Learning and Choice in Mood Disorders: Searching for the Computational Parameters of Anhedonia

Uncertainty of treatment efficacy moderates placebo effects on reinforcement learning

The placebo-reward hypothesis postulates that positive effects of treatment expectations on health (i.e., placebo effects) and reward processing share common neural underpinnings. Moreover, experiments in humans and animals indicate that reward uncertainty increases striatal dopamine, which is presumably involved in placebo responses and reward learning. Therefore, treatment uncertainty analogously to reward uncertainty may affect updating from rewards after placebo treatment. Here, we address whether different degrees of uncertainty regarding the efficacy of a sham treatment affect reward sensitivity. In an online between-subjects experiment with N = 141 participants, we systematically varied the provided efficacy instructions before participants first received a sham treatment that consisted of listening to binaural beats and then performed a probabilistic reinforcement learning task. We fitted a Q-learning model including two different learning rates for positive (gain) and negative (loss) reward prediction errors and an inverse gain parameter to behavioral decision data in the reinforcement learning task. Our results yielded an inverted-U-relationship between provided treatment efficacy probability and learning rates for gain, such that higher levels of treatment uncertainty, rather than of expected net efficacy, affect presumably dopamine-related reward learning. These findings support the placebo-reward hypothesis and suggest harnessing uncertainty in placebo treatment for recovering reward learning capabilities.

Perceptual Observer Modeling Reveals Likely Mechanisms of Face Expression Recognition Deficits in Depression

BACKGROUND

Deficits in face emotion recognition are well documented in depression, but the underlying mechanisms are poorly understood. Psychophysical observer models provide a way to precisely characterize such mechanisms. Using model-based analyses, we tested 2 hypotheses about how depression may reduce sensitivity to detect face emotion: 1) via a change in selectivity for visual information diagnostic of emotion or 2) via a change in signal-to-noise ratio in the system performing emotion detection.

METHODS

Sixty adults, one half meeting criteria for major depressive disorder and the other half healthy control participants, identified sadness and happiness in noisy face stimuli, and their responses were used to estimate templates encoding the visual information used for emotion identification. We analyzed these templates using traditional and model-based analyses; in the latter, the match between templates and stimuli, representing sensory evidence for the information encoded in the template, was compared against behavioral data.

RESULTS

Estimated happiness templates produced sensory evidence that was less strongly correlated with response times in participants with depression than in control participants, suggesting that depression was associated with a reduced signal-to-noise ratio in the detection of happiness. The opposite results were found for the detection of sadness. We found little evidence that depression was accompanied by changes in selectivity (i.e., information used to detect emotion), but depression was associated with a stronger influence of face identity on selectivity.

CONCLUSIONS

Depression is more strongly associated with changes in signal-to-noise ratio during emotion recognition, suggesting that deficits in emotion detection are driven primarily by deprecated signal quality rather than suboptimal sampling of information used to detect emotion.

Pramipexole Enhances Reward Learning by Preserving Value Estimates

BACKGROUND

Dopamine D2-like agonists show promise as treatments for depression. They are thought to act by enhancing reward learning; however, the mechanisms by which they achieve this are not clear. Reinforcement learning accounts describe 3 distinct candidate mechanisms: increased reward sensitivity, increased inverse decision-temperature, and decreased value decay. As these mechanisms produce equivalent effects on behavior, arbitrating between them requires measurement of how expectations and prediction errors are altered. We characterized the effects of 2 weeks of the D2-like agonist pramipexole on reward learning and used functional magnetic resonance imaging measures of expectation and prediction error to assess which of these 3 mechanistic processes were responsible for the behavioral effects.

METHODS

Forty healthy volunteers (50% female) were randomized to 2 weeks of pramipexole (titrated to 1 mg/day) or placebo in a double-blind, between-subject design. Participants completed a probabilistic instrumental learning task before and after the pharmacological intervention, with functional magnetic resonance imaging data collected at the second visit. Asymptotic choice accuracy and a reinforcement learning model were used to assess reward learning.

RESULTS

Pramipexole increased choice accuracy in the reward condition with no effect on losses. Participants who received pramipexole had increased blood oxygen level-dependent response in the orbital frontal cortex during the expectation of win trials but decreased blood oxygen level-dependent response to reward prediction errors in the ventromedial prefrontal cortex. This pattern of results indicates that pramipexole enhances choice accuracy by reducing the decay of estimated values during reward learning.

CONCLUSIONS

The D2-like receptor agonist pramipexole enhances reward learning by preserving learned values. This is a plausible mechanism for pramipexole's antidepressant effect.

Postnatal Phencyclidine-Induced Deficits in Decision Making Are Ameliorated by Optogenetic Inhibition of Ventromedial Orbitofrontal Cortical Glutamate Neurons

Background

The orbitofrontal cortex (OFC) is essential for decision making, and functional disruptions within the OFC are evident in schizophrenia. Postnatal phencyclidine (PCP) administration in rats is a neurodevelopmental manipulation that induces schizophrenia-relevant cognitive impairments. We aimed to determine whether manipulating OFC glutamate cell activity could ameliorate postnatal PCP-induced deficits in decision making.

Methods

Male and female Wistar rats (n = 110) were administered saline or PCP on postnatal days 7, 9, and 11. In adulthood, we expressed YFP (yellow fluorescent protein) (control), ChR2 (channelrhodopsin-2) (activation), or eNpHR 3.0 (enhanced halorhodopsin) (inhibition) in glutamate neurons within the ventromedial OFC (vmOFC). Rats were tested on the probabilistic reversal learning task once daily for 20 days while we manipulated the activity of vmOFC glutamate cells. Behavioral performance was analyzed using a Q-learning computational model of reinforcement learning.

Results

Compared with saline-treated rats expressing YFP, PCP-treated rats expressing YFP completed fewer reversals, made fewer win-stay responses, and had lower learning rates. We induced similar performance impairments in saline-treated rats by activating vmOFC glutamate cells (ChR2). Strikingly, PCP-induced performance deficits were ameliorated when the activity of vmOFC glutamate cells was inhibited (halorhodopsin).

Conclusions

Postnatal PCP-induced deficits in decision making are associated with hyperactivity of vmOFC glutamate cells. Thus, normalizing vmOFC activity may represent a potential therapeutic target for decision-making deficits in patients with schizophrenia.

Reinforcement learning and working memory in mood disorders: A computational analysis in a developmental transdiagnostic sample

BACKGROUND

Mood disorders commonly onset during adolescence and young adulthood and are conceptually and empirically related to reinforcement learning abnormalities. However, the nature of abnormalities associated with acute symptom severity versus lifetime diagnosis remains unclear, and prior research has often failed to disentangle working memory from reward processes.

