Introduction

Happiness is a central component of human experience, providing a subjective signal of value and guiding actions aimed at maximizing well-being^1,2. Yet, well-being is often disrupted, as evidenced by the high prevalence of mood disorders such as depression and anxiety. It is therefore crucial to understand what drives mood fluctuations, and how these processes promote happiness or contribute to mood disorders.

Mood dynamics are strongly influenced by reward prediction error (RPE) — discrepancies between expected and actual outcomes^3–9. This influence is particularly prominent in uncertain environments, such as gambling scenarios, where outcomes frequently deviate from expectations and thereby drive moment-to-moment mood changes^8,10–13. Although RPE-induced mood fluctuations play a critical role in adaptive behavior^4,7,14,15, atypical mood dynamics may increase vulnerability to affective disorders^4,16–18. Specifically, depression, often associated with blunted emotional responses^19,20, may be linked to dampened mood fluctuations^21–25. In contrast, anxiety, characterized by exaggerated responses to uncertainty²⁶, might intensify them^27,28. However, distinguishing the unique effects of depression and anxiety on mood dynamics presents a significant challenge, owing to their overlapping symptoms and entangled nature^29,30.

Recent work has used bifactor models of the tripartite model of depression and anxiety to clarify their distinct features and differential influences on decision-making^31,32. The tripartite model of anxiety and depression proposes that these two symptom dimensions share a broad general distress or negative affect component while also including symptom-specific components: low positive affect/anhedonia is more specific to depression, whereas physiological hyperarousal is more specific to anxiety^30,33,34. Bifactor analysis offers a way to model this structure statistically. In a bifactor model, symptoms load on a general factor reflecting their shared variance and on specific factors capturing residual variance in narrower symptom dimensions after accounting for the general factor. Although bifactor and hierarchical models have long been used in psychometrics, e.g., intelligence research^35,36, their application to anxiety and depression is grounded in the tripartite model and subsequent psychometric work distinguishing general internalizing/distress from symptom-specific dimensions. This framework has recently been extended to computational psychiatry, where shared and specific affective symptom dimensions have been linked to task-derived computational parameters. For example, Gagne et al. (2022) used bifactor analysis to show that depression was associated with weaker prior beliefs, whereas anxiety was associated with a stronger negative bias in belief updatin³¹. Intriguingly, mood sensitivity to RPE appears to remain intact in both patients with major depressive disorder (MDD) and individuals with high depression scores^12,25,37, contrary to the hypothesis that depression is associated with reduced RPE-related mood sensitivity²⁵. Individuals with anxiety not only show heightened vigilance before an outcome is known but may also assign greater precision to information that resolves uncertainty^38,39. Such increased precision-weighting could amplify the affective influence of RPEs, leading to larger mood shifts when outcomes deviate from expectations^40,41. Given evidence suggesting opposite associations of depression and anxiety with mood fluctuations^23,24,27,28, one possible explanation for the apparent intactness of RPE-related mood sensitivity²⁵ is that depression and anxiety exert opposing effects on this sensitivity, thereby counteracting each other. To test this hypothesis, we applied a bifactor model to disentangle these interrelated influences.

This study used a computational model of momentary mood in a gambling task across five experiments involving 2,043 participants. Participants also completed a series of questionnaires, enabling bifactor analysis to dissociate the influences of anxiety- and depression-specific traits on mood fluctuations. We measured momentary mood by asking participants, “How happy are you at this moment?” This measure has also been used to index happiness or momentary subjective well-being^8,14,15,42. Although mood can persist for hours, days, or even weeks^4,5,7,43, momentary mood measured in laboratory settings captures how multiple events accumulate to shape affective state over minutes^{4,7,8,11–13,25,37,44}. Its external validity is supported by associations with depressive symptoms^12,25. We first validated the tripartite model of depression and anxiety before examining the specific roles of depression and anxiety in mood fluctuations (n = 901). Next, we measured mood sensitivity to RPE in a gambling task and investigated its relation to anxiety- and depression-specific traits based on the tripartite model in a laboratory experiment (n = 44) and two additional online experiments (n = 747 and n = 235). In the final experiment (n = 116), we tested the generalizability of these findings in a clinical sample of patients with affective disorders, revealing dissociable roles of depression and anxiety in RPE-related mood dynamics.

