Skip to main content
NCBI home page
JAMA Network logoLink to JAMA Network
. 2025 Jun 11;82(8):818–829. doi: 10.1001/jamapsychiatry.2025.0839

Differential Associations of Dopamine and Serotonin With Reward and Punishment Processes in Humans

A Systematic Review and Meta-Analysis

Anahit Mkrtchian 1,✉, Zeguo Qiu 1, Yaniv Abir 1, Tore Erdmann 1, Quentin Dercon 1, Terezie Sedlinska 2, Michael Browning 3,4, Harry Costello 5,6, Quentin J M Huys 1
PMCID: PMC12159863  EMSID: EMS206494  PMID: 40498519

Key Points

Question

Do pharmacological manipulations of dopamine and serotonin affect components of reinforcement learning in humans?

Findings

In this systematic review and meta-analysis including data from 102 studies, upregulating dopamine was associated with increased reward learning or sensitivity and reward response vigor and decreased reward discounting. Upregulation of serotonin was associated with increased punishment learning or sensitivity and decreased reward discounting.

Meaning

Pharmacological manipulations of dopamine and serotonin had dissociable associations with different components of reinforcement learning; this forms a basis for the development of selective markers for treatment assignment.

This systematic review and meta-analysis compares the association of dopamine and serotonin in reinforcement learning.

Abstract

Importance

Mechanistic biomarkers for guiding treatment selection require selective sensitivity to specific pharmacological interventions. Reinforcement learning processes show potential, but there have been conflicting and sometimes inconsistent reports on how dopamine and serotonin—2 key targets in treating common mental illnesses—affect reinforcement learning in humans.

Objective

To perform a meta-analysis of pharmacological manipulations of dopamine and serotonin and examine whether they show distinct associations with reinforcement learning components in humans.

Data Sources

Ovid MEDLINE/PubMed, Embase, and PsycInfo databases were searched for studies published between January 1, 1946, and January 19, 2023 (repeated April 9, 2024, and October 15, 2024), investigating dopaminergic or serotonergic effects on reward and punishment processes in humans according to PRISMA guidelines.

Study Selection

Studies reporting randomized, placebo-controlled, dopaminergic or serotonergic manipulations on a behavioral outcome from a reward or punishment processing task in healthy humans were included.

Data Extraction and Synthesis

Standardized mean difference (SMD) scores were calculated for the comparison between each drug (dopamine or serotonin) and placebo on a behavioral reward or punishment outcome and quantified in random-effects models for overall reward or punishment processes and 4 main subcategories. Study quality (Cochrane Collaboration tool), moderators, heterogeneity, and publication bias were also assessed.

Main Outcomes and Measures

Performance on reward or punishment processing tasks.

Results

In total, 102 studies conducted among healthy volunteers were included (2291 participants receiving dopamine vs 2284 receiving placebo and 1491 receiving serotonin vs 1523 receiving placebo). Dopamine was associated with an increase in overall reward (SMD, 0.18; 95% CI, 0.09 to 0.28) but not punishment function (SMD, −0.06; 95% CI, −0.26 to 0.13). Serotonin was not meaningfully associated with overall punishment (SMD, 0.22; 95% CI, −0.04 to 0.49) or reward (SMD, 0.02; 95% CI, −0.33 to 0.36). Dopaminergic and serotonergic manipulations had distinct associations with subcomponents. Dopamine was associated with reward learning or sensitivity (SMD, 0.26; 95% CI, 0.11 to 0.40), reward discounting (SMD, −0.08; 95% CI, −0.14 to −0.01), and reward vigor (SMD, 0.32; 95% CI, 0.11 to 0.54). By contrast, serotonin was associated with punishment learning or sensitivity (SMD, 0.32; 95% CI, 0.05 to 0.59), reward discounting (SMD, −0.35; 95% CI, −0.67 to −0.02), and aversive pavlovian processes (within-participant studies only; SMD, 0.36; 95% CI, 0.20 to 0.53).

Conclusions and Relevance

In this study, pharmacological manipulations of both dopamine and serotonin had measurable associations with reinforcement learning in humans. The selective associations with different components suggest that reinforcement learning tasks could form the basis of selective, mechanistically interpretable biomarkers to support treatment assignment.

Introduction

Dopamine and serotonin are 2 critical neuromodulators in the brain, both playing central roles in clinical and basic neuroscience. Clinically, they are the targets of different antidepressants, which are equally effective in treating mood disorders.1 Yet, basic neuroscience suggests they serve distinct functions in behavior, particularly in reinforcement learning (RL). If dopamine and serotonin indeed have distinct effects in RL, as current theories predict, this could help identify new mechanisms to guide much-needed optimized treatments. Conversely, if they prove indistinguishable in humans, prevailing neuroscientific models would require substantial revision. Resolving this question in humans is therefore critical.

RL describes how rewards and losses guide behavior and is strongly related to depression. It is impaired in individuals who are currently experiencing depression2,3 and has substantial face validity as a core mechanism leading to or maintaining depression.4 RL is also intricately linked to the neuromodulator systems dopamine and serotonin as the brain appears to use these to broadcast key RL signals.5,6

However, a key challenge here lies in reconciling 2 seemingly contradictory bodies of research. On the one hand, antidepressants targeting dopamine or serotonin appear similarly effective in treating depression.1 On the other hand, substantial neuroscience research indicates that dopamine and serotonin play distinct roles in RL.5,6,7,8 One possible resolution is that different antidepressants engage different components of RL. Such a selectivity could potentially explain why individuals who do not respond to serotonergic medications may respond to more dopaminergic medications. Indeed, dopaminergic antidepressants are second-line options when first-line serotonergic treatments fail.9,10 As a necessary precondition for this, manipulating different neuromodulators must demonstrably affect distinct RL processes in humans—a question that remains unresolved. If validated, RL-based measures could serve as markers to guide treatment choices and inform the development of novel therapies. Thus, adopting an RL framework to clarify the cognitive roles of dopamine and serotonin represents a critical first step toward identifying candidate mechanisms driving the therapeutic effects of these antidepressants.

However, RL is not a single process but a collection of mechanisms that are hypothesized to involve dopamine and serotonin in distinct ways.11 Basic neuroscience highlights that dopamine is a key modulator of appetitive processes, particularly reward learning, reward discounting, and reward response vigor.6,11,12,13 Serotonin, while more complex, is often linked to aversive processes, such as punishment learning or inhibition in the face of punishments.8,14 However, human studies on these neuromodulators in RL have yielded mixed and sometimes conflicting results.15,16 These inconsistencies hinder our understanding of dopamine and serotonin in human behavior and the development of optimized treatments for mood and anxiety disorders. Therefore, we performed a meta-analysis of the effects of pharmacological manipulations of dopamine and serotonin on RL. Given the limited number of studies, for increased power we (1) considered pharmacological manipulations broadly, not only antidepressants, and (2) focused on healthy participants due to few patient studies. Our first aim was to ask whether dopamine and serotonin alter overall reward and punishment processing. Our second aim was to identify whether they affect specific RL subcomponents. Based on theoretical frameworks and a systematic review of the literature, we identified and focused the meta-analysis on the following subcomponents of RL.

Reward and Punishment Learning

A key component of RL involves trial-and-error learning from rewards and punishments via prediction errors (the difference between expected and actual outcomes).6 Dopamine has been extensively implicated in signaling reward prediction errors.6,17 Some theories also postulate that dopamine dips may drive punishment learning,18 while computational models suggest this is under serotonin’s control.14,19 However, recent animal studies have implicated serotonin in reward learning as well, firing to reward-predicting cues or, contrarily, suppressing reward learning.5,20 To examine these processes in humans, studies mostly use probabilistic instrumental learning tasks.

