Summary: Researchers have clarified new insights into a brain signal called “Reward Positivity” (RewP), which may help explain how simple pleasurable responses connect with more complex goal-directed behavior. This brief electrical response—appearing roughly 200 to 500 milliseconds after a positive outcome—has been used to study how people learn from surprising events. A recent theoretical review argues that past work has sometimes blurred the line between immediate rewards (for example, the taste of sugar) and goals (for example, choosing water to stay healthy), and that distinguishing these concepts improves both theory and clinical application.
In clinical depression, particularly among people who experience anhedonia (a reduced ability to feel pleasure), the Reward Positivity signal is reliably smaller. By reframing RewP as a marker of goal-related cognitive control—not merely a dopaminergic response to reward—researchers propose it could be used as a noninvasive biomarker to predict antidepressant response and to assess neural health in clinical trials.
Key Facts
- The Reward Positivity signal: An identifiable positive electrical deflection in EEG that appears several hundred milliseconds after a rewarding or confirming outcome, and which grows larger when the outcome is more surprising.
- Goal vs. reward distinction: A “reward” often invokes lower-level dopaminergic reactions (e.g., the hedonic pleasure of soda), whereas a “goal” reflects higher-level cognitive control about what is important to achieve (e.g., choosing water for health). The RewP appears to track goal-related prediction errors rather than simple hedonic receipt.
- Depression biomarker: Multiple studies show reduced RewP amplitude in individuals with depression. Clarifying what this signal indexes could refine how clinicians define and measure anhedonia and related dysfunctions.
- Clinical application potential: Because RewP can be recorded noninvasively with EEG and is straightforward to elicit with simple tasks, it may serve as an early neural outcome in clinical trials—indicating whether a treatment is restoring relevant brain processes before symptom changes become evident.
Source: University of New Mexico
James Cavanagh has been at the University of New Mexico for 13 years studying cognitive neuroscience. Supported in part by the National Institutes of Health, his work applies imaging and electrophysiological tools to better understand psychiatric and neurological disorders.
Cavanagh’s recent theoretical review, coauthored with Clay B. Holroyd, focuses on the Reward Positivity: a distinct EEG feature that reliably emerges as a burst of positive voltage about 200–500 milliseconds after positive feedback. This signal appears whenever a person receives confirming feedback—such as a small monetary reward, a “correct” notice, or a simple icon indicating success—and grows stronger with greater surprise.
“It hasn’t been a long time since it was discovered,” Cavanagh says. “We’re still working to understand precisely what it means and why it is so specific to this type of outcome.” He and his colleagues note that the RewP’s sensitivity to surprise aligns with core ideas from reinforcement learning, where prediction errors—differences between expected and actual outcomes—drive learning.
Their review argues for a clearer vocabulary: researchers should differentiate between hedonic rewards and goal-directed outcomes. While rewards and goals often coincide, they can diverge. For example, choosing water over soda at a fast-food restaurant separates a low-level reward (the sugary taste) from a higher-order goal (maintaining health). The authors suggest that RewP reflects a “goal prediction error”—a signal tied to whether an abstract, goal-relevant expectation has been met—rather than simply tracking hedonic reward receipt.
This shift in interpretation matters because it connects RewP to cognitive control systems that bridge high-level decisions and low-level reward processes. Cavanagh and collaborators propose that a critic-like architecture, similar to components of actor–critic models in reinforcement learning, produces this goal-related signal. This framework helps explain why RewP could be smaller in depression: the circuitry linking goal representation and learning may be compromised.
Cavanagh emphasizes the practical side of these theoretical advances. Funded partly by a grant studying anhedonia in depression, his team and others have documented reduced RewP in depressed samples, but the mechanism remains unclear. Better understanding the signal could enable clinicians to use RewP measurements as an early indicator of treatment effects. Several groups have shown that RewP amplitude can predict antidepressant response, suggesting it may act as a neural “preview” of recovery: if the signal strengthens shortly after treatment begins, the treatment may be restoring relevant neural processes even before subjective symptoms improve.
Rather than relying solely on self-reported symptom changes, Cavanagh proposes incorporating brief EEG tasks into phase II or III clinical trials to monitor whether brain markers associated with neural health are changing. Doing so could help identify promising interventions earlier, refine theoretical concepts like anhedonia to be more brain-aligned, and ultimately improve how treatments are evaluated.
Key Questions Answered:
A: Reward Positivity is an EEG signal—a brief positive deflection that occurs about half a second after a positive or confirming outcome. It scales with surprise: more unexpected positive outcomes produce a larger signal.
A: Because they recruit different processes. A reward reflects basic hedonic response, while a goal reflects a decision about what matters. Depression may blunt not only hedonic responses but also the higher-level goal systems that link values to behavior.
A: By using RewP as an early neural marker, clinicians could see whether treatments are affecting brain processes before patients report symptom relief. That could speed up decision-making in trials and clinical care, and help define treatment targets more precisely.
Editorial Notes:
- This article was edited by a Neuroscience News editor.
- Journal paper reviewed in full.
- Additional context added by staff to clarify theoretical and clinical implications.
About this neuroscience and depression research news
Author: Anna Padilla
Source: University of New Mexico
Contact: Anna Padilla – University of New Mexico
Image: Image credited to Neuroscience News
Original Research: Open access. “The Reward Positivity signals a goal prediction error” by James F. Cavanagh and Clay B. Holroyd. Trends in Cognitive Sciences. DOI: 10.1016/j.tics.2025.11.005
Abstract
The Reward Positivity signals a goal prediction error
The Reward Positivity (RewP) is an EEG feature that emerges following performance feedback and is commonly interpreted to index reward-prediction error signals. In contrast to that dominant view, Cavanagh and Holroyd argue that RewP is a distinct EEG feature that selectively responds to positive prediction errors and is superimposed on a common background signal. They propose RewP signals a goal prediction error: it is elicited by abstract, goal-related signals rather than by purely hedonic rewards. This goal prediction error appears to arise from a critic-like architecture linked to actor–critic frameworks in reinforcement learning, emphasizing RewP’s role in goal attainment and cognitive control rather than as a simple marker of reward receipt.