Results

Experimental Protocol

After completing a battery of questionnaires³² (see Methods), participants performed a gambling task with momentary mood ratings^8,10,13. Within this task, participants were asked to choose between a certain option and a gamble option with two possible outcomes, each occurring with a 50% probability. Participants were instructed to rate their mood every 2–3 trials (Figure 1). Detailed participant demographics are summarized in Table 1. Choice data (e.g., gambling rates) and mood data (e.g., initial mood, mean mood, and mood variation) showed patterns similar to those reported in previous studies measuring momentary mood during gambling tasks (Figure S2 & S3)^10,45. We also replicated established effects on momentary mood: mood was higher following gains than following losses, and mood drifted over time (all ps < 0.001; Figure S4).

Figure 1.

Table 1

To examine the computational drivers of momentary mood, we used the classic mood model, assuming that momentary mood is modeled as a recency-weighted sum of certain rewards from chosen certain options (CR), expected values of chosen gambles (EV), and reward prediction errors following gamble outcomes (RPE; Equation 1; Figure 1H)^8,13. RPE was defined as the difference between the obtained outcome and the expected value of the chosen gamble. We also incorporated a drift parameter to account for gradual changes in happiness over time^42,44.

This model explained momentary moods well across all gambling-task datasets (R²: mean ± SD = 0.67 ± 0.20 for the laboratory dataset, 0.69 ± 0.17 for online dataset 1, and 0.64 ± 0.19 for online dataset 2, and 0.47 ± 0.21 for the clinical dataset; see Supplementary Materials Note 2 and Note 5 for mood model space; see Table S2 & Table S4 for mood model comparisons), with good performance for parameter recovery (Figure S5). Across all gambling-task datasets, we replicated previous model-based findings on momentary mood^8,13: 1) the RPE weight was significantly higher than the EV weight (ts > 3.84, ps < 0.001; Figure 1I), suggesting that momentary mood was more strongly influenced by reward prediction errors following gamble outcomes than by the expected value of chosen gamble; 2) the baseline mood parameter β₀ was positively correlated with the initial mood ratings (rs > 0.25, ps < 0.006), supporting the interpretability of this model parameter. We also replicated previous depression-related findings²⁵: depression symptom measured by Beck Depression Inventory (BDI) was negatively correlated with the baseline mood parameter β₀ (r = -0.173, p < 0.001; Figure S6). In sum, these confirmatory results support the validity of the questionnaire and task measures, the substantial contribution of RPEs to momentary mood fluctuations, and a robust association between BDI-measured depressive symptoms and the baseline mood parameter.

Depression is associated with dampened mood fluctuations through reduced sensitivity to RPEs

We first examined the association between depression and mood fluctuations, as well as its computational signature. Depression and anxiety often cooccur at the symptom level^29,34,46,47, as reflected by a high correlation between depressive symptoms measured by the BDI and anxiety symptoms measured by the anxiety subscale of the Trait Anxiety Inventory (r = 0.75, p < 0.001). To differentiate the unique influences of anxiety- and depression-specific factors, we applied the validated bifactor structure from the psychometric dataset (Figure 1C) to decompose depression and anxiety symptoms into three orthogonal components: the general factor, anxiety-specific factor, and depression-specific factor (-0.001 < rs < 0.001, all ps = 1.000; see Supplementary Note 1 for psychometric properties of the tripartite model of depression and anxiety in our sample; n = 901).