Pavlovian Bias

Choices can be driven by reflexive pavlovian biases, where appetitive biases prompt approach toward rewards and aversive biases trigger inhibition in response to punishment, even when suboptimal.21 Influential models propose that serotonin promotes aversive pavlovian biases—that is, inhibition in aversive contexts.8,22,23,24 Human studies often use go/no-go tasks where these biases interfere with optimal actions or pavlovian-instrumental transfer tasks.21,25

Reward Discounting

Decision-making also requires balancing rewards against costs. Current theories predict that dopamine signals benefits over costs, while serotonin may promote willingness to wait or exert effort for larger rewards by modulating perceived costs. Both mechanisms thus contribute to reduced reward discounting.26,27,28,29,30 These processes are studied using tasks that contrast explicit choices between immediate or low-effort small rewards with delayed or high-effort larger rewards.

Reward Response Vigor

Motivation involves deciding how vigorously actions need to be executed to obtain rewards. Dopamine is theorized to signal average reward rates, enhancing response vigor.31,32 The role of serotonin is less defined, although an early computational theory suggests it may also modulate the long-term reward rates.14 Tasks in this domain assess reaction times or effort exertion relative to reward magnitude or average reward rate.

Overall, this meta-analysis aims to provide a clearer understanding of how dopamine and serotonin shape RL in humans. Such insights may contribute to both theoretical and clinical advances.

Methods

Systematic Review

We conducted a preregistered systematic review and meta-analysis (PROSPERO: CRD42022363747) according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline33 (eAppendix in Supplement 1). The Ovid MEDLINE/PubMed, Embase, and PsycInfo databases were searched for articles published between January 1, 1946, and January 21, 2023. The search was repeated on April 9, 2024, and October 15, 2024, for additional articles published since 2023. Titles or abstracts were searched containing the terms (dopamine or seroton or tryptophan or tyrosine) and (reward or motivat or punish or reinforce or decision or effort or learn or incentive or volition or choice) and (participa or subject or individ or human or healthy or investigat or experiment) and restricted to articles in English and humans. In addition to database searches, 1 study was identified through a relevant review article, and 1 was suggested by an expert in the field. Detailed description of the search strategy is described in eTable 1 in the eMethods in Supplement 1.

We included studies reporting interventions manipulating either dopamine or serotonin in healthy human volunteers aged 18 to 65 years in randomized placebo-controlled studies that reported a behavioral outcome from a reward and/or punishment processing task. We only included samples that did not overlap with other included datasets and contained sufficient information to calculate standardized mean difference (SMD) scores between placebo and drug conditions (eMethods in Supplement 1).

Meta-Analysis

Effect sizes were calculated for behavioral outcomes relating to reward and punishment learning or sensitivity, aversive and appetitive pavlovian bias, reward vigor, and reward discounting. To evaluate overall reward and punishment processes, we consolidated the components into 2 main categories. The overall reward category combined reward learning or sensitivity, appetitive pavlovian bias, reward vigor, and reward discounting—reversing the discounting direction so that a positive SMD represents increased reward weighting. The overall punishment function combined punishment learning or sensitivity and aversive pavlovian bias. We identified risk attitude and model-based learning or flexibility as 2 additional RL subcomponents. However, since these cannot be straightforwardly combined with the other subcomponents, we considered them separately (eMethods in Supplement 1).

Pharmacologically, we pooled data from both agonists and antagonists, assuming symmetrical effects for drugs that upregulate or downregulate a neuromodulator. We recoded antagonist effects as if they were agonist effects. For example, if a study reported that a dopamine antagonist reduced reward vigor, we recoded this effect to represent an agonist increasing reward vigor. Some dopamine studies used single low doses of agonists or antagonists intended to specifically target presynaptic autoreceptors. These effects were interpreted contrary to their typical activity profile (eg, a low-dose agonist was coded as an antagonist), as suggested by the literature (eMethods in Supplement 1). Thus, for a low-dose dopamine agonist that, for example, reduced reward learning, we first classified it as an antagonist reducing reward learning and then recoded it as an agonist increasing reward learning, maintaining a consistent directionality across studies. To assess the impact of this interpretation, we conducted sensitivity analyses using the original drug activity profiles as well.

Both within- and between-participant designs were included. The detailed procedures for calculating between- and within-participant SMDs, including the conversion procedures, are described in the eMethods in Supplement 1, with comparisons of different effect size conversions for within-participant studies depicted in eFigure 1 in the eMethods in Supplement 1. Random-effects models were conducted in R version 4.3.3 (R Foundation) using metafor34 packages to examine the association between upregulating dopamine and serotonin with placebo on overall reward and punishment processes and subcomponents. Heterogeneity, publication bias, moderators, and study quality were also assessed (eMethods in Supplement 1).

Results

Data from 102 studies were included (2291 participants receiving dopamine vs 2284 receiving placebo and 1491 receiving serotonin vs 1523 receiving placebo) (Figure 1; eTable 2 in the eResults in Supplement 1). Risk of bias was generally high for both randomization and allocation concealment, but low for blinding of outcome assessment (eFigure 2 in the eResults in Supplement 1).

Figure 1. Flow Diagram of Study Selection and Inclusion.

Figure 1.

Open in a new tab

5HT indicates serotonin; DA, dopamine.

aThe full text of 313 studies was assessed for eligibility as 9 studies reported data both for dopamine and serotonin conditions.

bFive studies reported data both for dopamine and serotonin.

Meta-Analysis Results

Dopamine

Overall, dopamine had a small positive association with reward processes (SMD, 0.18; 95% CI, 0.09 to 0.28) but a negligible negative association with overall punishment processes (SMD, −0.06; 95% CI, −0.26 to 0.13) (eFigure 3 in the eResults in Supplement 1). Analyses according to the original drug activity profile changed inference for the overall reward process (eFigure 4 in the eResults in Supplement 1).

Dopamine was associated with an increase in reward learning or sensitivity (SMD, 0.26; 95% CI, 0.11 to 0.40), reward vigor (SMD, 0.32; 95% CI, 0.11 to 0.54), and decreased reward discounting (SMD, −0.08; 95% CI, −0.14 to −0.01) (Figure 235,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80), but was not associated with any other subcomponents (Figure 336,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,81,82,83,84,85; eFigure 5 in the eResults in Supplement 1). Analyses according to the original drug activity profile influenced the inference of dopamine on reward learning or sensitivity and reward discounting but not other subcomponents (eFigure 6 in the eResults in Supplement 1). We also explored the associations with computational learning rate and sensitivity parameters, but there were far fewer studies reporting these and no clear pattern emerged (eFigure 7 in the eResults in Supplement 1).

Figure 2. Association of Dopamine Upregulation With Reward Subcomponents.

Figure 2.

Open in a new tab

Standardized mean differences (SMDs) of the association between upregulating dopamine vs placebo and reward learning or sensitivity (A), reward discounting (B), and reward response vigor (C). We recoded antagonist effects as if they were agonists (gray SMD point estimates and 95% CIs). Blue SMD point estimates and 95% CIs indicate studies that were coded as original agonists. Phe/tyr indicates phenylalanine/tyrosine.

aParticipants are shown as No. receiving study drug, No. receiving placebo. Where only 1 number is presented, this reflects a within-participant design.

bStudies that used a low dose of an agonist or antagonists. These effects were interpreted contrary to their typical activity profile (eg, a low-dose agonist acting antagonistically), as suggested by the literature.

