Evidence map›Paper›PMID 39418537›Full record

ReviewAnnual review of psychology2025

Representation of Anticipated Rewards and Punishments in the Human Brain.

Ziv Ben-Zion, Ifat Levy

Abstract readReview
In one paragraph

Review in Annual review of psychology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Polygenic Risks for Depression and Neural Responses to Reward and Punishment in Young Adults.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2025
    Article
  5. Article
  6. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Ziv Ben-ZionDepartment of Psychiatry, Yale School of Medicine, Yale University, New Haven, Connecticut, USA.
Ifat LevyWu Tsai Institute, Yale University, New Haven, Connecticut, USA.

Funding

Individual differences in decision making under uncertaintyR01MH118215 · NIMH · YALE UNIVERSITY · PI LEVY, IFAT · 2019 to 2023
$3.4M
Neural computations of learning, decision-making and memoryR01MH133886 · NIMH · YALE UNIVERSITY · PI Ifat Levy · 2024 to 2026
$2.1M
NIMH NIH HHS R01 MH118215NIMH NIH HHS R01 MH133886
6 · The paper itself

Abstract

Subjective value is a core concept in neuroeconomics, serving as the basis for decision making. Despite the extensive literature on the neural encoding of subjective reward value in humans, the neural representation of punishment value remains relatively understudied. This review synthesizes current knowledge on the neural representation of reward value, including methodologies, involved brain regions, and the concept of a common currency representation of diverse reward types in decision-making and learning processes. We then critically examine existing research on the neural representation of punishment value, highlighting conceptual and methodological challenges in human studies and insights gained from animal research. Finally, we explore how individual differences in reward and punishment processing may be linked to various mental illnesses, with a focus on stress-related psychopathologies. This review advocates for the integration of both rewards and punishments within value-based decision-making and learning frameworks, leveraging insights from cross-species studies and utilizing ecological gamified paradigms to reflect real-life scenarios.

Indexed as

Anticipation, PsychologicalBrainDecision MakingPunishmentRewardAnimalsHumansdecision makinglearningmental healthneuroeconomicssubjective valuevalence

Identifiers

PMID39418537
PMCPMC11930275

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.