Probabilistic Reward Learning Task Research Articles

Electrical brain activations in preadolescents during a probabilistic reward-learning task reflect cognitive processes and behavioral strategy.

Both adults and children learn through feedback which environmental events and choices are associated with higher probability of reward, an ability thought to be supported by the development of fronto-striatal reward circuits. Recent developmental studies have applied computational models of reward learning to investigate such learning in children. However, tasks and measures effective for assaying the cascade of reward-learning neural processes in children have been limited. Using a child-version of a probabilistic reward-learning task while recording event-related-potential (ERP) measures of electrical brain activity, this study examined key processes of reward learning in preadolescents (8-12 years old; n=30), namely: (1) reward-feedback sensitivity, as measured by the early-latency, reward-related, frontal ERP positivity, (2) rapid attentional shifting of processing toward favored visual stimuli, as measured by the N2pc component, and (3) longer-latency attention-related responses to reward feedback as a function of behavioral strategies (i.e., Win-Stay-Lose-Shift), as measured by the central-parietal P300. Consistent with our prior work in adults, the behavioral findings indicate preadolescents can learn stimulus-reward outcome associations, but at varying levels of performance. Neurally, poor preadolescent learners (those with slower learning rates) showed greater reward-related positivity amplitudes relative to good learners, suggesting greater reward-feedback sensitivity. We also found attention shifting towards to-be-chosen stimuli, as evidenced by the N2pc, but not to more highly rewarded stimuli as we have observed in adults. Lastly, we found the behavioral learning strategy (i.e., Win-Stay-Lose-Shift) reflected by the feedback-elicited parietal P300. These findings provide novel insights into the key neural processes underlying reinforcement learning in preadolescents.

College students with depression symptom are more sensitive to task difficulty in reinforcement learning

Backgrounds: Depression is usually characterized by impairments in reward function, and shows altered motivation to reward in reinforcement learning. This study further explored whether task difficulty affects reinforcement learning in college students with and without depression symptom. MethodsThe depression symptom group (20) and the no depression symptom group (26) completed a probabilistic reward learning task with low, medium, and high difficulty levels, in which task the response bias to reward and the discriminability of reward were analyzed. Additionally, electrophysiological responses to reward and loss feedback were recorded and analyzed while they performed a simple gambling task. ResultsThe depression symptom group showed more response bias to reward than the no depression symptom group when the task was easy and then exhibited more quickly decrease in response bias to reward as task difficulty increased. The no depression symptom group showed a decrease in response bias only in the high-difficulty condition. Further regression analyses showed that, the Feedback-related negativity (FRN) and theta oscillation could predict response bias change in the low-difficulty condition, the FRN and oscillations of theta and delta could predict response bias change in the medium and high-difficulty conditions. LimitationsThe electrophysiological responses to loss and reward were not recorded in the same task as the reinforcement learning behaviors. ConclusionsCollege students with depression symptom are more sensitive to task difficulty during reinforcement learning. The FRN, and oscillations of theta and delta could predict reward leaning behavior.

Reward Sensitivity and Noise Contribute to Negative Affective Bias: A Learning Signal Detection Theory Approach in Decision-Making.

In patients with mood disorders, negative affective biases - systematically prioritising and interpreting information negatively - are common. A translational cognitive task testing this bias has shown that depressed patients have a reduced preference for a high reward under ambiguous decision-making conditions. The precise mechanisms underscoring this bias are, however, not yet understood. We therefore developed a set of measures to probe the underlying source of the behavioural bias by testing its relationship to a participant's reward sensitivity, value sensitivity and reward learning rate. One-hundred-forty-eight participants completed three online behavioural tasks: the original ambiguous-cue decision-making task probing negative affective bias, a probabilistic reward learning task probing reward sensitivity and reward learning rate, and a gambling task probing value sensitivity. We modelled the learning task through a dynamic signal detection theory model and the gambling task through an expectation-maximisation prospect theory model. Reward sensitivity from the probabilistic reward task (β = 0.131, p = 0.024) and setting noise from the probabilistic reward task (β = -0.187, p = 0.028) both predicted the affective bias score in a logistic regression. Increased negative affective bias, at least on this specific task, may therefore be driven in part by a combination of reduced sensitivity to rewards and more variable responses.