Figure 3. Association of Dopamine Upregulation With Punishment Subcomponents.

Figure 3.

Open in a new tab

Standardized mean differences (SMDs) of the association between upregulating dopamine vs placebo and punishment learning or sensitivity (A) and aversive pavlovian bias (B). We recoded antagonist effects as if they were agonists (gray SMD point estimates and 95% CIs). Blue SMD point estimates and 95% CIs indicate studies that were coded as original agonists. Phe/tyr indicates phenylalanine/tyrosine.

aParticipants are shown as No. receiving study drug, No. receiving placebo. Where only 1 number is presented, this reflects a within-participant design.

bStudies that used a low dose of an agonist or antagonists. These effects were interpreted contrary to their typical activity profile (eg, a low-dose agonist acting antagonistically), as suggested by the literature.

There was low to moderate heterogeneity across all overall processes and main subcomponents (eResults in Supplement 1). Substantial heterogeneity was only found in the risk attitude and model-based or flexibility subcomponents. Outlier exclusion resolved risk attitude heterogeneity, resulting in a significant positive dopamine association (eFigure 8 in the eResults in Supplement 1).

Serotonin

Overall, serotonin did not show associations with reward (SMD, 0.02; 95% CI, −0.33 to 0.36) or punishment processes (SMD, 0.22; 95% CI, −0.04 to 0.49) (eFigure 9 in the eResults in Supplement 1). Serotonin had a small to moderate association with reward discounting (SMD, −0.35; 95% CI, −0.67 to −0.02) (Figure 425,75,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102) and punishment learning or sensitivity (SMD, 0.32; 95% CI, 0.05 to 0.59) (Figure 525,82,83,85,86,87,89,91,92,93,94) but not with reward learning or sensitivity, reward vigor, pavlovian biases (Figures 4 and 5), or other subcomponents (eFigure 10 in the eResults in Supplement 1).

Figure 4. Association of Serotonin Upregulation With Reward Subcomponents.

Figure 4.

Open in a new tab

Standardized mean differences (SMDs) of the association between upregulating serotonin vs placebo and reward learning or sensitivity (A), appetitive pavlovian bias (B), reward discounting (C), and reward response vigor (D). We recoded antagonist effects as if they were agonists (gray SMD point estimates and 95% CIs). Blue SMD point estimates and 95% CIs indicate studies that were coded as original agonists. TD indicates tryptophan depletion.

aParticipants are shown as No. receiving study drug, No. receiving placebo. Where only 1 number is presented, this reflects a within-participant design.

Figure 5. Association of Serotonin Upregulation With Punishment Subcomponents.

Figure 5.

Open in a new tab

Standardized mean differences (SMDs) of the association between upregulating serotonin vs placebo and punishment learning or sensitivity (A) and aversive pavlovian bias (B). We recoded antagonist effects as if they were agonists (gray SMD point estimates and 95% CIs). Blue SMD point estimates and 95% CIs indicate studies that were coded as original agonists. TD indicates tryptophan depletion.

aParticipants are shown as No. receiving study drug, No. receiving placebo. Where only 1 number is presented, this reflects a within-participant design.

However, 3 within-participant studies had to be excluded in the aversive pavlovian domain due to missing information needed to combine with between-participant studies. Examining the 6 within-participant studies separately, serotonin was associated with a small to medium increase in aversive pavlovian processes (SMD, 0.36; 95% CI, 0.20 to 0.53) ( eFigure 11 in the eResults in Supplement 1). Exploring learning rate and sensitivity computational parameters, serotonin was associated with increased punishment learning rates and decreased punishment sensitivity (eFigure 12 in the eResults in Supplement 1). However, this finding is based on a limited number of studies.

There was substantial heterogeneity in the overall reward and punishment categories and the reward learning or sensitivity and aversive pavlovian subcomponents. Removing identified outliers explained some of this variance and revealed a significant increase of overall punishment processes by serotonin (SMD, 0.32; 95% CI, 0.10 to 0.54) (eFigure 13 in the eResults in Supplement 1).

Publication Bias and Moderator Analyses

The Egger test suggested significant publication bias in the overall dopamine reward domain (z = 2.28, P = .02) but not in any of the other overall domains (dopamine punishment: z = −0.79, P = .43; serotonin reward: z = 0.07, P = .94; serotonin punishment: z = −0.09, P = .93) (eFigure 14 in the eResults in Supplement 1).

Dopamine

Only the reward and punishment learning or sensitivity and reward discounting categories contained greater than 9 studies, allowing a test for funnel plot asymmetry.103 None of these showed significant publication bias (reward learning or sensitivity: z = 1.49, P = .14; punishment learning or sensitivity: z = −1.42, P = .15; reward discounting: z = 0.06, P = .95) (eFigure 15 in the eResults in Supplement 1).

Drug type significantly moderated the reward discounting results (R2 = 13.88%; I2 = 6.45%; P = .03), with original agonist studies decreasing reward discounting (SMD ,−0.10; 95% CI, −0.16 to −0.03; 10 studies), while studies that originally used antagonists showed no meaningful association (SMD, 0.14; 95% CI, −0.06 to 0.35; 2 studies). None of the other moderators had an effect (eResults in Supplement 1).

Serotonin

The model-based learning or flexibility subcomponent was the only one with sufficient studies to conduct a funnel plot asymmetry analysis, which revealed no significant asymmetry (z = 1.45, P = .15) (eFigure 15 in the eResults in Supplement 1). No meaningful moderators were identified for serotonin (eResults in Supplement 1).

Discussion

This systematic review and meta-analysis found distinct roles for dopamine and serotonin in modulating RL processes in humans. Dopamine was associated with an increase in reward learning and sensitivity as well as reward vigor. A different pattern of effects was observed for serotonin, which was found to promote learning from punishments and possibly amplify aversive pavlovian biases. Both serotonin and dopamine increased the value of rewards when contrasted with the costs of time or effort. These findings contribute to 2 key domains: advancing neuroscience theories of dopamine and serotonin in humans, and informing clinical applications. We begin by addressing the theoretical implications and conclude with the clinical relevance.

The differing associations of dopamine and serotonin are broadly consistent with theoretical expectations. The most influential theory of dopamine posits that dopamine signals reward prediction errors, driving reward learning.6,104 Supporting this, we found that boosting dopamine promoted reward learning or sensitivity in humans. Unlike preclinical studies, however,5,20 we found no serotonin association with reward learning, reinforcing the idea that dopamine and serotonin serve dissociable functions here. Instead, serotonin was selectively associated with punishment learning, consistent with theories and human research linking serotonin to aversive processing8,105 and highlighting an opposing role to dopamine in learning.14

We also provide preliminary evidence for a dissociation of serotonin and dopamine in pavlovian biases. Specifically, serotonin—but not dopamine—was associated with enhanced aversive biases in within-participant studies. This resonates with findings in patients with depression being treated with serotonergic antidepressants106 and supports theories suggesting serotonin facilitates inhibition specifically in punishment contexts.8,22,23,24 However, this effect was not consistently observed, potentially owing to high measurement noise in these tasks,107,108 underscoring the need for psychometrically optimized tasks.109

Beyond learning, it is noteworthy that we found dopamine influencing reward vigor and discounting. This aligns with animal work suggesting that dopamine encodes a unified motivational signal, influencing both how energetically actions are taken (vigor) and decisions of whether to invest in effortful activity (value-based choice).32,110 At present, it is unclear whether the dopamine associations with vigor and discounting (especially effort-based discounting, which supplementary exploratory analyses indicated might be particularly associated with dopamine) are related in humans as well. Notably, however, our findings converge with a rodent and clinical meta-analysis, where dopaminergic agents affected both reward discounting and vigor.32,110,111,112