METHODS

The present sample (N = 220) included adolescents and young adults with a lifetime history of unipolar disorders (n = 127), bipolar disorders (n = 28), or no history of psychopathology (n = 62), and varying severity of mood symptoms. Analyses fitted a reinforcement learning and working memory model to an instrumental learning task that varied working memory load, and tested associations between model parameters and diagnoses or current symptoms.

RESULTS

Current severity of manic or anhedonic symptoms negatively correlated with task performance. Participants reporting higher severity of current anhedonia, or with lifetime unipolar or bipolar disorders, showed lower reward learning rates. Participants reporting higher severity of current manic symptoms showed faster working memory decay and reduced use of working memory.

LIMITATIONS

Computational parameters should be interpreted in the task environment (a deterministic reward learning paradigm), and developmental population. Future work should test replication in other paradigms and populations.

CONCLUSIONS

Results indicate abnormalities in reinforcement learning processes that either scale with current symptom severity, or correspond with lifetime mood diagnoses. Findings may have implications for understanding reward processing anomalies related to state-like (current symptom) or trait-like (lifetime diagnosis) aspects of mood disorders.

Examining social reinforcement learning in social anxiety

BACKGROUND AND OBJECTIVES

Reinforcement learning biases have been empirically linked to anhedonia in depression and theoretically linked to social anhedonia in social anxiety disorder, but little work has directly assessed how socially anxious individuals learn from social reward and punishment.

METHODS

N = 157 individuals high and low in social anxiety symptoms completed a social probabilistic selection task that involved selecting between pairs of neutral faces with varying probabilities of changing to a happy or angry face. Computational modeling was performed to estimate learning rates. Accuracy in choosing the more rewarding face was also analyzed.

RESULTS

No significant group differences were found for learning rates. Contrary to hypotheses, participants high in social anxiety showed impaired punishment learning accuracy; they were more accurate at choosing the most rewarding face than they were at avoiding the most punishing face, and their punishment learning accuracy was lower than that of participants low in social anxiety. Secondary analyses found that high (vs. low) social anxiety participants were less accurate at selecting the more rewarding face on more (vs. less) punishing face pairs.

LIMITATIONS

Stimuli were static, White, facial images, which lack important social cues (e.g., movement, sound) and diversity, and participants were largely non-Hispanic, White undergraduates, whose social reinforcement learning may differ from individuals at different developmental stages and those holding more marginalized identities.

CONCLUSIONS

Socially anxious individuals may be less accurate at learning to avoid social punishment, which may maintain negative beliefs through an interpersonal stress generation process.

Adaptive Design Optimization as a Promising Tool for Reliable and Efficient Computational Fingerprinting

A key challenge in understanding mental (dys)functions is their etiological and functional heterogeneity, and several multidimensional assessments have been proposed for their comprehensive characterization. However, such assessments require lengthy testing, which may hinder reliable and efficient characterization of individual differences due to increased fatigue and distraction, especially in clinical populations. Computational modeling may address this challenge as it often provides more reliable measures of latent neurocognitive processes underlying observed behaviors and captures individual differences better than traditional assessments. However, even with a state-of-the-art hierarchical modeling approach, reliable estimation of model parameters still requires a large number of trials. Recent work suggests that Bayesian adaptive design optimization (ADO) is a promising way to address these challenges. With ADO, experimental design is optimized adaptively from trial to trial to extract the maximum amount of information about an individual's characteristics. In this review, we first describe the ADO methodology and then summarize recent work demonstrating that ADO increases the reliability and efficiency of latent neurocognitive measures. We conclude by discussing the challenges and future directions of ADO and proposing development of ADO-based computational fingerprints to reliably and efficiently characterize the heterogeneous profiles of psychiatric disorders.

Anxiety and depression related abnormalities in socio-affective learning

Affective distress (as observed in anxiety and depression) has been observed to be related to insufficient sensitivity to changing reinforcement during operant learning. Whether such findings are specific to anxiety or depression is unclear given a wider literature relating negative affect to abnormal learning and the possibility that relationships are not consistent across incentive types (i.e., punishment and reward) and outcomes (i.e., positive or negative). In two separate samples (n = 100; n = 88), participants completed an operant learning task with positive or negative, and neutral socio-affective feedback, designed to assess adaptive responses to changing environmental volatility. Individual parameter estimates were generated with hierarchical Bayesian modelling. Effects of manipulations were modelled by decomposing parameters into a linear combination of effects on the logit scale. While effects tended to support prior work, neither general affective distress nor anxiety or depression were consistently related to a decrease in the adaptive adjustment of learning-rates in response to changing environmental volatility (Sample 1: β_{α:volatility} = -0.01, 95 % HDI = -0.14, 0.13; Sample 2: β_{α:volatility} = -0.15, 95 % HDI = -0.37, 0.05). Interaction effects in Sample 1 suggested that while distress was associated with decrements in adaptive learning under punishment-maximisation, it was associated with improvements under reward-maximisation. While our results are broadly consistent with prior work, they suggest that the role of anxiety or depression in volatility learning, if present, is subtle and difficult to detect. Inconsistencies between our samples, along with issues of parameter identifiability complicated interpretation.

Reduced sensitivity but intact motivation to monetary rewards and reversal learning in obesity

BACKGROUND

Obesity has been linked to altered reward processing but little is known about which components of reward processing including motivation, sensitivity and learning are impaired in obesity. We examined whether obesity compared to healthy weight controls is associated with differences in distinct subdomains of reward processing. To this end, we used two established paradigms, namely the Effort Expenditure for Rewards task (EEfRT) and the Probabilistic Reversal Learning Task (PRLT).

METHODS

30 individuals with obesity (OBS) and 30 healthy weight control subjects (HC) were included in the study. Generalized estimating equation models were used to analyze EEfRT choice behavior. PRLT data was analyzed using both conventional behavioral variables of choices and computational models.

RESULTS

Our findings from the different tasks speak in favor of a hyposensitivity to non-food rewards in obesity. OBS did not make fewer overall hard task selections compared to HC in the EEfRT suggesting generally intact non-food reward motivation. However, in highly rewarding trials (i.e.,trials with high reward magnitude and high reward probability),OBSmadefewer hard task selections compared to normal weight subjects suggesting decreased sensitivity to highly rewarding non-food reinforcers. Hyposensitivity to non-food rewards was also evident in OBS in the PRLT as evidenced by lower win-stay probability compared to HC. Our computational modelling analyses revealed decreased stochasticity but intact reward and punishment learning rates in OBS.

CONCLUSIONS

Our findings provide evidence for intact reward motivation and learning in OBS but lower reward sensitivity which is linked to stochasticity of choices in a non-food context. These findings might provide further insight into the mechanism underlying dysfunctional choices in obesity.

