Depression is characterized by abnormalities in emotional processing, but the specific drivers of such emotional abnormalities are unknown. Computational work indicates that both surprising outcomes (prediction errors; PEs) and outcomes (values) themselves drive emotional responses, but neither has been consistently linked to affective disturbances in depression. As a result, the computational mechanisms driving emotional abnormalities in depression remain unknown.

Here, in 687 individuals, one-third of whom qualify as depressed via a standard self-report measure (the PHQ-9), we use high-stakes, naturalistic events – the reveal of midterm exam grades – to test whether individuals with heightened depression display a specific reduction in emotional response to positive PEs.

Using Bayesian mixed effects models, we find that individuals with heightened depression do not affectively benefit from surprising, good outcomes – that is, they display reduced affective responses to positive PEs. These results were highly specific: effects were not observed to negative PEs, value signals (grades), and were not related to generalized anxiety. This suggests that the computational drivers of abnormalities in emotion in depression may be specifically due to positive PE-based emotional responding.

Affective abnormalities are core depression symptoms, but the computational mechanisms underlying such differences are unknown. This work suggests that blunted affective reactions to positive PEs are likely mechanistic drivers of emotional dysregulation in depression.

Individuals with cocaine use disorder or gambling disorder demonstrate impairments in cognitive flexibility: the ability to adapt to changes in the environment. Flexibility is commonly assessed in a laboratory setting using probabilistic reversal learning, which involves reinforcement learning, the process by which feedback from the environment is used to adjust behavior.

It is poorly understood whether impairments in flexibility differ between individuals with cocaine use and gambling disorders, and how this is instantiated by the brain. We applied computational modelling methods to gain a deeper mechanistic explanation of the latent processes underlying cognitive flexibility across two disorders of compulsivity.

We present a re-analysis of probabilistic reversal data from individuals with either gambling disorder (n = 18) or cocaine use disorder (n = 20) and control participants (n = 18), using a hierarchical Bayesian approach. Furthermore, we relate behavioural findings to their underlying neural substrates through an analysis of task-based functional magnetic resonanceimaging (fMRI) data.

We observed lower ‘stimulus stickiness’ in gambling disorder, and report differences in tracking expected values in individuals with gambling disorder compared to controls, with greater activity during reward expected value tracking in the cingulate gyrus and amygdala. In cocaine use disorder, we observed lower responses to positive punishment prediction errors and greater activity following negative punishment prediction errors in the superior frontal gyrus compared to controls.

Using a computational approach, we show that individuals with gambling disorder and cocaine use disorder differed in their perseverative tendencies and in how they tracked value neurally, which has implications for psychiatric classification.

Anorexia nervosa (AN) is associated with altered sensitivity to reward and punishment. Few studies have investigated whether this results in aberrant learning. The ability to learn from rewarding and aversive experiences is essential for flexibly adapting to changing environments, yet individuals with AN tend to demonstrate cognitive inflexibility, difficulty set-shifting and altered decision-making. Deficient reinforcement learning may contribute to repeated engagement in maladaptive behavior.

This study investigated learning in AN using a probabilistic associative learning task that separated learning of stimuli via reward from learning via punishment. Forty-two individuals with Diagnostic and Statistical Manual of Mental Disorders (DSM)-5 restricting-type AN were compared to 38 healthy controls (HCs). We applied computational models of reinforcement learning to assess group differences in learning, thought to be driven by violations in expectations, or prediction errors (PEs). Linear regression analyses examined whether learning parameters predicted BMI at discharge.

AN had lower learning rates than HC following both positive and negative PE (p < .02), and were less likely to exploit what they had learned. Negative PE on punishment trials predicted lower discharge BMI (p < .001), suggesting individuals with more negative expectancies about avoiding punishment had the poorest outcome.

This is the first study to show lower rates of learning in AN following both positive and negative outcomes, with worse punishment learning predicting less weight gain. An inability to modify expectations about avoiding punishment might explain persistence of restricted eating despite negative consequences, and suggests that treatments that modify negative expectancy might be effective in reducing food avoidance in AN.