Like dopamine, serotonin also diminished reward discounting. Although this might suggest that both have similar functions here, a more likely explanation is that they affect reward discounting through distinct mechanisms. Prior research suggests that dopamine reduces discounting by boosting the perceived benefits while serotonin may alter the perceived costs.27,28,35 Although we were unable to dissociate the precise mechanism, it is interesting that the serotonin studies exclusively covered delay discounting tasks, while dopamine additionally covered effort discounting tasks. This may suggest that serotonin increases the discounting factor in RL models to promote a focus on long-term over short-term outcomes.27,113

Clinically, perhaps the most important finding is that dopaminergic and serotonergic pharmacological manipulations had appreciably distinct associations. They engaged different components of RL in a manner that is measurable and identifiable with simple behavioral tasks. Hence, simple RL tasks may play a useful role in measuring treatment target engagement. This contrasts with symptom markers, which do not differentiate psychiatric medications.114,115 Interestingly, our observed effect sizes, though modest, align with RL impairments observed in mood disorders,2,3 dopamine effects in Parkinson disease,112 and antidepressant efficacy.1 Our results thus fulfill a key precondition for future work: if different antidepressants modulate distinct RL components, then RL-based biomarkers could guide more optimized treatments. This aligns with calls to use RL to understand antidepressant mechanisms and their clinical relevance.116 While individual-level precision requires further refinement, establishing even subtle systematic group-level neuromodulatory RL associations is a crucial first step in guiding those efforts. A key and important question for future work is whether individual differences in baseline RL measures could help identify individuals more likely to benefit from one or the other type of intervention. This will require studies that simultaneously measure multiple RL domains, psychometrically robust tasks and direct head-to-head comparison of the treatments of interest.

Limitations

Several limitations warrant consideration. First, few studies reported computational RL outcomes, forcing us to rely on broad measures. While we attempted to examine learning rate and sensitivity separately, this proved challenging, as few reported these. It was further difficult to disentangle them (indeed, serotonin was associated with increased punishment learning but decreased punishment sensitivity; eFigure 12 in the eResults in Supplement 1), and both are sensitive to task designs.117 Despite this, meaningful variability was evident in broad measures.

Second, the lack of studies directly comparing dopamine and serotonin prevented us from examining this association. However, such comparisons will be crucial for drawing definitive conclusions about their differential effects. There is also a lack of pharmacological specificity in the dopamine domain. Some medications target both dopamine and noradrenaline, complicating interpretation.

Third, the association between dopamine and reward learning and discounting depended on the drug-binding interpretation, shifting from negligible to meaningful when low-dose presynaptic accounts were considered. This highlights the complexities of dopaminergic research, including evidence that the direction of effects may depend on baseline dopamine levels.118 Such complexities might therefore have added noise to the effect size estimates.

Fourth, substantial heterogeneity was observed in the association between serotonin and reward learning, potentially due to the lack of common tasks across studies. This suggests the meta-analytic effect may be fragile, underscoring the need for improved task standardization and psychometrically robust tasks. Several other factors may have contributed to heterogeneity, such as feedback saliency119 or menstrual cycle,120 which future research needs to assess.

Fifth, most serotonin studies used tryptophan depletion, a method debated as a mild serotonin manipulation.121 However, our findings show meaningful associations even in tryptophan depletion–only analyses (reward discounting, aversive pavlovian processes), supporting their validity despite this debate.

We also pooled within- and between-participant data to increase power, applying effect size corrections of within-participant studies where feasible. When not possible, inflated variance of effect sizes would be the likely consequence, reducing our power to detect significant effects.

Sixth, all studies were done in healthy control individuals. Ultimately, head-to-head comparisons in patients across tasks will be required to establish the utility of RL processes for clinical applications.

Conclusions

Despite methodological heterogeneity, there is evidence that dopamine and serotonin affect RL and that they do so by altering specific, distinct processes. These insights are valuable as they set forth candidate mechanisms for understanding the differentiating effects of treatments targeting different neuromodulators. The selective associations are observable in highly scalable human behavioral tasks, suggesting that such assessments may provide fertile ground for the development of differentially sensitive biomarkers.

Supplement 1.

eMethods

eResults

eReferences

eAppendix

jamapsychiatry-e250839-s001.pdf (1MB, pdf)
Supplement 2.

Data sharing statement

jamapsychiatry-e250839-s002.pdf (16.3KB, pdf)