Reinforcement-Learning-Informed Queries Guide Behavioral Change

Algorithmically defined aspects of reinforcement learning correlate with psychopathology symptoms and change with symptom improvement following cognitive-behavioral therapy (CBT). Separate work in nonclinical samples has shown that varying the structure and statistics of task environments can change learning. Here, we combine these literatures, drawing on CBT-based guided restructuring of thought processes and computationally defined mechanistic targets identified by reinforcement-learning models in depression, to test whether and how verbal queries affect learning processes. Using a parallel-arm design, we tested 1,299 online participants completing a probabilistic reward-learning task while receiving repeated queries about the task environment (11 learning-query arms and one active control arm). Querying participants about reinforcement-learning-related task components altered computational-model-defined learning parameters in directions specific to the target of the query. These effects on learning parameters were consistent across depression-symptom severity, suggesting new learning-based strategies and therapeutic targets for evoking symptom change in mood psychopathology.

Anxiety associated with perceived uncontrollable stress enhances expectations of environmental volatility and impairs reward learning

Unavoidable stress can lead to perceived lack of control and learned helplessness, a risk factor for depression. Avoiding punishment and gaining rewards involve updating the values of actions based on experience. Such updating is however useful only if action values are sufficiently stable, something that a lack of control may impair. We examined whether self-reported stress uncontrollability during the first wave of the COVID-19 pandemic predicted impaired reward-learning. In a preregistered study during the first-wave of the COVID-19 pandemic, we used self-reported measures of depression, anxiety, uncontrollable stress, and COVID-19 risk from 427 online participants to predict performance in a three-armed-bandit probabilistic reward learning task. As hypothesised, uncontrollable stress predicted impaired learning, and a greater proportion of probabilistic errors following negative feedback for correct choices, an effect mediated by state anxiety. A parameter from the best-fitting hidden Markov model that estimates expected beliefs that the identity of the optimal choice will shift across images, mediated effects of state anxiety on probabilistic errors and learning deficits. Our findings show that following uncontrollable stress, anxiety promotes an overly volatile representation of the reward-structure of uncertain environments, impairing reward attainment, which is a potential path to anhedonia in depression.

Reward System in Late-Life Depression: a Cross-Sectional Case-Control Study.

Anhedonia, commonly defined as a reduced ability to feel pleasure, is a core clinical symptom of late-life depression (LLD). Deficits in reward processing are hypothesised to be associated with anhedonia. We examined differences in reward sensitivity between patients with LLD and healthy controls and explored the associations between LLD-related symptomatology, global cognition, and the reward system. The reward responsiveness of 63 patients with LLD and 58 healthy controls aged ≥60 years was assessed using the probabilistic reward learning task with an asymmetric reward schedule. Compared with healthy controls, patients with LLD displayed lower response bias and reward learning. Global cognition of all participants was positively correlated with response bias. In patients with LLD, anhedonia severity explained impaired reward learning. A deficit in reward processing is implicated in patients with LLD. Our findings suggest that executive dysfunction and anhedonia contribute to lower sensitivity to reward learning in patients with LLD.

Striatal dopamine supports reward expectation and learning: A simultaneous PET/fMRI study

Converging evidence from both human neuroimaging and animal studies has supported a model of mesolimbic processing underlying reward learning behaviors, based on the computation of reward prediction errors. However, competing evidence supports human dopamine signaling in the basal ganglia as also contributing to the generation of higher order learning heuristics. Here, we present data from a large (N = 81, 18–30yo), multi-modal neuroimaging study using simultaneously acquired task fMRI, affording temporal resolution of reward system function, and PET imaging with [11C]Raclopride (RAC), assessing striatal dopamine (DA) D2/3 receptor binding, during performance of a probabilistic reward learning task. Both fMRI activation and PET DA measures showed ventral striatum involvement for signaling rewards. However, greater DA release was uniquely associated with learning strategies (i.e., learning rates) that were more task-optimal within the best fitting reinforcement learning model. This DA response was associated with BOLD activation of a network of regions including anterior cingulate cortex, medial prefrontal cortex, thalamus and posterior parietal cortex, primarily during expectation, rather than prediction error, task epochs. Together, these data provide novel, human in vivo evidence that striatal dopaminergic signaling interacts with a network of cortical regions to generate task-optimal learning strategies, rather than representing reward outcomes in isolation.