To examine whether depression-specific factor scores were associated with mood fluctuations, we performed correlation analyses between the depression-specific factor and mood variation, defined as the standard deviation of happiness ratings across trials. We found convergent results across the laboratory dataset, online dataset 1, and online dataset 2. Specifically, depression-specific scores were negatively correlated with mood variation (rs < -0.13, ps < 0.041; Figures 2A–C). Model-based correlational analyses further showed negative correlations between depression-specific scores and RPE-related mood sensitivity (β_RPE; rs < -0.14, ps <0.019; Figure 2E-G), which remained significant after controlling for gender, age, task earnings, and mood drift (ps < 0.035). Bootstrap validation yielded consistent results, indicating that higher depression-specific scores were associated with reduced mood fluctuations and decreased RPE-related mood sensitivity. Given the strong correlation between β_RPE and mood variation (rs > 0.67, ps < 0.001), we further conducted a mediation analysis to examine whether individual differences in RPE-related mood sensitivity statistically accounted for the association between depression loading and mood variation. This analysis was motivated by the hypothesis that depression-related dampening of mood variability may arise, at least in part, from reduced mood sensitivity to RPEs. The results supported our hypothesis (ps < 0.015; Figure 2I), suggesting that higher depression-specific scores are associated with dampened momentary mood variation through reduced RPE-related mood sensitivity. See Table S7 for full statistical results for each dataset. To further test whether the association between depression and mood fluctuations was specific to RPE-related mood sensitivity, we regressed depression-specific factor scores on mood sensitivities to CR, EV, and RPE in the combined dataset (N = 1,026). Only RPE-related mood sensitivity was significantly associated with depression-specific scores (CR: t = 0.274, p = 0.784; EV: t = -1.069, p = 0.285; RPE: t = -3.282, p = 0.001; Figure 2K).

Figure 2.

Anxiety is associated with intensified mood fluctuations through increased sensitivity to RPEs

We then examined how anxiety was associated with mood fluctuations. Correlations between the anxiety-specific factor and mood variation were positive in direction across datasets, although they were not statistically significant in several datasets (the laboratory dataset: r = 0.10, p = 0.531; the online dataset 1: r = 0.08, p = 0.026; the online dataset 2: r = 0.19, p = 0.004). Similarly, correlations between the anxiety-specific factor and β_RPE were positive in direction but statistically inconsistent across datasets (the laboratory dataset: r = 0.04, p = 0.820; the online dataset 1: r = 0.05, p = 0.216; the online dataset 2: r = 0.19, p = 0.004; Figure 2A-C & 2E-G). Because these datasets used comparable task and questionnaire procedures and showed positive effect directions, and because reliable individual differences often require large samples to detect⁴⁸, we combined the laboratory dataset, online dataset 1, and online dataset 2 (total N = 1,026). This approach is analogous to an individual-participant-data meta-analytic analysis. We fitted linear mixed-effects models predicting mood variation and β_RPE from the three bifactor scores, with dataset included as a random intercept to account for dataset-level variability. For mood variation, the anxiety-specific factor was positively associated with mood variation (t = 3.46, p < 0.001), whereas the depression-specific factor was negatively associated with mood variation (t = -6.13, p < 0.001). For RPE-related mood sensitivity, the anxiety-specific factor was positively associated with β_RPE (t = 2.60, p = 0.009), whereas the depression-specific factor was negatively associated with β_RPE (t = -5.30, p < 0.001). These associations remained significant after controlling for gender, age, task earnings, and mood drift. In addition, we performed a mini metaanalysis on these correlation coefficients⁴⁹. Results showed significant positive correlation for both mood variation and RPE-related mood sensitivity (mood variation: Z = 3.399, 95 % CI for correlation coefficient r [0.045, 0.166]; RPE-related mood sensitivity: Z = 2.618, 95 % CI for correlation coefficient r [0.021, 0.143]), supporting that anxiety is associated with intensified mood fluctuations and increased mood sensitivity to RPE in non-clinical participants. Mediation analysis further showed that heightened mood sensitivity to RPEs statistically mediated the association between anxiety loading and greater mood fluctuations (a × b = 0.055, 95% CI = [0.013, 0.097], p = 0.010; Figure 2J). Moreover, linear regression against anxiety-specific factor with mood sensitivity to CR, EV, and RPE as regressors showed RPE-specific mood hypersensitivity with anxiety loading (CR: t = -0.36, p = 0.718; EV: t = -0.35, p = 0.724; RPE: t = 2.19, p = 0.028; Figure 2L).

To address the possibility that individual differences in subjective scale calibration confounded our findings, we conducted several complementary analyses. For example, individuals with nonlinear utility functions, such as risk-seeking participants, might show disproportionately large mood responses to large versus small wins. We therefore reanalyzed two publicly available datasets using similar risky decision-making tasks with repeated happiness ratings: Vanhasbroeck et al. (2021; n = 49)¹³ and the Rutledge smartphone app dataset (n = 46,204). See Supplementary Note 3 for details. Collectively, these analyses suggested that individual differences in risk preference did not account for our primary findings regarding distinct mood dynamics in anxiety versus depression. These results further suggest a partial dissociation between decision-related risk preferences and RPE-related mood dynamics. Together, these results suggest that depression and anxiety are associated with opposite patterns of mood dynamics through RPE-specific mood sensitivity.