A computational analysis of mouse behavior in the sucrose preference test

The sucrose preference test (SPT) measures the relative preference of sucrose over water to assess hedonic behaviors in rodents. Yet, it remains uncertain to what extent the SPT reflects other behavioral components, such as learning, memory, motivation, and choice. Here, we conducted an experimental and computational decomposition of mouse behavior in the SPT and discovered previously unrecognized behavioral subcomponents associated with changes in sucrose preference. We show that acute and chronic stress have sex-dependent effects on sucrose preference, but anhedonia was observed only in response to chronic stress in male mice. Additionally, reduced sucrose preference induced by optogenetics is not always indicative of anhedonia but can also reflect learning deficits. Even small variations in experimental conditions influence behavior, task outcome and interpretation. Thus, an ostensibly simple behavioral task can entail high levels of complexity, demonstrating the need for careful dissection of behavior into its subcomponents when studying the underlying neurobiology.

Anhedonia and sensitivity to punishment in schizophrenia, depression and opiate use disorder

BACKGROUND

From a behavioural perspective anhedonia is defined as diminished interest in the engagement of pleasurable activities. Despite its presence across a range of psychiatric disorders, the cognitive processes that give rise to anhedonia remain unclear.

METHODS

Here we examine whether anhedonia is associated with learning from positive and negative outcomes in patients diagnosed with major depression, schizophrenia and opiate use disorder alongside a healthy control group. Responses in the Wisconsin Card Sorting Test - a task associated with healthy prefrontal cortex function - were fitted to the Attentional Learning Model (ALM) which separates learning from positive and negative feedback.

RESULTS

Learning from punishment, but not from reward, was negatively associated with anhedonia beyond other socio-demographic, cognitive and clinical variables. This impairment in punishment sensitivity was also associated with faster responses following negative feedback, independently of the degree of surprise.

LIMITATIONS

Future studies should test the longitudinal association between punishment sensitivity and anhedonia also in other clinical populations controlling for the effect of specific medications.

CONCLUSIONS

Together the results reveal that anhedonic subjects, because of their negative expectations, are less sensitive to negative feedbacks; this might lead them to persist in actions leading to negative outcomes.

Individuals with problem gambling and obsessive-compulsive disorder learn through distinct reinforcement mechanisms

Obsessive-compulsive disorder (OCD) and pathological gambling (PG) are accompanied by deficits in behavioural flexibility. In reinforcement learning, this inflexibility can reflect asymmetric learning from outcomes above and below expectations. In alternative frameworks, it reflects perseveration independent of learning. Here, we examine evidence for asymmetric reward-learning in OCD and PG by leveraging model-based functional magnetic resonance imaging (fMRI). Compared with healthy controls (HC), OCD patients exhibited a lower learning rate for worse-than-expected outcomes, which was associated with the attenuated encoding of negative reward prediction errors in the dorsomedial prefrontal cortex and the dorsal striatum. PG patients showed higher and lower learning rates for better- and worse-than-expected outcomes, respectively, accompanied by higher encoding of positive reward prediction errors in the anterior insula than HC. Perseveration did not differ considerably between the patient groups and HC. These findings elucidate the neural computations of reward-learning that are altered in OCD and PG, providing a potential account of behavioural inflexibility in those mental disorders.

Mood Symptom Dimensions and Developmental Differences in Neurocognition in Adolescence

Adolescence is critical period of neurocognitive development as well as increased prevalence of mood pathology. This cross-sectional study replicated developmental patterns of neurocognition and tested whether mood symptoms moderated developmental effects. Participants were 419 adolescents (n=246 with current mood disorders) who completed reward learning and executive functioning tasks, and reported on age, puberty, and mood symptoms. Structural equation modeling revealed a quadratic relationship between puberty and reward learning performance that was moderated by symptom severity: in early puberty, adolescents reporting higher manic symptoms exhibited heightened reward learning performance (better maximizing of rewards on learning tasks), whereas adolescents reporting elevated anhedonia showed blunted reward learning performance. Models also showed a linear relationship between age and executive functioning that was moderated by manic symptoms: adolescents reporting higher mania showed poorer executive functioning at older ages. Findings suggest neurocognitive development is altered in adolescents with mood pathology and suggest directions for longitudinal studies.

Pavlovian-to-Instrumental Transfer across Mental Disorders: A Review

A mechanism known as Pavlovian-to-instrumental transfer (PIT) describes a phenomenon by which the values of environmental cues acquired through Pavlovian conditioning can motivate instrumental behavior. PIT may be one basic mechanism of action control that can characterize mental disorders on a dimensional level beyond current classification systems. Therefore, we review human PIT studies investigating subclinical and clinical mental syndromes. The literature prevails an inhomogeneous picture concerning PIT. While enhanced PIT effects seem to be present in non-substance-related disorders, overweight people, and most studies with AUD patients, no altered PIT effects were reported in tobacco use disorder and obesity. Regarding AUD and relapsing alcohol-dependent patients, there is mixed evidence of enhanced or no PIT effects. Additionally, there is evidence for aberrant corticostriatal activation and genetic risk, e.g., in association with high-risk alcohol consumption and relapse after alcohol detoxification. In patients with anorexia nervosa, stronger PIT effects elicited by low caloric stimuli were associated with increased disease severity. In patients with depression, enhanced aversive PIT effects and a loss of action-specificity associated with poorer treatment outcomes were reported. Schizophrenic patients showed disrupted specific but intact general PIT effects. Patients with chronic back pain showed reduced PIT effects. We provide possible reasons to understand heterogeneity in PIT effects within and across mental disorders. Further, we strengthen the importance of reliable experimental tasks and provide test-retest data of a PIT task showing moderate to good reliability. Finally, we point toward stress as a possible underlying factor that may explain stronger PIT effects in mental disorders, as there is some evidence that stress per se interacts with the impact of environmental cues on behavior by selectively increasing cue-triggered wanting. To conclude, we discuss the results of the literature review in the light of Research Domain Criteria, suggesting future studies that comprehensively assess PIT across psychopathological dimensions.

Computational approaches to treatment response prediction in major depression using brain activity and behavioral data: A systematic review

Major depressive disorder is a heterogeneous diagnostic category with multiple available treatments. With the goal of optimizing treatment selection, researchers are developing computational models that attempt to predict treatment response based on various pretreatment measures. In this paper, we review studies that use brain activity data to predict treatment response. Our aim is to highlight and clarify important methodological differences between various studies that relate to the incorporation of domain knowledge, specifically within two approaches delineated as data-driven and theory-driven. We argue that theory-driven generative modeling, which explicitly models information processing in the brain and thus can capture disease mechanisms, is a promising emerging approach that is only beginning to be utilized in treatment response prediction. The predictors extracted via such models could improve interpretability, which is critical for clinical decision-making. We also identify several methodological limitations across the reviewed studies and provide suggestions for addressing them. Namely, we consider problems with dichotomizing treatment outcomes, the importance of investigating more than one treatment in a given study for differential treatment response predictions, the need for a patient-centered approach for defining treatment outcomes, and finally, the use of internal and external validation methods for improving model generalizability.