Depressive episodes experienced in unipolar (UD) and bipolar (BD) disorders are characterized by anhedonia and have been associated with abnormalities in reward processes related to reward valuation and error prediction. It remains however unclear whether these deficits are associated with familial vulnerability to mood disorders.

In a functional magnetic resonance imaging study, we evaluated differences in the expected value (EV) and reward prediction error (RPE) signals in ventral striatum (VS) and prefrontal cortex between three groups of monozygotic twins: affected twins in remission for either UD or BD (n = 53), their high-risk unaffected co-twins (n = 34), and low-risk twins with no family history of mood disorders (n = 25).

Compared to low-risk twins, affected twins showed lower EV signal bilaterally in the frontal poles and lower RPE signal bilaterally in the VS, left frontal pole and superior frontal gyrus. The high-risk group did not show a significant change in the EV or RPE signals in frontostriatal regions, yet both reward signals were consistently lower compared with low-risk twins in all regions where the affected twins showed significant reductions.

Our findings strengthen the notion that reduced valuation of expected rewards and reduced error-dependent reward learning may underpin core symptom of depression such as loss of interest in rewarding activities. The trend reduction in reward-related signals in unaffected co-twins warrants further investigation of this effect in larger samples and prospective follow-up to confirm possible association with increased familial vulnerability to mood disorders.

The significant proportion of schizophrenia patients refractory to treatment, primarily directed at the dopamine system, suggests that multiple mechanisms may underlie psychotic symptoms. Reinforcement learning tasks have been employed in schizophrenia to assess dopaminergic functioning and reward processing, but these have not directly compared groups of treatment-refractory and non-refractory patients.

In the current functional magnetic resonance imaging study, 21 patients with treatment-resistant schizophrenia (TRS), 21 patients with non-treatment-resistant schizophrenia (NTR), and 24 healthy controls (HC) performed a probabilistic reinforcement learning task, utilizing emotionally valenced face stimuli which elicit a social bias toward happy faces. Behavior was characterized with a reinforcement learning model. Trial-wise reward prediction error (RPE)-related neural activation and the differential impact of emotional bias on these reward signals were compared between groups.

Patients showed impaired reinforcement learning relative to controls, while all groups demonstrated an emotional bias favoring happy faces. The pattern of RPE signaling was similar in the HC and TRS groups, whereas NTR patients showed significant attenuation of RPE-related activation in striatal, thalamic, precentral, parietal, and cerebellar regions. TRS patients, but not NTR patients, showed a positive relationship between emotional bias and RPE signal during negative feedback in bilateral thalamus and caudate.

TRS can be dissociated from NTR on the basis of a different neural mechanism underlying reinforcement learning. The data support the hypothesis that a favorable response to antipsychotic treatment is contingent on dopaminergic dysfunction, characterized by aberrant RPE signaling, whereas treatment resistance may be characterized by an abnormality of a non-dopaminergic mechanism – a glutamatergic mechanism would be a possible candidate.

Addicts show both reward processing deficits and increased salience attribution to drug cues. However, no study to date has demonstrated that salience attribution to drug cues can directly modulate inferences of reward value to non-drug cues. Associative learning depends on salience: a more salient predictor of an outcome will ‘overshadow’ a less salient predictor of the same outcome. Similarly, blocking, a demonstration that learning depends on prediction error, can be influenced by the salience of the cues employed.

This study investigated whether salient drug cues might interact with neutral cues predicting financial reward in an associative learning task indexing blocking and overshadowing in satiated smokers (n=24), abstaining smokers (n=24) and non-smoking controls (n=24). Attentional bias towards drug cues, craving and expired CO were also indexed.

Abstaining smokers showed drug cue induced overshadowing, attributing higher reward value to drug cues than to neutral cues that were equally predictive of reward. Overshadowing was positively correlated with expired CO levels, which, in turn, were correlated with craving in abstainers. An automatic attentional bias towards cigarette cues was found in abstainers only.

These findings provide the first evidence that drug cues interact with reward processing in a drug dependent population.