Differential associations of depression and anxiety with mood fluctuations

To directly test whether depression- and anxiety-specific factors differed in their associations with mood dynamics, we compared the corresponding correlations. These comparisons showed that depression-specific associations were significantly more negative than anxiety-specific associations for both mood variation (laboratory dataset: Z = -1.84, p = 0.033; online dataset 1: Z = -5.36, p < 0.001; online dataset 2: Z = -3.42, p < 0.001) and β_RPE (laboratory dataset: Z = -1.77, p = 0.038; online dataset 1: Z = -3.67, p < 0.001; online dataset 2: Z = -4.00, p < 0.001; Figures 2A–C and 2E–G). These results support distinct associations of depression- and anxiety-specific factors with RPE-related mood dynamics.

Clinical relevance of reduced RPE-related mood fluctuations in depression

To test whether abnormalities in RPE-driven mood fluctuations can serve as clinically relevant computational markers of depression- and anxiety-related symptom dimensions, we recruited patients with affective disorders (n = 116) to complete the same questionnaire battery and gambling task with momentary mood ratings (Figure 1). Demographic, psychological, and clinical characteristics are summarized in Table 1 and Table S8. We observed significant negative correlations between depression-specific scores and both mood variation (r = -0.239, p = 0.009) and RPE-related mood sensitivity (β_RPE; r = -0.216, p = 0.020). These associations remained significant after controlling for demographic and clinical covariates, task earnings, and mood drift (ps < 0.05). Bootstrap validation yielded consistent results. Mediation analyses further showed that reduced mood sensitivity to RPEs statistically mediated the association between depressionspecific scores and lower mood fluctuations (a × b = -0.141, 95% CI = [-0.261, -0.038], p = 0.021; Figure 3). However, we did not observe significant correlation with anxiety (mood variation: r = -0.092, p = 0.327; β_RPE: r = -0.095, p = 0.311).

Figure 3.

Exploratory associations of the common factor with mood and choice parameters

Given the theoretical relevance of the common factor—often linked to shared symptoms such as somatic complaints, sleep disturbances, and cognitive impairments³⁴—as well as the potential for nonlinear associations with symptom severity, we conducted exploratory analyses to examine its relationship with choice and mood parameters in both healthy and clinical datasets. Choice parameters were estimated using an established approachavoidance prospect theory model^10,45,50, which included loss aversion, domain-specific risk attitude parameters in the gain and loss domains, and value-independent Pavlovian approach and avoidance parameters (see Supplementary Note 7 for details of the computational choice models). In this model, risk attitude was quantified by the exponent parameter a in a prospect-theory-inspired subjective value function. Lower α values reflect greater risk aversion, whereas values closer to or above 1 reflect more linear or risk-seeking valuation.

To improve robustness and reduce the risk of false positives, we focused on findings that were consistent across datasets. Model comparisons using BIC consistently favored linear over nonlinear models for associations between the common factor and mood and choice parameters (Table 2). Specifically, we observed significant negative associations between the common factor and two key variables: the baseline mood parameter and gain-domain risk attitude (Figure 4). Notably, these parameters were not consistently related to either the depression-specific or anxiety-specific factor across healthy and clinical datasets (depression × baseline mood parameter: r = -0.12, p < 0.001 for the healthy datasets and r = -0.08, p = 0.934 for the clinical dataset; anxiety × baseline mood parameter: r = - 0.14, p < 0.001 for the healthy datasets and r = -0.15, p = 0.104 for the clinical dataset; depression × risk attitude for gain: r = -0.02, p = 0.505 for the healthy datasets and r = -0.00, p = 0.961 for the clinical dataset; anxiety × risk attitude for gain: r = 0.01, p = 0.846 for the healthy datasets and r = 0.04, p = 0.688 for the clinical dataset). These findings suggest that higher common-factor scores, reflecting general internalizing psychopathology, are associated with a lower affective baseline and greater gain-domain risk aversion, consistent with the idea that the common factor captures transdiagnostic features shared across mood and anxiety disorders.