Reinforcement Learning in Patients With Mood and Anxiety Disorders vs Control Individuals: A Systematic Review and Meta-analysis

IMPORTANCE

Computational psychiatry studies have investigated how reinforcement learning may be different in individuals with mood and anxiety disorders compared with control individuals, but results are inconsistent.

OBJECTIVE

To assess whether there are consistent differences in reinforcement-learning parameters between patients with depression or anxiety and control individuals.

DATA SOURCES

Web of Knowledge, PubMed, Embase, and Google Scholar searches were performed between November 15, 2019, and December 6, 2019, and repeated on December 3, 2020, and February 23, 2021, with keywords (reinforcement learning) AND (computational OR model) AND (depression OR anxiety OR mood).

STUDY SELECTION

Studies were included if they fit reinforcement-learning models to human choice data from a cognitive task with rewards or punishments, had a case-control design including participants with mood and/or anxiety disorders and healthy control individuals, and included sufficient information about all parameters in the models.

DATA EXTRACTION AND SYNTHESIS

Articles were assessed for inclusion according to MOOSE guidelines. Participant-level parameters were extracted from included articles, and a conventional meta-analysis was performed using a random-effects model. Subsequently, these parameters were used to simulate choice performance for each participant on benchmarking tasks in a simulation meta-analysis. Models were fitted, parameters were extracted using bayesian model averaging, and differences between patients and control individuals were examined. Overall effect sizes across analytic strategies were inspected.

MAIN OUTCOMES AND MEASURES

The primary outcomes were estimated reinforcement-learning parameters (learning rate, inverse temperature, reward learning rate, and punishment learning rate).

RESULTS

A total of 27 articles were included (3085 participants, 1242 of whom had depression and/or anxiety). In the conventional meta-analysis, patients showed lower inverse temperature than control individuals (standardized mean difference [SMD], -0.215; 95% CI, -0.354 to -0.077), although no parameters were common across all studies, limiting the ability to infer differences. In the simulation meta-analysis, patients showed greater punishment learning rates (SMD, 0.107; 95% CI, 0.107 to 0.108) and slightly lower reward learning rates (SMD, -0.021; 95% CI, -0.022 to -0.020) relative to control individuals. The simulation meta-analysis showed no meaningful difference in inverse temperature between patients and control individuals (SMD, 0.003; 95% CI, 0.002 to 0.004).

CONCLUSIONS AND RELEVANCE

The simulation meta-analytic approach introduced in this article for inferring meta-group differences from heterogeneous computational psychiatry studies indicated elevated punishment learning rates in patients compared with control individuals. This difference may promote and uphold negative affective bias symptoms and hence constitute a potential mechanistic treatment target for mood and anxiety disorders.

Impulsivity and Emotional Dysregulation Predict Choice Behavior During a Mixed-Strategy Game in Adolescents With Borderline Personality Disorder

Impulsivity and emotional dysregulation are two core features of borderline personality disorder (BPD), and the neural mechanisms recruited during mixed-strategy interactions overlap with frontolimbic networks that have been implicated in BPD. We investigated strategic choice patterns during the classic two-player game, Matching Pennies, where the most efficient strategy is to choose each option randomly from trial-to-trial to avoid exploitation by one’s opponent. Twenty-seven female adolescents with BPD (mean age: 16 years) and twenty-seven age-matched female controls (mean age: 16 years) participated in an experiment that explored the relationship between strategic choice behavior and impulsivity in both groups and emotional dysregulation in BPD. Relative to controls, BPD participants showed marginally fewer reinforcement learning biases, particularly decreased lose-shift biases, increased variability in reaction times (coefficient of variation; CV), and a greater percentage of anticipatory decisions. A subset of BPD participants with high levels of impulsivity showed higher overall reward rates, and greater modulation of reaction times by outcome, particularly following loss trials, relative to control and BPD participants with lower levels of impulsivity. Additionally, BPD participants with higher levels of emotional dysregulation showed marginally increased reward rate and increased entropy in choice patterns. Together, our preliminary results suggest that impulsivity and emotional dysregulation may contribute to variability in mixed-strategy decision-making in female adolescents with BPD.

Pleasure, Reward Value, Prediction Error and Anhedonia

In order to develop effective treatments for anhedonia we need to understand its underlying neurobiological mechanisms. Anhedonia is conceptually strongly linked to reward processing, which involves a variety of cognitive and neural operations. This chapter reviews the evidence for impairments in experiencing hedonic response (pleasure), reward valuation and reward learning based on outcomes (commonly conceptualised in terms of "reward prediction error"). Synthesising behavioural and neuroimaging findings, we examine case-control studies of patients with depression and schizophrenia, including those focusing specifically on anhedonia. Overall, there is reliable evidence that depression and schizophrenia are associated with disrupted reward processing. In contrast to the historical definition of anhedonia, there is surprisingly limited evidence for impairment in the ability to experience pleasure in depression and schizophrenia. There is some evidence that learning about reward and reward prediction error signals are impaired in depression and schizophrenia, but the literature is inconsistent. The strongest evidence is for impairments in the representation of reward value and how this is used to guide action. Future studies would benefit from focusing on impairments in reward processing specifically in anhedonic samples, including transdiagnostically, and from using designs separating different components of reward processing, formulating them in computational terms, and moving beyond cross-sectional designs to provide an assessment of causality.

The interpretation of computational model parameters depends on the context

Reinforcement Learning (RL) models have revolutionized the cognitive and brain sciences, promising to explain behavior from simple conditioning to complex problem solving, to shed light on developmental and individual differences, and to anchor cognitive processes in specific brain mechanisms. However, the RL literature increasingly reveals contradictory results, which might cast doubt on these claims. We hypothesized that many contradictions arise from two commonly-held assumptions about computational model parameters that are actually often invalid: That parameters generalize between contexts (e.g. tasks, models) and that they capture interpretable (i.e. unique, distinctive) neurocognitive processes. To test this, we asked 291 participants aged 8–30 years to complete three learning tasks in one experimental session, and fitted RL models to each. We found that some parameters (exploration / decision noise) showed significant generalization: they followed similar developmental trajectories, and were reciprocally predictive between tasks. Still, generalization was significantly below the methodological ceiling. Furthermore, other parameters (learning rates, forgetting) did not show evidence of generalization, and sometimes even opposite developmental trajectories. Interpretability was low for all parameters. We conclude that the systematic study of context factors (e.g. reward stochasticity; task volatility) will be necessary to enhance the generalizability and interpretability of computational cognitive models.

