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Hindsight Biases

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Confirmation Biases

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Related Experiment Video

Updated: Jun 29, 2025

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Published on: September 19, 2012

Hidden Reward: Affect and Its Prediction Errors as Windows Into Subjective Value.

Marius C Vollberg^1,2,3, David Sander^2,3

¹Department of Psychology, University of Amsterdam.

Current Directions in Psychological Science

|April 2, 2024

Summary

This summary is machine-generated.

Reinforcement learning concepts, like prediction errors, can explain subjective feelings and value. Researchers should test these concepts for affect research, not assume they apply directly.

Keywords:

affect prediction errors reinforcement learning subjective value

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Area of Science:

Cognitive Science
Neuroscience
Psychology

Background:

Reinforcement learning (RL) concepts are increasingly applied to the study of affect.
Reward prediction errors are foundational in RL research on adaptive behavior.
Historically, RL research focused on observable rewards, with less attention to subjective feelings.

Purpose of the Study:

To explore the application of RL concepts to affect.
To investigate the explanatory power of affect-based prediction errors.
To propose RL concepts for elucidating subjective value.

Main Methods:

Review and conceptual analysis of reinforcement learning principles applied to affect.
Examination of existing research on reward prediction errors in humans and animals.
Discussion of the shift towards affectivism in psychological research.

Main Results:

Affective prediction errors (differences between expected and experienced feelings) offer explanatory power beyond observable rewards.
Subjective value can be elucidated by applying RL concepts to affect.
Human ability to report expected and experienced affect is a key factor.

Conclusions:

Applying RL concepts to affect shows promise for understanding subjective value.
Scientists should critically test RL concepts for their applicability to affect.
Future research should focus on the unique aspects of affective prediction errors.