References

  • 1.Cipriani A, Furukawa TA, Salanti G, et al. Comparative efficacy and acceptability of 21 antidepressant drugs for the acute treatment of adults with major depressive disorder: a systematic review and network meta-analysis. Lancet. 2018;391(10128):1357-1366. doi: 10.1016/S0140-6736(17)32802-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Halahakoon DC, Kieslich K, O’Driscoll C, Nair A, Lewis G, Roiser JP. Reward-processing behavior in depressed participants relative to healthy volunteers: a systematic review and meta-analysis. JAMA Psychiatry. 2020;77(12):1286-1295. doi: 10.1001/jamapsychiatry.2020.2139 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Pike AC, Robinson OJ. Reinforcement learning in patients with mood and anxiety disorders vs control individuals: a systematic review and meta-analysis. JAMA Psychiatry. 2022;79(4):313-322. doi: 10.1001/jamapsychiatry.2022.0051 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Huys QJ, Daw ND, Dayan P. Depression: a decision-theoretic analysis. Annu Rev Neurosci. 2015;38:1-23. doi: 10.1146/annurev-neuro-071714-033928 [DOI] [PubMed] [Google Scholar]
  • 5.Cardozo Pinto DF, Pomrenze MB, Guo MY, et al. Opponent control of reinforcement by striatal dopamine and serotonin. Nature. 2024;639(8053):143-152. doi: 10.1038/s41586-024-08412-x [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Schultz W, Dayan P, Montague PR. A neural substrate of prediction and reward. Science. 1997;275(5306):1593-1599. doi: 10.1126/science.275.5306.1593 [DOI] [PubMed] [Google Scholar]
  • 7.Maier SF, Watkins LR. Stressor controllability and learned helplessness: the roles of the dorsal raphe nucleus, serotonin, and corticotropin-releasing factor. Neurosci Biobehav Rev. 2005;29(4-5):829-841. doi: 10.1016/j.neubiorev.2005.03.021 [DOI] [PubMed] [Google Scholar]
  • 8.Dayan P, Huys QJ. Serotonin in affective control. Annu Rev Neurosci. 2009;32:95-126. doi: 10.1146/annurev.neuro.051508.135607 [DOI] [PubMed] [Google Scholar]
  • 9.Rush AJ, Trivedi MH, Wisniewski SR, et al. Acute and longer-term outcomes in depressed outpatients requiring one or several treatment steps: a STAR*D report. Am J Psychiatry. 2006;163(11):1905-1917. doi: 10.1176/ajp.2006.163.11.1905 [DOI] [PubMed] [Google Scholar]
  • 10.Webb CA, Trivedi MH, Cohen ZD, et al. Personalized prediction of antidepressant v. placebo response: evidence from the EMBARC study. Psychol Med. 2019;49(7):1118-1127. doi: 10.1017/S0033291718001708 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Husain M, Roiser JP. Neuroscience of apathy and anhedonia: a transdiagnostic approach. Nat Rev Neurosci. 2018;19(8):470-484. doi: 10.1038/s41583-018-0029-9 [DOI] [PubMed] [Google Scholar]
  • 12.Flagel SB, Clark JJ, Robinson TE, et al. A selective role for dopamine in stimulus-reward learning. Nature. 2011;469(7328):53-57. doi: 10.1038/nature09588 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Steinberg EE, Keiflin R, Boivin JR, Witten IB, Deisseroth K, Janak PH. A causal link between prediction errors, dopamine neurons and learning. Nat Neurosci. 2013;16(7):966-973. doi: 10.1038/nn.3413 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Daw ND, Kakade S, Dayan P. Opponent interactions between serotonin and dopamine. Neural Netw. 2002;15(4-6):603-616. doi: 10.1016/S0893-6080(02)00052-7 [DOI] [PubMed] [Google Scholar]
  • 15.Martins D, Mehta MA, Prata D. The “highs and lows” of the human brain on dopaminergics: Evidence from neuropharmacology. Neurosci Biobehav Rev. 2017;80:351-371. doi: 10.1016/j.neubiorev.2017.06.003 [DOI] [PubMed] [Google Scholar]
  • 16.Rogers RD. The roles of dopamine and serotonin in decision making: evidence from pharmacological experiments in humans. Neuropsychopharmacology. 2011;36(1):114-132. doi: 10.1038/npp.2010.165 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Montague PR, Dayan P, Sejnowski TJ. A framework for mesencephalic dopamine systems based on predictive Hebbian learning. J Neurosci. 1996;16(5):1936-1947. doi: 10.1523/JNEUROSCI.16-05-01936.1996 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Collins AG, Frank MJ. Opponent actor learning (OpAL): modeling interactive effects of striatal dopamine on reinforcement learning and choice incentive. Psychol Rev. 2014;121(3):337-366. doi: 10.1037/a0037015 [DOI] [PubMed] [Google Scholar]
  • 19.Deakin JF, Graeff FG. 5-HT and mechanisms of defence. J Psychopharmacol. 1991;5(4):305-315. doi: 10.1177/026988119100500414 [DOI] [PubMed] [Google Scholar]
  • 20.Cohen JY, Amoroso MW, Uchida N. Serotonergic neurons signal reward and punishment on multiple timescales. Elife. 2015;4:e06346. doi: 10.7554/eLife.06346 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Guitart-Masip M, Duzel E, Dolan R, Dayan P. Action versus valence in decision making. Trends Cogn Sci. 2014;18(4):194-202. doi: 10.1016/j.tics.2014.01.003 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Boureau YL, Dayan P. Opponency revisited: competition and cooperation between dopamine and serotonin. Neuropsychopharmacology. 2011;36(1):74-97. doi: 10.1038/npp.2010.151 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Cools R, Nakamura K, Daw ND. Serotonin and dopamine: unifying affective, activational, and decision functions. Neuropsychopharmacology. 2011;36(1):98-113. doi: 10.1038/npp.2010.121 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Dayan P, Huys QJ. Serotonin, inhibition, and negative mood. PLoS Comput Biol. 2008;4(2):e4. doi: 10.1371/journal.pcbi.0040004 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Geurts DE, Huys QJ, den Ouden HE, Cools R. Serotonin and aversive pavlovian control of instrumental behavior in humans. J Neurosci. 2013;33(48):18932-18939. doi: 10.1523/JNEUROSCI.2749-13.2013 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Dalley JW, Roiser JP. Dopamine, serotonin and impulsivity. Neuroscience. 2012;215:42-58. doi: 10.1016/j.neuroscience.2012.03.065 [DOI] [PubMed] [Google Scholar]
  • 27.Doya K. Metalearning and neuromodulation. Neural Netw. 2002;15(4-6):495-506. doi: 10.1016/S0893-6080(02)00044-8 [DOI] [PubMed] [Google Scholar]
  • 28.Meyniel F, Goodwin GM, Deakin JW, et al. A specific role for serotonin in overcoming effort cost. Elife. 2016;5:e17282. doi: 10.7554/eLife.17282 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Salamone JD, Pardo M, Yohn SE, López-Cruz L, SanMiguel N, Correa M. Mesolimbic dopamine and the regulation of motivated behavior. Curr Top Behav Neurosci. 2016;27:231-257. doi: 10.1007/7854_2015_383 [DOI] [PubMed] [Google Scholar]
  • 30.Soubrié P. Reconciling the role of central serotonin neurons in human and animal behavior. Behav Brain Sci. 1986;9(2):319-335. doi: 10.1017/S0140525X00022871 [DOI] [Google Scholar]
  • 31.Dayan P. Instrumental vigour in punishment and reward. Eur J Neurosci. 2012;35(7):1152-1168. doi: 10.1111/j.1460-9568.2012.08026.x [DOI] [PubMed] [Google Scholar]
  • 32.Niv Y, Daw ND, Joel D, Dayan P. Tonic dopamine: opportunity costs and the control of response vigor. Psychopharmacology (Berl). 2007;191(3):507-520. doi: 10.1007/s00213-006-0502-4 [DOI] [PubMed] [Google Scholar]
  • 33.Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372(71-n):n71. doi: 10.1136/bmj.n71 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Viechtbauer W. Conducting Meta-Analyses in R with the metafor Package. J Stat Softw. 2010;36(3):1-48. doi: 10.18637/jss.v036.i03 [DOI] [Google Scholar]