Challenging the negative learning bias hypothesis of depression: reversal learning in a naturalistic psychiatric sample

BACKGROUND

Classic theories posit that depression is driven by a negative learning bias. Most studies supporting this proposition used small and selected samples, excluding patients with comorbidities. However, comorbidity between psychiatric disorders occurs in up to 70% of the population. Therefore, the generalizability of the negative bias hypothesis to a naturalistic psychiatric sample as well as the specificity of the bias to depression, remain unclear. In the present study, we tested the negative learning bias hypothesis in a large naturalistic sample of psychiatric patients, including depression, anxiety, addiction, attention-deficit/hyperactivity disorder, and/or autism. First, we assessed whether the negative bias hypothesis of depression generalized to a heterogeneous (and hence more naturalistic) depression sample compared with controls. Second, we assessed whether negative bias extends to other psychiatric disorders. Third, we adopted a dimensional approach, by using symptom severity as a way to assess associations across the sample.

METHODS

We administered a probabilistic reversal learning task to 217 patients and 81 healthy controls. According to the negative bias hypothesis, participants with depression should exhibit enhanced learning and flexibility based on punishment v. reward. We combined analyses of traditional measures with more sensitive computational modeling.

RESULTS

In contrast to previous findings, this sample of depressed patients with psychiatric comorbidities did not show a negative learning bias.

CONCLUSIONS

These results speak against the generalizability of the negative learning bias hypothesis to depressed patients with comorbidities. This study highlights the importance of investigating unselected samples of psychiatric patients, which represent the vast majority of the psychiatric population.

A Computational View on the Nature of Reward and Value in Anhedonia

Anhedonia - a common feature of depression and other neuropsychiatric disorders - encompasses a reduction in the subjective experience and anticipation of rewarding events, and a reduction in the motivation to seek out such events. The presence of anhedonia often predicts or accompanies treatment resistance, and as such better interventions and treatments are important. Yet the mechanisms giving rise to anhedonia are not well understood. In this chapter, we briefly review existing computational conceptualisations of anhedonia. We argue that they are mostly descriptive and fail to provide an explanatory account of why anhedonia may occur. Working within the framework of reinforcement learning, we examine two potential computational mechanisms that could give rise to anhedonic phenomena. First, we show how anhedonia can arise in multi-dimensional drive-reduction settings through a trade-off between different rewards or needs. We then generalise this in terms of model-based value inference and identify a key role for associational belief structure. We close with a brief discussion of treatment implications of both of these conceptualisations. In summary, computational accounts of anhedonia have provided a useful descriptive framework. Recent advances in reinforcement learning suggest promising avenues by which the mechanisms underlying anhedonia may be teased apart, potentially motivating novel approaches to treatment.

[Belief updating and mood congruence in depressive disorder]

Depressive disorder is characterized by a polymorphic symptomatology associating emotional, cognitive and behavioral disturbances. One of the most specific symptoms is negative beliefs, called congruent to mood. Despite the importance of these beliefs in the development, the maintenance, and the recurrence of depressive episodes, little is known about the processes underlying the generation of depressive beliefs. In this paper, we detail the link between belief updating mechanisms and the genesis of depressive beliefs. We show how depression alters information processing, generating cognitive immunization when processing positive information, affective updating bias related to the valence of belief and prediction error, and difficultie to disengage from negative information. We suggest that disruption of belief-updating mechanisms forms the basis of belief-mood congruence in depression.

Investigation of reward learning and feedback sensitivity in non-clinical participants with a history of early life stress

Early life stress (ELS) is an important risk factor for the development of depression. Impairments in reward learning and feedback sensitivity are suggested to be an intermediate phenotype in depression aetiology therefore we hypothesised that healthy adults with a history of ELS would exhibit reward processing deficits independent of any current depressive symptoms. We recruited 64 adults with high levels of ELS and no diagnosis of a current mental health disorder and 65 controls. Participants completed the probabilistic reversal learning task and probabilistic reward task followed by depression, anhedonia, social status, and stress scales. Participants with high levels of ELS showed decreased positive feedback sensitivity in the probabilistic reversal learning task compared to controls. High ELS participants also trended towards possessing a decreased model-free learning rate. This was coupled with a decreased learning ability in the acquisition phase of block 1 following the practice session. Neither group showed a reward induced response bias in the probabilistic reward task however high ELS participants exhibited decreased stimuli discrimination. Overall, these data suggest that healthy participants without a current mental health diagnosis but with high levels of ELS show deficits in positive feedback sensitivity and reward learning in the probabilistic reversal learning task that are distinct from depressed patients. These deficits may be relevant to increased depression vulnerability.

Reinforcement Learning Disruptions in Individuals With Depression and Sensitivity to Symptom Change Following Cognitive Behavioral Therapy

IMPORTANCE

Major depressive disorder is prevalent and impairing. Parsing neurocomputational substrates of reinforcement learning in individuals with depression may facilitate a mechanistic understanding of the disorder and suggest new cognitive therapeutic targets.

OBJECTIVE

To determine associations among computational model-derived reinforcement learning parameters, depression symptoms, and symptom changes after treatment.

DESIGN, SETTING, AND PARTICIPANTS

In this mixed cross-sectional-cohort study, individuals performed reward and loss variants of a probabilistic learning task during functional magnetic resonance imaging at baseline and follow-up. A volunteer sample with and without a depression diagnosis was recruited from the community. Participants were assessed from July 2011 to February 2017, and data were analyzed from May 2017 to May 2021.

MAIN OUTCOMES AND MEASURES

Computational model-based analyses of participants' choices assessed a priori hypotheses about associations between components of reward-based and loss-based learning with depression symptoms. Changes in both learning parameters and symptoms were then assessed in a subset of participants who received cognitive behavioral therapy (CBT).

RESULTS

Of 101 included adults, 69 (68.3%) were female, and the mean (SD) age was 34.4 (11.2) years. A total of 69 participants with a depression diagnosis and 32 participants without a depression diagnosis were included at baseline; 48 participants (28 with depression who received CBT and 20 without depression) were included at follow-up (mean [SD] of 115.1 [15.6] days). Computational model-based analyses of behavioral choices and neural data identified associations of learning with symptoms during reward learning and loss learning, respectively. During reward learning only, anhedonia (and not negative affect or arousal) was associated with model-derived learning parameters (learning rate: posterior mean regression β = -0.14; 95% credible interval [CrI], -0.12 to -0.03; outcome sensitivity: posterior mean regression β = 0.18; 95% CrI, 0.02 to 0.37) and neural learning signals (moderation of association between striatal prediction error and expected value signals: t97 = -2.10; P = .04). During loss learning only, negative affect (and not anhedonia or arousal) was associated with learning parameters (outcome shift: posterior mean regression β = -0.11; 95% CrI, -0.20 to -0.01) and disrupted neural encoding of learning signals (association with subgenual anterior cingulate prediction error signals: r = -0.28; P = .005). Symptom improvement following CBT was associated with normalization of learning parameters that were disrupted at baseline (reward learning rate: posterior mean regression β = 0.15; 90% CrI, 0.001 to 0.41; loss outcome shift: posterior mean regression β = 0.42; 90% CrI, 0.09 to 0.77).