Figure 4.

Table 2.

Discussion

Although atypical mood dynamics are considered central features of depression and anxiety^21,23,27,28, little is known about their underlying computational mechanisms. Our study offers computational insights into the distinct associations of depression and anxiety with mood fluctuations. By orthogonally decomposing shared and specific symptom variance in depression and anxiety, we were able to distinguish their specific associations with mood dynamics. Depression-specific symptoms were associated with dampened mood fluctuations through reduced mood sensitivity to RPEs, whereas anxiety-specific symptoms were linked to intensified mood fluctuations through increased mood sensitivity to RPEs in non-clinical datasets. Notably, the depression-related reduction in RPE-related mood sensitivity was replicated in patients with affective disorders, highlighting its potential clinical relevance as an affective computational marker. Moreover, the shared depression-anxiety component was linked to a lower affective baseline and greater risk aversion.

Depression and anxiety, though often coexisting^29,30, show contrasting associations with mood dynamics, consistent with their distinct affective profiles^24,27,28. Our computational model not only replicates the important role of RPEs in mood dynamics but also highlights the divergent mediating roles of RPE-related mood sensitivity in the associations of depression and anxiety with mood fluctuations. The opposite associations of depression and anxiety with mood sensitivity to RPEs complement previous findings of apparently intact RPE-related mood sensitivity in depression^12,25,37. These findings further underscore the necessity of decomposing shared and specific components of depression and anxiety in studies of mood dynamics, which can enhance our understanding of their distinct associations with emotion processing and cognitive flexibility. This point is consistent with bifactor-based work showing that shared and specific symptom dimensions can have different computational correlates. For example, Gagne et al. (2020) showed that bifactor-derived symptom dimensions differentially relate to maladaptation to environmental volatility³², complementing previous findings that trait anxiety is associated with inflexible adjustment to volatility³². Although the present study did not include neuroimaging, the observed computational dissociation may map onto partially distinct neural systems involved in reward learning, mood updating, and affective psychopathology. RPE processing has been consistently linked to striatal- midbrain dopaminergic reward-learning circuits^8,51. The integration of these rewardlearning signals into subjective mood and value-based decision-making may further involve the ventral medial prefrontal cortex and orbitofrontal cortex⁴⁴. In addition, the anterior insula may be particularly relevant for integrating feedback-related signals with affective and interoceptive states^8,44, potentially linking RPE processing to anxiety- and depression-related mood dynamics. Consistent with this view, Cecchi et al. (2022)⁵² used intracranial EEG to show that feedback-related neural activity tracks mood fluctuations and risky choice. Future neuroimaging studies should test whether depression-related reductions and anxiety-related increases in RPE-related mood sensitivity are associated with altered interactions among striatal, prefrontal, and insular circuits.

This study bridges research on affective dynamics and reward-based decision-making in depression. Anhedonia, a core symptom of depression, refers to a reduced ability to experience pleasure⁵³. In affective science, researchers have used experience-sampling methods over several weeks or months^5,7,27,54, showing dampened mood fluctuations in depression^23,24. In parallel, decision-making and learning research has often adopted laboratory-based learning or gambling tasks, revealing alterations in reward processing in individuals with depression^55,56. However, how altered reward processing translates into aberrant momentary hedonic experience remains largely unknown. Our study employed a gambling task with momentary mood ratings, allowing us to examine reward- and RPE- related mood dynamics. Consistent with both affective-science and reward-learning accounts^20,24, this study provides evidence that depressive symptoms are associated with reduced mood sensitivity to reward prediction errors.