Pupillary response in reward processing in adults with major depressive disorder in remission.

Major depressive disorder (MDD) is associated with impaired reward processing and reward learning. The literature is inconclusive regarding whether these impairments persist after remission. The current study examined reward processing during a probabilistic learning task in individuals in remission from MDD (n = 19) and never depressed healthy controls (n = 31) matched for age and sex. The outcome measures were pupil dilation (an indirect index of noradrenergic activity and arousal) and computational modeling parameters. Participants completed two versions (facial/nonfacial feedback) of probabilistic reward learning task with changing contingencies. Pupil dilation was measured with a corneal reflection eye tracker. The hypotheses and analysis plan were preregistered. Healthy controls had larger pupil dilation following losses than gains (p <.001), whereas no significant difference between outcomes was found in individuals with a history of MDD, resulting in an interaction between group and outcome (β = 0.81, SE = 0.34, t = 2.37, p = .018). The rMDD group also achieved lower mean score at the last trial (t[46.77] = 2.12, p = .040) as well as a smaller proportion of correct choices (t[46.70] = 2.09, p = .041) compared with healthy controls. Impaired reward processing may persist after remission from MDD and could constitute a latent risk factor for relapse. Measuring pupil dilation in a reward learning task is a promising method for identifying reward processing abnormalities linked to MDD. The task is simple and noninvasive, which makes it feasible for clinical research.

P242. Anxiety Associated with Perceived Lack of Control Over Stress During the COVID-19 Pandemic Impairs Reward Learning