CONCLUSIONS AND RELEVANCE

In this study, the mapping of reinforcement learning components to symptoms of major depression revealed mechanistic features associated with these symptoms and points to possible learning-based therapeutic processes and targets.

Psychiatric symptoms influence reward-seeking and loss-avoidance decision-making through common and distinct computational processes

AIM

Psychiatric symptoms are often accompanied by impairments in decision-making to attain rewards and avoid losses. However, due to the complex nature of mental disorders (e.g., high comorbidity), symptoms that are specifically associated with deficits in decision-making remain unidentified. Furthermore, the influence of psychiatric symptoms on computations underpinning reward-seeking and loss-avoidance decision-making remains elusive. Here, we aim to address these issues by leveraging a large-scale online experiment and computational modeling.

METHODS

In the online experiment, we recruited 1900 non-diagnostic participants from the general population. They performed either a reward-seeking or loss-avoidance decision-making task, and subsequently completed questionnaires about psychiatric symptoms.

RESULTS

We found that one trans-diagnostic dimension of psychiatric symptoms related to compulsive behavior and intrusive thought (CIT) was negatively correlated with overall decision-making performance in both the reward-seeking and loss-avoidance tasks. A deeper analysis further revealed that, in both tasks, the CIT psychiatric dimension was associated with lower preference for the options that recently led to better outcomes (i.e. reward or no-loss). On the other hand, in the reward-seeking task only, the CIT dimension was associated with lower preference for recently unchosen options.

CONCLUSION

These findings suggest that psychiatric symptoms influence the two types of decision-making, reward-seeking and loss-avoidance, through both common and distinct computational processes.

Affective Bias Through the Lens of Signal Detection Theory

Affective bias – a propensity to focus on negative information at the expense of positive information – is a core feature of many mental health problems. However, it can be caused by wide range of possible underlying cognitive mechanisms. Here we illustrate this by focusing on one particular behavioural signature of affective bias – increased tendency of anxious/depressed individuals to predict lower rewards – in the context of the Signal Detection Theory (SDT) modelling framework. Specifically, we show how to apply this framework to measure affective bias and compare it to the behaviour of an optimal observer. We also show how to extend the framework to make predictions about bias when the individual holds incorrect assumptions about the decision context. Building on this theoretical foundation, we propose five experiments to test five hypothetical sources of this affective bias: beliefs about prior probabilities, beliefs about performance, subjective value of reward, learning differences, and need for accuracy differences. We argue that greater precision about the mechanisms driving affective bias may eventually enable us to better understand the mechanisms underlying mood and anxiety disorders.

Sex difference in the weighting of expected uncertainty under chronic stress

The neurobiological literature implicates chronic stress induced decision-making deficits as a major contributor to depression and anxiety. Given that females are twice as likely to suffer from these disorders, we hypothesized the existence of sex difference in the effects of chronic stress on decision-making. Here employing a decision-making paradigm that relies on reinforcement learning of probabilistic predictive relationships, we show female volunteers with a high level of perceived stress in the past month are more likely to make suboptimal choices than males. Computational characterizations of this sex difference suggest that while under high stress, females and males differ in their weighting but not learning of the expected uncertainty in the predictive relationships. These findings provide a mechanistic account of the sex difference in decision-making under chronic stress and may have important implications for the epidemiology of sex difference in depression and anxiety.

Revisiting the importance of model fitting for model-based fMRI: It does matter in computational psychiatry

Computational modeling has been applied for data analysis in psychology, neuroscience, and psychiatry. One of its important uses is to infer the latent variables underlying behavior by which researchers can evaluate corresponding neural, physiological, or behavioral measures. This feature is especially crucial for computational psychiatry, in which altered computational processes underlying mental disorders are of interest. For instance, several studies employing model-based fMRI-a method for identifying brain regions correlated with latent variables-have shown that patients with mental disorders (e.g., depression) exhibit diminished neural responses to reward prediction errors (RPEs), which are the differences between experienced and predicted rewards. Such model-based analysis has the drawback that the parameter estimates and inference of latent variables are not necessarily correct-rather, they usually contain some errors. A previous study theoretically and empirically showed that the error in model-fitting does not necessarily cause a serious error in model-based fMRI. However, the study did not deal with certain situations relevant to psychiatry, such as group comparisons between patients and healthy controls. We developed a theoretical framework to explore such situations. We demonstrate that the parameter-misspecification can critically affect the results of group comparison. We demonstrate that even if the RPE response in patients is completely intact, a spurious difference to healthy controls is observable. Such a situation occurs when the ground-truth learning rate differs between groups but a common learning rate is used, as per previous studies. Furthermore, even if the parameters are appropriately fitted to individual participants, spurious group differences in RPE responses are observable when the model lacks a component that differs between groups. These results highlight the importance of appropriate model-fitting and the need for caution when interpreting the results of model-based fMRI.

Impaired adaptation of learning to contingency volatility in internalizing psychopathology

Using a contingency volatility manipulation, we tested the hypothesis that difficulty adapting probabilistic decision-making to second-order uncertainty might reflect a core deficit that cuts across anxiety and depression and holds regardless of whether outcomes are aversive or involve reward gain or loss. We used bifactor modeling of internalizing symptoms to separate symptom variance common to both anxiety and depression from that unique to each. Across two experiments, we modeled performance on a probabilistic decision-making under volatility task using a hierarchical Bayesian framework. Elevated scores on the common internalizing factor, with high loadings across anxiety and depression items, were linked to impoverished adjustment of learning to volatility regardless of whether outcomes involved reward gain, electrical stimulation, or reward loss. In particular, high common factor scores were linked to dampened learning following better-than-expected outcomes in volatile environments. No such relationships were observed for anxiety- or depression-specific symptom factors.

Negative Learning Bias in Depression Revisited: Enhanced Neural Response to Surprising Reward Across Psychiatric Disorders

BACKGROUND

Prior work has proposed that major depressive disorder (MDD) is associated with a specific cognitive bias: patients with depression seem to learn more from punishment than from reward. This learning bias has been associated with blunting of reward-related neural responses in the striatum. A key question is whether negative learning bias is also present in patients with MDD and comorbid disorders and whether this bias is specific to depression or shared across disorders.