In addition to supporting the proposed role of anxiety in intensified mood fluctuations^26–28, our study provides a computational account of this phenomenon: anxiety was associated with heightened mood sensitivity to RPEs. Notably, the anxiety-related effects were less robust than the depression-related effects and were detectable only in the pooled dataset (n = 1,026); therefore, they require further replication in larger samples. Anxiety symptoms are often marked by exaggerated anticipatory responses to uncertainty and excessive affective reactivity²⁶. Because RPEs quantify the discrepancy revealed when uncertain outcomes are resolved, our findings suggest a potential pathway through which anxiety may shape mood responses to uncertainty resolution—by amplifying affective responses to unexpected outcomes. In this way, individuals with higher anxiety may exhibit a stronger need to resolve uncertainty⁵⁷, which could contribute to increased mood reactivity to RPEs. Our findings differ from those of Browning et al. (2015)⁵⁷, who reported reduced adaptation of learning rates to environmental volatility in anxiety. However, the two findings may reflect different aspects of uncertainty processing: while Browning et al. (2015) focused on learning-rate adaptation in response to environmental volatility, our study targets affective responsiveness to outcome-level prediction errors. Thus, anxious individuals may exhibit reduced cognitive flexibility in belief updating yet increased emotional reactivity to prediction errors, highlighting a possible dissociation between cognitive and affective systems. Notably, the pattern of heightened RPE sensitivity observed in the pooled non-clinical dataset was not observed in the clinical sample. On the one hand, this discontinuity may reflect that the clinical sample was underpowered to detect anxiety-specific effects, especially given the high comorbidity between anxiety and depression in affective disorders (Table S8). Based on the effect size observed in the non-clinical datasets (r = 0.079), we estimated that a sample size of 1,226 would be required to detect this effect with 80% statistical power using a two-tailed test with α = .05. This estimate is substantially larger than the current clinical sample size (n = 116). On the other hand, it may reflect a disruption of mood homeostasis in clinical populations^41,58. In non-clinical individuals, counterbalancing associations of depression-and anxiety-related traits with mood variation may help maintain emotional equilibrium. In contrast, affective disorders may involve a loss of such regulatory balance, reducing the ability to stabilize mood in the face of competing depression-and anxiety-related affective signals.

The common factor—reflecting general internalizing psychopathology—was consistently associated with both a lower affective baseline (i.e., affective setpoint) and greater gaindomain risk aversion. Regarding baseline mood, previous large-scale studies have established a reliable association between a lower affective setpoint and elevated depressive symptoms in both individuals scoring high on the BDI and patients diagnosed with MDD²⁵. Extending this work, our bifactor approach decomposed shared and specific symptom variance in depression and anxiety into three orthogonal components and revealed that only the common factor, rather than the depression- or anxiety-specific components, was significantly associated with a lower affective baseline. This finding suggests that the shared general distress component, rather than depression- or anxietyspecific symptom variance, is more closely related to individuals’ affective baselines⁴. With respect to decision-making, prior literature using risky decision-making tasks without feedback has linked pathological anxiety to greater risk aversion⁵⁹. In line with this, our results from a risky decision-making task with feedback suggest that the common factor, rather than anxiety-specific variance per se, is more consistently associated with risk aversion. This suggests that heightened gain-domain risk aversion may be a transdiagnostic feature of internalizing psychopathology, rather than being uniquely attributable to anxiety. Notably, model comparison favored linear over nonlinear associations, indicating that even subclinical levels of general internalizing symptoms are measurably associated with affective baseline and gain-domain risk processing. Taken together, these findings underscore the importance of incorporating general internalizing dimensions into computational models of affect and choice.

Several limitations of the present study should be noted. First, although anxiety- and depression-related associations differed consistently, the anxiety-specific associations themselves were less robust across datasets. This pattern may raise the possibility of confounding by scale-related variability—that is, individual differences in how participants calibrate or use mood rating scales. Because both mood variability and β_RPE are derived from subjective ratings, such scale-use differences could disproportionately affect weaker anxiety-specific effects. Our additional analyses, including analyses of two publicly available datasets with similar paradigms (Vanhasbroeck et al. 2021¹³, n = 49; Rutledge et al., n = 46,204), did not support this explanation (see Supplementary Note 3). Nevertheless, future studies would benefit from task designs that better control for scale sensitivity and elicit stronger mood responses to risky outcomes, possibly by incorporating more emotionally salient or high-stakes decision contexts. Second, our symptom assessment focused specifically on anxiety and depression. This choice was motivated by our primary hypotheses, but it limits our ability to evaluate the specificity of the observed associations. Recent work has shown that individuals with suicidal thoughts and behaviors exhibit reduced mood sensitivity to certain rewards (CR), but not to RPEs⁵⁰, suggesting that the current RPE-related effects are not driven by suicide-related processes. However, because we did not assess other psychiatric dimensions, such as compulsivity or schizophrenia-spectrum symptoms, we cannot determine whether the current findings are specific to anxiety- and depression-related symptom dimensions or instead reflect broader transdiagnostic psychopathology or nonspecific response-related variance. Future studies should include measures covering a wider range of psychiatric dimensions, such as internalizing, externalizing, inattentive/neurodevelopmental, mood/anxiety, and withdrawal dimensions identified by Wise et al. (2026)⁶⁰, to better characterize whether links among symptom dimensions, RPE sensitivity, and mood variability are disorder-specific or transdiagnostic.