  • 35.Westbrook A, van den Bosch R, Määttä JI, et al. Dopamine promotes cognitive effort by biasing the benefits versus costs of cognitive work. Science. 2020;367(6484):1362-1366. doi: 10.1126/science.aaz5891 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Jocham G, Klein TA, Ullsperger M. Dopamine-mediated reinforcement learning signals in the striatum and ventromedial prefrontal cortex underlie value-based choices. J Neurosci. 2011;31(5):1606-1613. doi: 10.1523/JNEUROSCI.3904-10.2011 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Admon R, Kaiser RH, Dillon DG, et al. Dopaminergic enhancement of striatal response to reward in major depression. Am J Psychiatry. 2017;174(4):378-386. doi: 10.1176/appi.ajp.2016.16010111 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Frank MJ, O’Reilly RC. A mechanistic account of striatal dopamine function in human cognition: psychopharmacological studies with cabergoline and haloperidol. Behav Neurosci. 2006;120(3):497-517. doi: 10.1037/0735-7044.120.3.497 [DOI] [PubMed] [Google Scholar]
  • 39.Walsh AEL, Huneke NTM, Brown R, Browning M, Cowen P, Harmer CJ. A dissociation of the acute effects of bupropion on positive emotional processing and reward processing in healthy volunteers. Front Psychiatry. 2018;9:482. doi: 10.3389/fpsyt.2018.00482 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Bernacer J, Corlett PR, Ramachandra P, et al. Methamphetamine-induced disruption of frontostriatal reward learning signals: relation to psychotic symptoms. Am J Psychiatry. 2013;170(11):1326-1334. doi: 10.1176/appi.ajp.2013.12070978 [DOI] [PubMed] [Google Scholar]
  • 41.Westbrook A, van den Bosch R, Hofmans L, et al. Striatal dopamine can enhance learning, both fast and slow, and also make it cheaper. bioRxiv. Preprint posted online February 17, 2024. doi: 10.1101/2024.02.14.580392 [DOI]
  • 42.Howlett JR, Huang H, Hysek CM, Paulus MP. The effect of single-dose methylphenidate on the rate of error-driven learning in healthy males: a randomized controlled trial. Psychopharmacology (Berl). 2017;234(22):3353-3360. doi: 10.1007/s00213-017-4723-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Addicott MA, Pearson JM, Schechter JC, Sapyta JJ, Weiss MD, Kollins SH. Attention-deficit/hyperactivity disorder and the explore/exploit trade-off. Neuropsychopharmacology. 2021;46(3):614-621. doi: 10.1038/s41386-020-00881-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Bellebaum C, Kuchinke L, Roser P. Modafinil alters decision making based on feedback history—a randomized placebo-controlled double blind study in humans. J Psychopharmacol. 2017;31(2):243-249. doi: 10.1177/0269881116668591 [DOI] [PubMed] [Google Scholar]
  • 45.Pessiglione M, Seymour B, Flandin G, Dolan RJ, Frith CD. Dopamine-dependent prediction errors underpin reward-seeking behaviour in humans. Nature. 2006;442(7106):1042-1045. doi: 10.1038/nature05051 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Jansen M, Lockwood PL, Cutler J, de Bruijn ERA. l-DOPA and oxytocin influence the neurocomputational mechanisms of self-benefitting and prosocial reinforcement learning. Neuroimage. 2023;270:119983. doi: 10.1016/j.neuroimage.2023.119983 [DOI] [PubMed] [Google Scholar]
  • 47.Weis T, Puschmann S, Brechmann A, Thiel CM. Effects of L-dopa during auditory instrumental learning in humans. PLoS One. 2012;7(12):e52504. doi: 10.1371/journal.pone.0052504 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Chakroun K, Wiehler A, Wagner B, et al. Dopamine regulates decision thresholds in human reinforcement learning in males. Nat Commun. 2023;14(1):5369. doi: 10.1038/s41467-023-41130-y [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Halahakoon DC, Kaltenboeck A, Martens M, et al. Pramipexole enhances reward learning by preserving value estimates. Biol Psychiatry. 2024;95(3):286-296. doi: 10.1016/j.biopsych.2023.05.023 [DOI] [PubMed] [Google Scholar]
  • 50.Jocham G, Klein TA, Ullsperger M. Differential modulation of reinforcement learning by D2 dopamine and NMDA glutamate receptor antagonism. J Neurosci. 2014;34(39):13151-13162. doi: 10.1523/JNEUROSCI.0757-14.2014 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Kahnt T, Weber SC, Haker H, Robbins TW, Tobler PN. Dopamine D2-receptor blockade enhances decoding of prefrontal signals in humans. J Neurosci. 2015;35(9):4104-4111. doi: 10.1523/JNEUROSCI.4182-14.2015 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52.Murray GK, Knolle F, Ersche KD, et al. Dopaminergic drug treatment remediates exaggerated cingulate prediction error responses in obsessive-compulsive disorder. Psychopharmacology (Berl). 2019;236(8):2325-2336. doi: 10.1007/s00213-019-05292-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53.Mikus N, Korb S, Massaccesi C, et al. Effects of dopamine D2/3 and opioid receptor antagonism on the trade-off between model-based and model-free behaviour in healthy volunteers. eLife. 2022;11:e79661. doi: 10.7554/eLife.79661 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 54.Cremer A, Kalbe F, Müller JC, Wiedemann K, Schwabe L. Disentangling the roles of dopamine and noradrenaline in the exploration-exploitation tradeoff during human decision-making. Neuropsychopharmacology. 2023;48(7):1078-1086. doi: 10.1038/s41386-022-01517-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55.Diederen KM, Ziauddeen H, Vestergaard MD, Spencer T, Schultz W, Fletcher PC. Dopamine modulates adaptive prediction error coding in the human midbrain and striatum. J Neurosci. 2017;37(7):1708-1720. doi: 10.1523/JNEUROSCI.1979-16.2016 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 56.Haarsma J, Fletcher PC, Griffin JD, et al. Precision weighting of cortical unsigned prediction error signals benefits learning, is mediated by dopamine, and is impaired in psychosis. Mol Psychiatry. 2021;26(9):5320-5333. doi: 10.1038/s41380-020-0803-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 57.Eisenegger C, Naef M, Linssen A, et al. Role of dopamine D2 receptors in human reinforcement learning. Neuropsychopharmacology. 2014;39(10):2366-2375. doi: 10.1038/npp.2014.84 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 58.Grob S, Pizzagalli DA, Dutra SJ, et al. Dopamine-related deficit in reward learning after catecholamine depletion in unmedicated, remitted subjects with bulimia nervosa. Neuropsychopharmacology. 2012;37(8):1945-1952. doi: 10.1038/npp.2012.41 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 59.Cox SM, Frank MJ, Larcher K, et al. Striatal D1 and D2 signaling differentially predict learning from positive and negative outcomes. Neuroimage. 2015;109:95-101. doi: 10.1016/j.neuroimage.2014.12.070 [DOI] [PubMed] [Google Scholar]
  • 60.Pizzagalli DA, Evins AE, Schetter EC, et al. Single dose of a dopamine agonist impairs reinforcement learning in humans: behavioral evidence from a laboratory-based measure of reward responsiveness. Psychopharmacology (Berl). 2008;196(2):221-232. doi: 10.1007/s00213-007-0957-y [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 61.Wagner B, Clos M, Sommer T, Peters J. Dopaminergic modulation of human intertemporal choice: a diffusion model analysis using the d2-receptor antagonist haloperidol. J Neurosci. 2020;40(41):7936-7948. doi: 10.1523/JNEUROSCI.0592-20.2020 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 62.Erfanian Abdoust M, Froböse MI, Schnitzler A, Schreivogel E, Jocham G. Dopamine and acetylcholine have distinct roles in delay- and effort-based decision-making in humans. PLoS Biol. 2024;22(7):e3002714. doi: 10.1371/journal.pbio.3002714 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 63.de Wit H, Enggasser JL, Richards JB. Acute administration of d-amphetamine decreases impulsivity in healthy volunteers. Neuropsychopharmacology. 2002;27(5):813-825. doi: 10.1016/S0893-133X(02)00343-3 [DOI] [PubMed] [Google Scholar]
