Definition and research guide
This page combines a MeSH definition, publication statistics and selected research abstracts. It is a starting point for reading the literature, not a medical textbook or a systematic evidence review.
Content available: 3 complete abstracts (547 words), 9 references. Full article text is not stored or reproduced here; follow the PMID, DOI or PMC links to read the original sources and their access terms.
Definition
An object or a situation that can serve to reinforce a response, to satisfy a motive, or to afford pleasure.
Definition: NLM Medical Subject Headings (MeSH) scope note (Public domain (U.S. National Library of Medicine); attribution requested). View the MeSH record.
Also known as: Rewards.
Where it fits in the MeSH classification
- Broader topics
- Reinforcement, Psychology
- Narrower topics
- Token Economy
- See also
- Delay Discounting
Research landscape
PubMed indexes 30,613 records tagged with the MeSH heading “Reward” (counts retrieved 2026-10-31). 8,868 of them (29.0%) were published from 2020 onward, and the busiest period so far is 2010-2019 with 12,727 records.
Reviews account for 3,258 records (10.6%) and meta-analyses for 257. Randomized controlled trials number 1,194 (3.9%) as a share of all records on the topic.
Show the data
| Period | Records |
|---|---|
| through 1999 | 4,168 |
| 2000-2009 | 4,850 |
| 2010-2019 | 12,727 |
| 2020 onward | 8,868 |
Publication-type chart data
| Type | Records | Share |
|---|---|---|
| Reviews | 3,258 | 10.6% |
| Meta-analyses | 257 | 0.8% |
| Randomized controlled trials | 1,194 | 3.9% |
Method: PubMed query "Reward"[MeSH Terms] with publication-type and date filters, retrieved 2026-10-31.
Reading the evidence in psychiatry and psychology
Research on mental health and behaviour relies on questionnaires, interviews and rating scales rather than physical measurements, so how a condition was defined and measured shapes every result. Many studies are observational or small, placebo responses can be large, and findings in one group or culture may not carry over to another. Nothing on this page is advice about diagnosis or treatment; anyone worried about their mental health should talk to a qualified clinician. Check how outcomes were measured, who took part and how long they were followed.
Evidence from open-access studies
These open-access studies come from PubMed’s best-match results for this heading (papers whose main topic it is), ranked here by citation count, with one recent addition. Each abstract is reproduced in full under its recorded open licence so that you can read the authors’ own words. They illustrate the literature; they are not a systematic summary of it. Citation counts measure attention, not reliability or clinical benefit.
| Study / authors | Year | Source label / design | Europe PMC citations | Licence / source |
|---|---|---|---|---|
| Non-invasive ultrasonic neuromodulation of the human nucleus accumbens impacts reward sensitivity | 2025 | Research article Design not recorded in structured metadata | 19 | cc by PMID 41309575 · DOI · PMC12660320 |
| The gut-brain vagal axis governs mesolimbic dopamine dynamics and reward events | 2026 | Research article Design not recorded in structured metadata | 18 | cc by PMID 41616060 · DOI · PMC12857734 |
| A computational approach to understanding effort-based decision-making in depression | 2025 | Research article Design not recorded in structured metadata | 16 | cc by PMID 41059633 · DOI · PMC12527505 |
Non-invasive ultrasonic neuromodulation of the human nucleus accumbens impacts reward sensitivity [1]
Abstract reading summary — source excerpts
- Opening excerpt
- Precisely neuromodulating deep brain regions could bring transformative advancements in both neuroscience and treatment.
- Closing excerpt
- These findings establish TUS as a viable approach for non-invasive deep-brain neuromodulation.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (148 words)
Precisely neuromodulating deep brain regions could bring transformative advancements in both neuroscience and treatment. We demonstrate that non-invasive transcranial ultrasound stimulation (TUS) can selectively modulate deep brain activity and affect learning and decision making, comparable to deep brain stimulation (DBS). We tested whether TUS could causally influence neural and behavioural responses by targeting the nucleus accumbens (NAcc) using a reinforcement learning task. Twenty-six healthy adults completed a within-subject TUS-fMRI experiment with three conditions: TUS to the NAcc, dorsal anterior cingulate cortex (dACC), or Sham. After TUS, participants performed a probabilistic learning task during fMRI. TUS-NAcc altered BOLD responses to reward expectation in the NAcc and surrounding areas. It also affected reward-related behaviours, including win-stay strategy use, learning rate following rewards, learning curves, and repetition rates of rewarded choices. DBS-NAcc perturbed the same features, confirming target engagement. These findings establish TUS as a viable approach for non-invasive deep-brain neuromodulation.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 41309575 · DOI · PMC12660320
The gut-brain vagal axis governs mesolimbic dopamine dynamics and reward events [2]
Abstract reading summary — source excerpts
- Opening excerpt
- Reward processes have traditionally been ascribed to dopamine (DA)-associated circuits.
- Closing excerpt
- By uncovering the influence of gut-brain vagal communication on DA functions, this work provides insights into the neurobiology of adaptive and maladaptive reward, with broad relevance for eating disorders and addiction.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (158 words)
Reward processes have traditionally been ascribed to dopamine (DA)-associated circuits. While external stimuli, such as food and drugs of abuse, are activators of DA-neuron activity, growing evidence indicates that interoceptive signals also play a critical role. Among these, the gut-brain vagal axis has emerged as a key regulator, although its precise contribution to mesolimbic DA signaling and behavior remains unclear. Here, we combine complementary ex vivo and in vivo approaches across multiple scales to show that gut-brain vagal tone is essential for gating mesolimbic DA system activity and functions, modulating DA-dependent molecular and cellular processes, and scaling both food- and drug-induced reinforcement. These findings challenge the traditional brain-centric view of reward processing, supporting a more integrated model in which vagus-mediated interoceptive signals intrinsically shape motivation and reinforcement. By uncovering the influence of gut-brain vagal communication on DA functions, this work provides insights into the neurobiology of adaptive and maladaptive reward, with broad relevance for eating disorders and addiction.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 41616060 · DOI · PMC12857734
A computational approach to understanding effort-based decision-making in depression [3]
Abstract reading summary — source excerpts
- Opening excerpt
- BackgroundMotivational dysfunction is a core feature of depression and can have debilitating effects on everyday function.
