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Pavlovian Conditioned Approach Training in Rats

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Linking confidence biases to reinforcement-learning processes.

Nahuel Salem-Garcia¹, Stefano Palminteri², Maël Lebreton¹

¹Swiss Center for Affective Science, University of Geneva.

Psychological Review

|May 8, 2023

Summary

People consistently misjudge their economic performance due to overconfidence and valence-induced confidence biases. These biases in decision-making originate from underlying learning biases in reinforcement learning (RL) tasks.

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Area of Science:

Cognitive Science
Neuroeconomics
Behavioral Economics

Background:

Individuals exhibit overconfidence and valence-induced confidence biases in economic tasks.
These biases persist even in reinforcement learning (RL) contexts with immediate feedback, posing a theoretical challenge.

Purpose of the Study:

To investigate the origins of confidence biases in economic decision-making.
To test the hypothesis that metacognitive biases stem from learning biases in RL.

Main Methods:

Utilized data from multiple experiments assessing instrumental choices and confidence judgments.
Employed a reinforcement-learning model with context-dependent learning and confirmatory updating to analyze participant behavior.
Examined the relationship between learning model parameters and metacognitive biases.

Main Results:

Participant choices were best explained by an RL model with context-dependent learning and confirmatory updating.
Biased confidence judgments were linked to overweighting the learned value of chosen options.
Individual learning bias parameters predicted individual metacognitive biases.

Conclusions:

Metacognitive biases in economic tasks and RL contexts likely originate from biased learning computations.
Confirmatory updating and outcome context-dependency in learning contribute to overconfidence and valence-induced confidence biases.
Understanding learning biases is crucial for explaining and potentially mitigating metacognitive errors.