In conclusion, our findings emphasize the distinct associations of depression and anxiety with mood fluctuations, underlining the importance of partitioning shared and specific symptom variance in affective science. In particular, the robust association between depression and reduced RPE-related mood sensitivity may inform the development of computationally informed, mechanism-based interventions for affective disorders, offering a promising direction for future research and clinical translation.

Methods

A total of 2634 participants via online platforms (questionnaires from https://www.wjx.cn and tasks from https://www.naodao.com) took part in five experiments, including a psychometric experiment, a laboratory experiment, two online replication experiments. Participants were recruited through participant pools and study advertisement. For online experiments, interested participants accessed the study through an online link and completed the questionnaires and task remotely. For the laboratory experiment, participants completed the study in a controlled laboratory setting. The study was approved by the Ethics Committee of Beijing Normal University (approve number: ICBIR_A_0016_028). Written or electronic informed consent was obtained from all participants before participation. Participants were paid a basic participation fee and a performance-based bonus, and the payment structure was explained before the experiment.

Participants

Data from 1145 participants was collected in the psychometric experiment. Participants were excluded if 1) they failed any of the attentional checks (4 items); 2) they made the same choices for all items; 3) they responded with extreme inconsistency in two similar questionnaires (difference in z-scores out of ±2). The final sample for the psychometric dataset consisted of 901 participants. We recruited 59, 1087, and 343 participants for the laboratory experiment, the online experiment 1, and the online experiment 2, respectively. Participants were excluded if 1) they failed any of the attentional checks in questionnaires (4 items) 2) they failed any of the catch trials (4 trials) and 3) they responded too fast (reaction time for decision < 200 ms) in more than 10% of the 90 trials. The final sample for the laboratory dataset, the online dataset 1, and the online dataset 2 included 44, 747, and 235 participants, separately. See Table 1 for demographic information and data collection periods. Because online data collection may raise concerns about AI-generated responses, we note that artificial intelligence tools, such as ChatGPT, became widely known to the public in November 2022, whereas all online experiments in the present study were conducted before November 2022 (see Table 1). Therefore, these data were unlikely to have been substantially affected by participants’ use of AI tools.

Measurements of anxiety and depression

In line with previous literature orthogonally decomposing anxiety and depression^31,32, participants completed a set of Chinese version questionnaires of anxiety and depression. These measurements included the Mood and Anxiety Symptoms Questionnaire (MASQ; 62 items)⁶¹, the Trait subscale of the State-Trait Anxiety Inventory (TAI; 20 items)⁶², the Beck Depression Inventory (BDI; 21 items)⁶³, the Penn State Worry Questionnaire (PSWQ; 16 items)⁶⁴, the Center for Epidemiologic Studies Depression Scale (CESD; 20 items)⁶⁵, and the Big Five Inventory-2 (BFI; 60 items)⁶⁶. Each item in the TAI, BDI, and CESD was rated on a four-point Likert scale, while five-point Likert rating scale was used for the MASQ, PSWQ, and BFI. There were four items for attentional checks, which required the participants to make a specific choice and were embedded in the entire measurements, e.g., “please select the second option for this item”.

Patients with affective disorders

We also recruited 121 patients with affective disorders, including major depressive disorder, anxiety disorder, and bipolar disorder. After excluding participants with no variance in mood ratings, the final sample included 116 patients. See Table 1 and Table S8 for details.

