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Updated: Sep 10, 2025

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Separable neural signals for reward and emotion prediction errors.

Joseph Heffner^1,2, Romy Frömer³, Matthew R Nassar^4,5

¹Cognitive and Psychological Sciences, Brown University, Providence, RI, USA.

Nature Communications

|August 22, 2025

Summary

Deviations from emotion expectations, or affective prediction errors, influence behavior, especially in social learning. Neural signals for emotion and reward are separable, with distinct brain potentials tracking each type of prediction error.

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Area of Science:

Neuroscience
Cognitive Science
Social Learning

Background:

Reinforcement learning traditionally emphasizes reward prediction errors.
Emerging research highlights the role of affective prediction errors (deviations from emotion expectations) in shaping behavior.
The neural basis for distinguishing emotion and reward signals remains unclear.

Purpose of the Study:

To investigate the neural signatures of reward and affective prediction errors during social learning.
To determine if emotion and reward signals are neurally separable.

Main Methods:

Utilized electroencephalography (EEG) during a social learning task.
Analyzed event-related potentials (ERPs) to identify neural correlates of prediction errors.
Correlated neural activity with behavioral choices.

Main Results:

Behavioral data showed affective prediction errors influenced choices in uncertain social situations.
Distinct event-related potentials (ERPs) reflected reward and affective prediction errors.
The feedback-related negativity (FRN) was primarily associated with reward prediction errors.
The P3b component was more consistently linked to affective prediction errors and predicted subsequent choices.

Conclusions:

Provides evidence for a neurobiologically distinct emotion learning signal.
Demonstrates neural separability between emotion and reward prediction error signals.
Highlights the mechanistic role of affective prediction errors in social learning.