework for understanding human inference and strategy adaptation.
- Reinforcement learning and subjective expectations are key mechanisms in strategy selection.
- Human adaptation to changing environments can be limited by prior learning, a phenomenon well-modeled by strategy selection learning theory.
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