METHODS

We employed a transdiagnostic approach assessing a heterogeneous group of (nonpsychotic) psychiatric patients from the MIND-Set (Measuring Integrated Novel Dimensions in Neurodevelopmental and Stress-Related Mental Disorders) cohort with and without MDD but also with anxiety, attention-deficit/hyperactivity disorder, and/or autism (n = 66) and healthy control subjects (n = 24). To investigate reward and punishment learning, we employed a deterministic reversal learning task with functional magnetic resonance imaging.

RESULTS

In contrast to previous studies, patients with MDD did not exhibit impaired reward learning or reduced reward-related neural activity anywhere in the brain. Interestingly, we observed consistently increased neural responses in the bilateral lateral prefrontal cortex of patients when they received a surprising reward. This increase was not specific to MDD, but generalized to anxiety, attention-deficit/hyperactivity disorder, and autism. Critically, increased prefrontal activity to surprising reward scaled with transdiagnostic symptom severity, particularly that associated with concentration and attention, as well as the number of diagnoses; patients with more comorbidities showed a stronger prefrontal response to surprising reward.

CONCLUSIONS

Prefrontal enhancement may reflect compensatory working memory recruitment, possibly to counteract the inability to swiftly update reward expectations. This neural mechanism may provide a candidate transdiagnostic index of psychiatric severity.

Associations between aversive learning processes and transdiagnostic psychiatric symptoms in a general population sample

Symptom expression in psychiatric conditions is often linked to altered threat perception, however how computational mechanisms that support aversive learning relate to specific psychiatric symptoms remains undetermined. We answer this question using an online game-based aversive learning task together with measures of common psychiatric symptoms in 400 subjects. We show that physiological symptoms of anxiety and a transdiagnostic compulsivity-related factor are associated with enhanced safety learning, as measured using a probabilistic computational model, while trait cognitive anxiety symptoms are associated with enhanced learning from danger. We use data-driven partial least squares regression to identify two separable components across behavioural and questionnaire data: one linking enhanced safety learning and lower estimated uncertainty to physiological anxiety, compulsivity, and impulsivity; the other linking enhanced threat learning and heightened uncertainty estimation to symptoms of depression and social anxiety. Our findings implicate aversive learning processes in the expression of psychiatric symptoms that transcend diagnostic boundaries.

Effects of Depressive Symptoms, Feelings, and Interoception on Reward-Based Decision-Making: Investigation Using Reinforcement Learning Model

Background: In this study, we examined the relationships between reward-based decision-making in terms of learning rate, memory rate, exploration rate, and depression-related subjective emotional experience, in terms of interoception and feelings, to understand how reward-based decision-making is impaired in depression. Methods: In all, 52 university students were randomly assigned to an experimental group and a control group. To manipulate interoception, the participants in the experimental group were instructed to tune their internal somatic sense to the skin-conductance-response waveform presented on a display. The participants in the control group were only instructed to stay relaxed. Before and after the manipulation, the participants completed a probabilistic reversal-learning task to assess reward-based decision-making using reinforcement learning modeling. Similarly, participants completed a probe-detection task, a heartbeat-detection task, and self-rated scales. Results: The experimental manipulation of interoception was not successful. In the baseline testing, reinforcement learning modeling indicated a marginally-significant correlation between the exploration rate and depressive symptoms. However, the exploration rate was significantly associated with lower interoceptive attention and higher depressive feeling. Conclusions: The findings suggest that situational characteristics may be closely involved in reward exploration and highlight the clinically-meaningful possibility that intervention for affective processes may impact reward-based decision-making in those with depression.

Under the Hood: Using Computational Psychiatry to Make Psychological Therapies More Mechanism-Focused

Psychological therapies, such as CBT, are an important part of the treatment of a range of psychiatric disorders such as depression and anxiety. There is a growing desire to understand the mechanisms by which such therapies effect change so as to improve treatment outcomes. Here we argue that adopting a computational framework may be one such approach. Computational psychiatry aims to provide a theoretical framework for moving between higher-level psychological states (like emotions, decisions and beliefs) to neural circuits, by modeling these constructs mathematically. These models are explicit hypotheses that contain quantifiable variables and parameters derived from each individual's behavior. This approach has two advantages. Firstly, some of the variables described by these models appears to reflect the neural activity of specific brain regions. Secondly, the parameters estimated by these models may offer a unique description of a patient's symptoms which can be used to both tailor therapy and track its effect. In doing so this approach may offer some additional granularity in understanding how psychological therapies, such as CBT, are working. Although this field shows significant promise, we also highlight several of the key hurdles that must first be overcome before clinical translation of computational insights can be realized.

Translating a rodent measure of negative bias into humans: the impact of induced anxiety and unmedicated mood and anxiety disorders

BACKGROUND

Mood and anxiety disorders are ubiquitous but current treatment options are ineffective for many sufferers. Moreover, a number of promising pre-clinical interventions have failed to translate into clinical efficacy in humans. Improved treatments are unlikely without better animal-human translational pipelines. Here, we translate a rodent measure of negative affective bias into humans, exploring its relationship with (1) pathological mood and anxiety symptoms and (2) transient induced anxiety.

METHODS

Adult participants (age = 29 ± 11) who met criteria for mood or anxiety disorder symptomatology according to a face-to-face neuropsychiatric interview were included in the symptomatic group. Study 1 included N = 77 (47 = asymptomatic [female = 21]; 30 = symptomatic [female = 25]), study 2 included N = 47 asymptomatic participants (25 = female). Outcome measures were choice ratios, reaction times and parameters recovered from a computational model of reaction time - the drift diffusion model (DDM) - from a two-alternative-forced-choice task in which ambiguous and unambiguous auditory stimuli were paired with high and low rewards.

RESULTS

Both groups showed over 93% accuracy on unambiguous tones indicating intact discrimination, but symptomatic individuals demonstrated increased negative affective bias on ambiguous tones [proportion high reward = 0.42 (s.d. = 0.14)] relative to asymptomatic individuals [0.53 (s.d. = 0.17)] as well as a significantly reduced DDM drift rate. No significant effects were observed for the within-subjects anxiety-induction.

CONCLUSIONS

Humans with pathological anxiety symptoms directly mimic rodents undergoing anxiogenic manipulation. The lack of sensitivity to transient anxiety suggests the paradigm might be more sensitive to clinically relevant symptoms. Our results establish a direct translational pipeline (and candidate therapeutics screen) from negative affective bias in rodents to pathological mood and anxiety symptoms in humans.