  • 64.Wardle MC, Treadway MT, Mayo LM, Zald DH, de Wit H. Amping up effort: effects of d-amphetamine on human effort-based decision-making. J Neurosci. 2011;31(46):16597-16602. doi: 10.1523/JNEUROSCI.4387-11.2011 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 65.Soder HE, Cooper JA, Lopez-Gamundi P, et al. Dose-response effects of d-amphetamine on effort-based decision-making and reinforcement learning. Neuropsychopharmacology. 2021;46(6):1078-1085. doi: 10.1038/s41386-020-0779-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 66.Daood M, Peled-Avron L, Ben-Hayun R, et al. Fronto-striatal connectivity patterns account for the impact of methylphenidate on choice impulsivity among healthy adults. Neuropharmacology. 2022;216:109190. doi: 10.1016/j.neuropharm.2022.109190 [DOI] [PubMed] [Google Scholar]
  • 67.Pine A, Shiner T, Seymour B, Dolan RJ. Dopamine, time, and impulsivity in humans. J Neurosci. 2010;30(26):8888-8896. doi: 10.1523/JNEUROSCI.6028-09.2010 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 68.Smith E, Theis H, van Eimeren T, et al. Dopamine and temporal discounting: revisiting pharmacology and individual differences. bioRxiv. Preprint posted online August 29, 2024. doi: 10.1101/2024.08.28.610170 [DOI] [PMC free article] [PubMed]
  • 69.Kayser AS, Allen DC, Navarro-Cebrian A, Mitchell JM, Fields HL. Dopamine, corticostriatal connectivity, and intertemporal choice. J Neurosci. 2012;32(27):9402-9409. doi: 10.1523/JNEUROSCI.1180-12.2012 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 70.Weber SC, Beck-Schimmer B, Kajdi ME, Müller D, Tobler PN, Quednow BB. Dopamine D2/3- and μ-opioid receptor antagonists reduce cue-induced responding and reward impulsivity in humans. Transl Psychiatry. 2016;6(7):e850. doi: 10.1038/tp.2016.113 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 71.Kelm MK, Boettiger CA. Effects of acute dopamine precusor depletion on immediate reward selection bias and working memory depend on catechol-O-methyltransferase genotype. J Cogn Neurosci. 2013;25(12):2061-2071. doi: 10.1162/jocn_a_00464 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 72.Ikeda Y, Funayama T, Tateno A, Fukayama H, Okubo Y, Suzuki H. Bupropion increases activation in nucleus accumbens during anticipation of monetary reward. Psychopharmacology (Berl). 2019;236(12):3655-3665. doi: 10.1007/s00213-019-05337-6 [DOI] [PubMed] [Google Scholar]
  • 73.Grimm O, Nägele M, Küpper-Tetzel L, de Greck M, Plichta M, Reif A. No effect of a dopaminergic modulation fMRI task by amisulpride and L-DOPA on reward anticipation in healthy volunteers. Psychopharmacology (Berl). 2021;238(5):1333-1342. doi: 10.1007/s00213-020-05693-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 74.Michely J, Viswanathan S, Hauser TU, Delker L, Dolan RJ, Grefkes C. The role of dopamine in dynamic effort-reward integration. Neuropsychopharmacology. 2020;45(9):1448-1453. doi: 10.1038/s41386-020-0669-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 75.Beierholm U, Guitart-Masip M, Economides M, et al. Dopamine modulates reward-related vigor. Neuropsychopharmacology. 2013;38(8):1495-1503. doi: 10.1038/npp.2013.48 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 76.Mehta MA, Swainson R, Ogilvie AD, Sahakian J, Robbins TW. Improved short-term spatial memory but impaired reversal learning following the dopamine D(2) agonist bromocriptine in human volunteers. Psychopharmacology (Berl). 2001;159(1):10-20. doi: 10.1007/s002130100851 [DOI] [PubMed] [Google Scholar]
  • 77.Au-Yeung SK, Halahakoon DC, Kaltenboeck A, Cowen P, Browning M, Manohar SG. The effects of pramipexole on motivational vigour during a saccade task: a placebo-controlled study in healthy adults. Psychopharmacology (Berl). 2024;241(7):1365-1375. doi: 10.1007/s00213-024-06567-z [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 78.Graf H, Wiegers M, Metzger CD, Walter M, Abler B. Differential noradrenergic modulation of monetary reward and visual erotic stimulus processing. Front Psychiatry. 2018;9:346. doi: 10.3389/fpsyt.2018.00346 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 79.Abler B, Erk S, Walter H. Human reward system activation is modulated by a single dose of olanzapine in healthy subjects in an event-related, double-blind, placebo-controlled fMRI study. Psychopharmacology (Berl). 2007;191(3):823-833. doi: 10.1007/s00213-006-0690-y [DOI] [PubMed] [Google Scholar]
  • 80.Mehta MA, Gumaste D, Montgomery AJ, McTavish SF, Grasby PM. The effects of acute tyrosine and phenylalanine depletion on spatial working memory and planning in healthy volunteers are predicted by changes in striatal dopamine levels. Psychopharmacology (Berl). 2005;180(4):654-663. doi: 10.1007/s00213-004-2128-8 [DOI] [PubMed] [Google Scholar]
  • 81.Kelm MK, Boettiger CA. Age moderates the effect of acute dopamine depletion on passive avoidance learning. Pharmacol Biochem Behav. 2015;131:57-63. doi: 10.1016/j.pbb.2015.01.014 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 82.Guitart-Masip M, Chowdhury R, Sharot T, Dayan P, Duzel E, Dolan RJ. Action controls dopaminergic enhancement of reward representations. Proceedings of the National Academy of Sciences of the United States of America. 2012;109(19):7511-7516. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 83.Guitart-Masip M, Economides M, Huys QJ, et al. Differential, but not opponent, effects of L -DOPA and citalopram on action learning with reward and punishment. Psychopharmacology (Berl). 2014;231(5):955-966. doi: 10.1007/s00213-013-3313-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 84.Scholz V, Hook RW, Kandroodi MR, et al. Cortical dopamine reduces the impact of motivational biases governing automated behaviour. Neuropsychopharmacology. 2022;47(8):1503-1512. doi: 10.1038/s41386-022-01291-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 85.Hebart MN, Gläscher J. Serotonin and dopamine differentially affect appetitive and aversive general pavlovian-to-instrumental transfer. Psychopharmacology (Berl). 2015;232(2):437-451. doi: 10.1007/s00213-014-3682-3 [DOI] [PubMed] [Google Scholar]
  • 86.Colwell MJ, Tagomori H, Shang F, et al. Direct serotonin release in humans shapes aversive learning and inhibition. Nat Commun. 2024;15(1):6617. doi: 10.1038/s41467-024-50394-x [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 87.Michely J, Eldar E, Erdman A, Martin IM, Dolan RJ. Serotonin modulates asymmetric learning from reward and punishment in healthy human volunteers. Commun Biol. 2022;5(1):812. doi: 10.1038/s42003-022-03690-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 88.Jepma M, Te Beek ET, Wagenmakers EJ, van Gerven JM, Nieuwenhuis S. The role of the noradrenergic system in the exploration-exploitation trade-off: a psychopharmacological study. Front Hum Neurosci. 2010;4:170. doi: 10.3389/fnhum.2010.00170 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 89.Luo Q, Kanen JW, Bari A, et al. Comparable roles for serotonin in rats and humans for computations underlying flexible decision-making. Neuropsychopharmacology. 2024;49(3):600-608. doi: 10.1038/s41386-023-01762-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 90.Finger EC, Marsh AA, Buzas B, et al. The impact of tryptophan depletion and 5-HTTLPR genotype on passive avoidance and response reversal instrumental learning tasks. Neuropsychopharmacology. 2007;32(1):206-215. doi: 10.1038/sj.npp.1301182 [DOI] [PubMed] [Google Scholar]
  • 91.Cools R, Robinson OJ, Sahakian B. Acute tryptophan depletion in healthy volunteers enhances punishment prediction but does not affect reward prediction. Neuropsychopharmacology. 2008;33(9):2291-2299. doi: 10.1038/sj.npp.1301598 [DOI] [PubMed] [Google Scholar]