Experimental Procedure

Participants were asked to make a choice between a certain option and a gamble (50% probability for each outcome) and to rate their momentary moods. Before the task protocol, participants were asked to rate their current happiness that we consider as their initial mood. At the beginning of the task, participants were endowed with 500 points. Each trial started with two options (a gamble option and a certain option) that presented randomly on each side (Figure 1F). Upon response, the chosen option would be highlighted in yellow for 0.5 s. Then the corresponding outcome at the screen center was presented for 1 s, followed by a fixation cross with a random duration (0.6~1.4 s). If the gamble was chosen, participants had equal probability to obtain each outcome. The obtained outcome would be accumulated to their total score, which was presenting at the top-right concern. Every 2~3 trials, participants rated “how happy are you at this moment” from 0 (very unhappy) to 100 (very happy) by moving a slider anchoring at midpoint (i.e., 50). Upon identifying their current mood, a fixation cross was presented with a random duration (0.6~1.4 s). Please note that the slider in the laboratory experiment was anchoring at the midpoint, i.e., 50. To exclude the potential anchoring effect, we did not set an anchor in the online replication experiment to check the robustness of our findings. This task consisted of 90 randomly presented trials, including 30 mixed trials, 30 gain trials, and 30 loss trials. In mixed trials, participants made a choice between a certain amount 0 and a gamble with a gain amount {40, 45, or 75} and a loss amount determined by a multiplier {0.2, 0.34, 0.5, 0.64, 0.77, 0.89, 1, 1.1, 1.35, or 2} on the gain amount. In gain trials, there was a certain gain amount {35, 45, or 55} and a gamble with 0 and a gain amount determined by a multiplier {1.68, 1.82, 2, 2.22, 2.48, 2.8, 3.16, 3.6, 4.2, or 5} on the certain gain amount. In loss trials, there were a certain loss amount {-35, -45, or -55} and a gamble with 0 and a loss amount determined by a multiplier {1.68, 1.82, 2, 2.22, 2.48, 2.8, 3.16, 3.6, 4.2, or 5} on the certain loss amount. Many amounts and multipliers were used to accommodate a wide range of risk and loss sensitivity, as in previous literature^8,10. We also set 4 trials embedded in the entire task for attentional checks. For example, participants were asked to make a choice between a certain gain 20 and a gamble 35/55, where the correct response for this trial was the gamble choice. All experimental procedures were programmed using Psychopy3 (2021.2.3) builder and hosted on https://www.naodao.com.

Model fitting

We fit model parameters by using the method of maximum likelihood estimation (MLE) with fmincon function of MATLAB (version R2015a) at the individual level. To avoid local minimum, we ran this optimization function with random starting locations 50 times. Bayesian information criteria (BIC) were used to compare model fits.

Mediation model

The mediation model was conducted using a sequence of regression steps. First, a regression of the independent variable depression on the dependent variable mood variation was performed; Second, a regression of the independent variable depression on the mediator variable β_RPE was performed; Next, a regression of the independent variable depression and the mediator variable β_RPE on the dependent variable mood variation was performed; Finally, the indirect and total effects were estimated.

Statistical analysis

We performed correlations among depression/anxiety factor scores, behavioral indices, and parameters using Matlab R2015a. To further validate our main correlational results, the percentile bootstrap CIs were estimated using the R package ‘boot’ and 5000 bootstrap resamples. We used an online calculator (https://www.psychometrica.de/correlation.html) to examine differences between two correlation coefficients. All reported tests are two-tailed. We set the significance level at p = 0.05.

Data availability

The data and code that support the findings of this study are available from https://github.com/ZhihaoWangpsyer/depression_anxiety_mood

Acknowledgements

This study was funded by the National Natural Science Foundation of China (32371104, 31920103009, 32271093 and 32500929), the Major Project of National Social Science Foundation (20&ZD153), the National Science and Technology Innovation 2030 Major Program (2022ZD0205500), Beijing Natural Science Foundation (Z230010), Shenzhen-Hong Kong Institute of Brain Science – Shenzhen Fundamental Research Institutions (2023SHIBS0003), and Ministry of Education Humanities and Social Sciences (25YJC190023).

Additional information

Funding

MOST | National Natural Science Foundation of China (NSFC) (32371104)

• Yuejia Luo