Modeling anxiety in healthy humans: a key intermediate bridge between basic and clinical sciences

Animal models of anxiety disorders are important for elucidating neurobiological defense mechanisms. However, animal models are limited when it comes to understanding the more complex processes of anxiety that are unique to humans (e.g., worry) and to screen new treatments. In this review, we outline how the Experimental Psychopathology approach, based on experimental models of anxiety in healthy subjects, can mitigate these limitations and complement research in animals. Experimental psychopathology can bridge basic research in animals and clinical studies, as well as guide and constrain hypotheses about the nature of psychopathology, treatment mechanisms, and treatment targets. This review begins with a brief review of the strengths and limitations of animal models before discussing the need for human models of anxiety, which are especially necessary to probe higher-order cognitive processes. This can be accomplished by combining anxiety-induction procedures with tasks that probe clinically relevant processes to identify neurocircuits that are potentially altered by anxiety. The review then discusses the validity of experimental psychopathology and introduces a methodological approach consisting of five steps: (1) select anxiety-relevant cognitive or behavioral operations and associated tasks, (2) identify the underlying neurocircuits supporting these operations in healthy controls, 3) examine the impact of experimental anxiety on the targeted operations in healthy controls, (4) utilize findings from step 3 to generate hypotheses about neurocircuit dysfunction in anxious patients, and 5) evaluate treatment mechanisms and screen novel treatments. This is followed by two concrete illustrations of this approach and suggestions for future studies.

Social anxiety and dynamic social reinforcement learning in a volatile environment

Adaptive social behavior requires learning probabilities of social reward and punishment, and updating these probabilities when they change. Given prior research on aberrant reinforcement learning in affective disorders, this study examines how social anxiety affects probabilistic social reinforcement learning and dynamic updating of learned probabilities in a volatile environment. N=222 online participants completed questionnaires and a computerized ball-catching game with changing probabilities of reward and punishment. Dynamic learning rates were estimated to assess the relative importance ascribed to new information in response to volatility. Mixed-effects regression was used to analyze throw patterns as a function of social anxiety symptoms. Higher social anxiety predicted fewer throws to the previously punishing avatar and different learning rates after certain role changes, suggesting that social anxiety may be characterized by difficulty updating learned social probabilities. Socially anxious individuals may miss the chance to learn that a once-punishing situation no longer poses a threat.

Assessing inter-individual differences with task-related functional neuroimaging

Explaining and predicting individual behavioural differences induced by clinical and social factors constitutes one of the most promising applications of neuroimaging. In this Perspective, we discuss the theoretical and statistical foundations of the analyses of inter-individual differences in task-related functional neuroimaging. Leveraging a five-year literature review (July 2013-2018), we show that researchers often assess how activations elicited by a variable of interest differ between individuals. We argue that the rationale for such analyses, typically grounded in resource theory, offers an over-large analytical and interpretational flexibility that undermines their validity. We also recall how, in the established framework of the general linear model, inter-individual differences in behaviour can act as hidden moderators and spuriously induce differences in activations. We conclude with a set of recommendations and directions, which we hope will contribute to improving the statistical validity and the neurobiological interpretability of inter-individual difference analyses in task-related functional neuroimaging.

The Misestimation of Uncertainty in Affective Disorders

Our knowledge about the state of the world is often incomplete, making it difficult to select the best course of action. One strategy that can be used to improve our ability to make decisions is to identify the causes of our ignorance (i.e., why an unexpected event might have occurred) and use estimates of the uncertainty induced by these causes to guide our learning. Here, we explain the logic behind this process and describe the evidence that human learners use estimates of uncertainty to sculpt their learning. Finally, we describe recent work suggesting that misestimation of uncertainty is involved in the development of anxiety and depression and describe how these ideas may be advanced.

Contrasting contributions of anhedonia to obsessive-compulsive, hoarding, and post-traumatic stress disorders

Anhedonia is a transdiagnostic construct that can occur independent of other symptoms of depression; its role in neuropsychiatric disorders that are not primarily affective, such as obsessive compulsive disorder (OCD), hoarding disorder (HD), and post-traumatic stress disorder (PTSD) has received limited attention. This paper addresses this gap. First, the data revealed a positive contribution of anhedonia, beyond the effects of general depression, to symptom severity in OCD but not in HD or PTSD. Second, anhedonia was operationalized as a reduced sensitivity to rewards, which allowed employing the value based decision making framework to investigate effects of anhedonia on reward-related behavioral outcomes, such as increased risk aversion and increased difficulty of making value-based choices. Both self-report and behavior-based measures were used to characterize individual risk aversion: risk perception and risk-taking propensities (measured using the Domain Specific Risk Taking scale) and risk attitudes evaluated using a gambling task. Data revealed the positive theoretically predicted correlation between anhedonia and risk perception in OCD; effects on self-reported risk taking and behavior-based risk aversion were non-significant. The same relations were weaker in HD and absent in PTSD. Response time during a gambling task, an index of difficulty of making value-based choices, significantly correlated with anhedonia in individuals with OCD and individuals with HD, even after controlling for general depression, but not in individuals with PTSD. The results suggest a unique contribution of one aspect of anhedonia in obsessive-compulsive disorder and confirm the importance of investigating the role of anhedonia transdiagnostically beyond affective and psychotic disorders.

Haste or Speed? Alterations in the Impact of Incentive Cues on Task Performance in Remitted and Depressed Patients With Bipolar Disorder

A variety of evidence suggests that bipolar disorder is associated with disruptions of reward related processes, although the properties, and scope of these changes are not well understood. In the present study, we aimed to address this question by examining performance of patients with bipolar disorder (30 depressed bipolar; 35 euthymic bipolar) on a motivated choice reaction time task. We compared performance with a group of healthy control individuals (n = 44) and a group of patients with unipolar depression (n = 41), who were matched on several demographic variables. The task consists of an "odd-one-out" discrimination, in the presence of a cue signaling the probability of reward on a given trial (10, 50, or 90%) given a sufficiently fast response. All groups showed similar reaction time (RT) performance, and similar shortening of RT following the presentation of a reward predictive cue. However, compared to healthy individuals, the euthymic bipolar group showed a relative increase in commission errors during the high reward compared to low condition. Further correlational analysis revealed that in the healthy control and unipolar depression groups, participants tended either to shorten RTs for the high rather than low reward cue a relatively large amount with an increase in error rate, or to shorten RTs to a lesser extent but without increasing errors to the same degree. By contrast, reward-related speeding and reward-related increase in errors were less well coupled in the bipolar groups, significantly so in the BPD group. These findings suggest that although RT performance on the present task is relatively well matched, there may be a specific failure of individuals with bipolar disorder to calibrate RT speed and accuracy in a strategic way in the presence of reward-related stimuli.