  • 92.Crockett MJ, Clark L, Apergis-Schoute AM, Morein-Zamir S, Robbins TW. Serotonin modulates the effects of pavlovian aversive predictions on response vigor. Neuropsychopharmacology. 2012;37(10):2244-2252. doi: 10.1038/npp.2012.75 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 93.Robinson OJ, Cools R, Sahakian BJ. Tryptophan depletion disinhibits punishment but not reward prediction: implications for resilience. Psychopharmacology (Berl). 2012;219(2):599-605. doi: 10.1007/s00213-011-2410-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 94.Seymour B, Daw ND, Roiser JP, Dayan P, Dolan R. Serotonin selectively modulates reward value in human decision-making. J Neurosci. 2012;32(17):5833-5842. doi: 10.1523/JNEUROSCI.0053-12.2012 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 95.Gilger MD, Hellrung L, Neukam PT, et al. Arbitration between model-free and model-based control is not affected by transient changes in tonic serotonin levels. J Psychopharmacol. 2024;38(2):178-187. doi: 10.1177/02698811231216325 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 96.Crean J, Richards JB, de Wit H. Effect of tryptophan depletion on impulsive behavior in men with or without a family history of alcoholism. Behav Brain Res. 2002;136(2):349-357. doi: 10.1016/S0166-4328(02)00132-8 [DOI] [PubMed] [Google Scholar]
  • 97.Tanaka SC, Schweighofer N, Asahi S, et al. Serotonin differentially regulates short- and long-term prediction of rewards in the ventral and dorsal striatum. PLoS One. 2007;2(12):e1333. doi: 10.1371/journal.pone.0001333 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 98.Schweighofer N, Bertin M, Shishida K, et al. Low-serotonin levels increase delayed reward discounting in humans. J Neurosci. 2008;28(17):4528-4532. doi: 10.1523/JNEUROSCI.4982-07.2008 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 99.Worbe Y, Savulich G, Voon V, Fernandez-Egea E, Robbins TW. Serotonin depletion induces ‘waiting impulsivity’ on the human four-choice serial reaction time task: cross-species translational significance. Neuropsychopharmacology. 2014;39(6):1519-1526. doi: 10.1038/npp.2013.351 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 100.Cools R, Blackwell A, Clark L, Menzies L, Cox S, Robbins TW. Tryptophan depletion disrupts the motivational guidance of goal-directed behavior as a function of trait impulsivity. Neuropsychopharmacology. 2005;30(7):1362-1373. doi: 10.1038/sj.npp.1300704 [DOI] [PubMed] [Google Scholar]
  • 101.Roiser JP, Blackwell AD, Cools R, et al. Serotonin transporter polymorphism mediates vulnerability to loss of incentive motivation following acute tryptophan depletion. Neuropsychopharmacology. 2006;31(10):2264-2272. doi: 10.1038/sj.npp.1301055 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 102.Steding J, Ritschel F, Boehm I, et al. The effects of acute tryptophan depletion on instrumental reward learning in anorexia nervosa—an fMRI study. Psychol Med. 2023;53(8):3426-3436. doi: 10.1017/S0033291721005493 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 103.Higgins JPT, Thomas J, Chandler J, et al. Cochrane Handbook for Systematic Reviews of Interventions. Version 6.5. Cochrane; 2024. [Google Scholar]
  • 104.Bayer HM, Glimcher PW. Midbrain dopamine neurons encode a quantitative reward prediction error signal. Neuron. 2005;47(1):129-141. doi: 10.1016/j.neuron.2005.05.020 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 105.Tortora F, Hadipour AL, Battaglia S, Falzone A, Avenanti A, Vicario CM. The role of serotonin in fear learning and memory: a systematic review of human studies. Brain Sci. 2023;13(8):1197. doi: 10.3390/brainsci13081197 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 106.Malamud J, Lewis G, Moutoussis M, et al. The selective serotonin reuptake inhibitor sertraline alters learning from aversive reinforcements in patients with depression: evidence from a randomized controlled trial. Psychol Med. 2024;54(10):2719-2731. doi: 10.1017/S0033291724000837 [DOI] [PubMed] [Google Scholar]
  • 107.Moutoussis M, Garzón B, Neufeld S, et al. ; NSPN Consortium . Decision-making ability, psychopathology, and brain connectivity. Neuron. 2021;109(12):2025-2040.e7. doi: 10.1016/j.neuron.2021.04.019 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 108.Pike AC, Tan KHT, Tromblee H, Wing M, Robinson OJ. Test-retest reliability of two computationally-characterised affective bias tasks. Comput Psychiatr. 2024;8(1):217-232. doi: 10.5334/cpsy.92 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 109.Zorowitz S, Niv Y. Improving the reliability of cognitive task measures: a narrative review. Biol Psychiatry Cogn Neurosci Neuroimaging. 2023;8(8):789-797. doi: 10.1016/j.bpsc.2023.02.004 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 110.Hamid AA, Pettibone JR, Mabrouk OS, et al. Mesolimbic dopamine signals the value of work. Nat Neurosci. 2016;19(1):117-126. doi: 10.1038/nn.4173 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 111.Castrellon JJ, Meade J, Greenwald L, Hurst K, Samanez-Larkin GR. Dopaminergic modulation of reward discounting in healthy rats: a systematic review and meta-analysis. Psychopharmacology (Berl). 2021;238(3):711-723. doi: 10.1007/s00213-020-05723-5 [DOI] [PubMed] [Google Scholar]
  • 112.Costello H, Berry AJ, Reeves S, et al. Disrupted reward processing in Parkinson’s disease and its relationship with dopamine state and neuropsychiatric syndromes: a systematic review and meta-analysis. J Neurol Neurosurg Psychiatry. 2022;93(5):555-562. doi: 10.1136/jnnp-2021-327762 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 113.Miyazaki KW, Miyazaki K, Tanaka KF, et al. Optogenetic activation of dorsal raphe serotonin neurons enhances patience for future rewards. Curr Biol. 2014;24(17):2033-2040. doi: 10.1016/j.cub.2014.07.041 [DOI] [PubMed] [Google Scholar]
  • 114.Arnow BA, Blasey C, Williams LM, et al. Depression subtypes in predicting antidepressant response: a report from the iSPOT-D Trial. Am J Psychiatry. 2015;172(8):743-750. doi: 10.1176/appi.ajp.2015.14020181 [DOI] [PubMed] [Google Scholar]
  • 115.Chekroud AM, Hawrilenko M, Loho H, et al. Illusory generalizability of clinical prediction models. Science. 2024;383(6679):164-167. doi: 10.1126/science.adg8538 [DOI] [PubMed] [Google Scholar]
  • 116.Lan DCL, Browning M. What can reinforcement learning models of dopamine and serotonin tell us about the action of antidepressants? Comput Psychiatr. 2022;6(1):166-188. doi: 10.5334/cpsy.83 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 117.Eckstein MK, Master SL, Xia L, Dahl RE, Wilbrecht L, Collins AGE. The interpretation of computational model parameters depends on the context. Elife. 2022;11:e75474. doi: 10.7554/eLife.75474 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 118.Cools R, D’Esposito M. Inverted-U-shaped dopamine actions on human working memory and cognitive control. Biol Psychiatry. 2011;69(12):e113-e125. doi: 10.1016/j.biopsych.2011.03.028 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 119.Kanen JW, Apergis-Schoute AM, Yellowlees R, et al. Serotonin depletion impairs both pavlovian and instrumental reversal learning in healthy humans. Mol Psychiatry. 2021;26(12):7200-7210. doi: 10.1038/s41380-021-01240-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 120.Dreher JC, Schmidt PJ, Kohn P, Furman D, Rubinow D, Berman KF. Menstrual cycle phase modulates reward-related neural function in women. Proc Natl Acad Sci U S A. 2007;104(7):2465-2470. doi: 10.1073/pnas.0605569104 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 121.Faulkner P, Deakin JF. The role of serotonin in reward, punishment and behavioural inhibition in humans: insights from studies with acute tryptophan depletion. Neurosci Biobehav Rev. 2014;46(Pt 3):365-378. doi: 10.1016/j.neubiorev.2014.07.024 [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplement 1.

eMethods

eResults

eReferences

eAppendix

jamapsychiatry-e250839-s001.pdf (1MB, pdf)
Supplement 2.

Data sharing statement

jamapsychiatry-e250839-s002.pdf (16.3KB, pdf)

Articles from JAMA Psychiatry are provided here courtesy of American Medical Association

RESOURCES