- Closing excerpt
- Lower willingness to exert effort could represent a trait-like factor contributing to symptoms and a fruitful target for treatment and prevention.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (241 words)
BackgroundMotivational dysfunction is a core feature of depression and can have debilitating effects on everyday function. However, it is unclear which cognitive processes underlie impaired motivation and whether impairments persist following remission. Decision-making concerning exerting effort to obtain rewards offers a promising framework for understanding motivation, especially when examined with computational tools.MethodsEffort-based decision-making was assessed using the Apple Gathering Task, where participants decide whether to exert effort via a grip-force device to obtain varying levels of reward; effort levels were individually calibrated and varied parametrically. We present a comprehensive computational analysis of decision-making, initially validating our model in healthy volunteers (N = 67), before applying it in a case-control study including current (N = 41) and remitted (N = 46) unmedicated depressed individuals and healthy volunteers with (N = 36) and without (N = 57) a family history of depression.ResultsFour fundamental computational mechanisms that drive patterns of effort-based decisions, which replicated across samples, were identified: overall bias to accept effort challenges; reward sensitivity; and linear and quadratic effort sensitivity. Traditional model-agnostic analyses showed that both depressed groups showed lower willingness to exert effort. In contrast with previous findings, computational analysis revealed that this difference was primarily driven by lower effort-acceptance bias, but not altered effort or reward sensitivity.ConclusionsThis work provides insight into the computational mechanisms underlying motivational dysfunction in depression. Lower willingness to exert effort could represent a trait-like factor contributing to symptoms and a fruitful target for treatment and prevention.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 41059633 · DOI · PMC12527505
Key reviews and meta-analyses
The most cited review-type papers among PubMed’s best-match results for this heading, listed with bibliographic details only. Follow the links for the full text and access terms.
- Research article Common and distinct networks underlying reward valence and processing stages: a meta-analysis of functional neuroimaging studies [4]
- Research article Delayed reward discounting and addictive behavior: a meta-analysis [5]
- Research article The Neuroscience of Drug Reward and Addiction [6]
- Research article A meta-analysis of voucher-based reinforcement therapy for substance use disorders [7]
- Research article Striatal circuits for reward learning and decision-making [8]
- Research article Reward Processing in Depression: A Conceptual and Meta-Analytic Review Across fMRI and EEG Studies [9]
References
- Yaakub SN, Eraifej J, Bault N, Lojkiewiez M, Bellec E, Roberts J, et al.. Non-invasive ultrasonic neuromodulation of the human nucleus accumbens impacts reward sensitivity. . 2025. PMID 41309575 · DOI · PMC12660320
- Onimus O, Arrivet F, Le Borgne T, Perez S, Castel J, Ansoult A, et al.. The gut-brain vagal axis governs mesolimbic dopamine dynamics and reward events. . 2026. PMID 41616060 · DOI · PMC12857734
- Valton V, Mkrtchian A, Moses-Payne M, Gray A, Kieslich K, VanUrk S, et al.. A computational approach to understanding effort-based decision-making in depression. . 2025. PMID 41059633 · DOI · PMC12527505
- Liu X, Hairston J, Schrier M, Fan J. Common and distinct networks underlying reward valence and processing stages: a meta-analysis of functional neuroimaging studies. Neurosci Biobehav Rev. 2011. PMID 21185861 · DOI · PMC3395003
- MacKillop J, Amlung MT, Few LR, Ray LA, Sweet LH, Munafò MR. Delayed reward discounting and addictive behavior: a meta-analysis. Psychopharmacology (Berl). 2011. PMID 21373791 · DOI · PMC3201846
- Volkow ND, Michaelides M, Baler R. The Neuroscience of Drug Reward and Addiction. Physiol Rev. 2019. PMID 31507244 · DOI · PMC6890985
- Lussier JP, Heil SH, Mongeon JA, Badger GJ, Higgins ST. A meta-analysis of voucher-based reinforcement therapy for substance use disorders. Addiction. 2006. PMID 16445548 · DOI
- Cox J, Witten IB. Striatal circuits for reward learning and decision-making. Nat Rev Neurosci. 2019. PMID 31171839 · DOI · PMC7231228
- Keren H, O'Callaghan G, Vidal-Ribas P, Buzzell GA, Brotman MA, Leibenluft E, et al.. Reward Processing in Depression: A Conceptual and Meta-Analytic Review Across fMRI and EEG Studies. Am J Psychiatry. 2018. PMID 29921146 · DOI · PMC6345602
About this article
- Sources
- NLM Medical Subject Headings (definition, synonyms); NCBI PubMed (counts, identifiers); Europe PMC (open-access abstracts and citation counts).
- Verification
- Every reference was resolved against PubMed on 2026-11-04; title, year and DOI matched.
- Review state
- automated source verified; no human expert review has been recorded yet.
- Version
- 1, last updated 2026-11-04.
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Evidence strength
EvidenceHelix's computed view of the study designs behind this topic: how common meta-analyses, randomized trials and reviews are, how recent the literature is, and a plain-language reading. It describes the design mix of the literature. It is not a quality appraisal of any study, and it is not medical advice.