BackgroundUnavoidable stress can lead to perceived lack of control and learned helplessness, a risk factor for depression. Avoiding punishment and gaining rewards involve updating the values of actions based on experience. We examined whether self-reported stress uncontrollability during the first-wave of the COVID-19 pandemic predicted impaired reward-learning.MethodsIn a preregistered study, 427 online participants completed a three-armed-bandit probabilistic reward-learning task to maximise monetary reward. Participants chose between three composite-stimuli comprising three images each. Choosing the target image, which changed every 20-30 trials without warning, resulted in reward on 80% of trials (20% otherwise). Participants completed self-reported measures of depression, anxiety, stress uncontrollability, and risk of COVID-19, which we used to predict accuracy across trials. We tested various reinforcement learning and hidden Markov models (HMMs) to examine cognitive mechanisms underlying the effect of perceived uncontrollability on choices. We hypothesized that stress uncontrollability would predict worse reward-learning.ResultsHigher state anxiety and stress uncontrollability separately predicted impaired reward-learning (z=-2.66, p=.008; z=-3.05, p=.002). The effect of stress uncontrollability was mediated by anxiety (β=0.61, p=.052). State anxiety was positively associated with a parameter from the winning HMM that estimates how much beliefs about the identity of the target diffuse among images (r=.12, p=.0152).ConclusionsGreater perceived stress uncontrollability during the COVID-19 pandemic was associated with impaired reward-learning, and this effect was explained by current anxiety. These results suggest that anxiety promotes imprecision in the representation of the reward structure of the environment, mediating the observed effect of lack of control on reward-learning.Supported BySwedish Research CouncilKeywordsStress Controllability, Reward Learning, Computational Modelling, State Anxiety BackgroundUnavoidable stress can lead to perceived lack of control and learned helplessness, a risk factor for depression. Avoiding punishment and gaining rewards involve updating the values of actions based on experience. We examined whether self-reported stress uncontrollability during the first-wave of the COVID-19 pandemic predicted impaired reward-learning. Unavoidable stress can lead to perceived lack of control and learned helplessness, a risk factor for depression. Avoiding punishment and gaining rewards involve updating the values of actions based on experience. We examined whether self-reported stress uncontrollability during the first-wave of the COVID-19 pandemic predicted impaired reward-learning. MethodsIn a preregistered study, 427 online participants completed a three-armed-bandit probabilistic reward-learning task to maximise monetary reward. Participants chose between three composite-stimuli comprising three images each. Choosing the target image, which changed every 20-30 trials without warning, resulted in reward on 80% of trials (20% otherwise). Participants completed self-reported measures of depression, anxiety, stress uncontrollability, and risk of COVID-19, which we used to predict accuracy across trials. We tested various reinforcement learning and hidden Markov models (HMMs) to examine cognitive mechanisms underlying the effect of perceived uncontrollability on choices. We hypothesized that stress uncontrollability would predict worse reward-learning. In a preregistered study, 427 online participants completed a three-armed-bandit probabilistic reward-learning task to maximise monetary reward. Participants chose between three composite-stimuli comprising three images each. Choosing the target image, which changed every 20-30 trials without warning, resulted in reward on 80% of trials (20% otherwise). Participants completed self-reported measures of depression, anxiety, stress uncontrollability, and risk of COVID-19, which we used to predict accuracy across trials. We tested various reinforcement learning and hidden Markov models (HMMs) to examine cognitive mechanisms underlying the effect of perceived uncontrollability on choices. We hypothesized that stress uncontrollability would predict worse reward-learning. ResultsHigher state anxiety and stress uncontrollability separately predicted impaired reward-learning (z=-2.66, p=.008; z=-3.05, p=.002). The effect of stress uncontrollability was mediated by anxiety (β=0.61, p=.052). State anxiety was positively associated with a parameter from the winning HMM that estimates how much beliefs about the identity of the target diffuse among images (r=.12, p=.0152). Higher state anxiety and stress uncontrollability separately predicted impaired reward-learning (z=-2.66, p=.008; z=-3.05, p=.002). The effect of stress uncontrollability was mediated by anxiety (β=0.61, p=.052). State anxiety was positively associated with a parameter from the winning HMM that estimates how much beliefs about the identity of the target diffuse among images (r=.12, p=.0152). ConclusionsGreater perceived stress uncontrollability during the COVID-19 pandemic was associated with impaired reward-learning, and this effect was explained by current anxiety. These results suggest that anxiety promotes imprecision in the representation of the reward structure of the environment, mediating the observed effect of lack of control on reward-learning. Greater perceived stress uncontrollability during the COVID-19 pandemic was associated with impaired reward-learning, and this effect was explained by current anxiety. These results suggest that anxiety promotes imprecision in the representation of the reward structure of the environment, mediating the observed effect of lack of control on reward-learning.

Reward learning in unmedicated women with bulimia nervosa: A pilot investigation

Bulimia nervosa (BN) is characterized by recurrent engagement in eating disorder behaviors despite negative consequences, potentially reflecting aberrant stimulus-response or reward-learning processes. Indeed, frontostriatal circuitry involved in reward learning is altered in persons with BN and preliminary research suggests reward learning is impaired in persons with BN. Additional research on reward learning in BN and its association with eating disorder symptom expression is warranted to further the field's understanding of potential pathophysiological mechanisms of BN. To this end, the probabilistic reward learning task (PRLT) was administered to unmedicated women with BN (n = 15) and demographically matched psychiatrically healthy women (n = 18). Contrary to our hypotheses, results demonstrated that women with BN showed greater reward learning during the PRLT relative to healthy comparison women when covarying for symptoms of depression, social anxiety, and mania. Exploratory analyses showed that binge-eating frequency was inversely associated with reward learning in women with BN; however, results should be interpreted with caution due to the small sample size. Together, results suggest that women with BN do not have deficits in implicit reward learning. Given the preliminary nature of this investigation, larger-scale studies are needed to further examine reward learning in current BN and could compare reward learning using general (e.g., monetary) and disorder-specific (e.g., food) reinforcers. Further work is needed to confirm the inverse association between reward learning and binge eating.

Aberrant computational mechanisms of social learning and decision-making in schizophrenia and borderline personality disorder.

Psychiatric disorders are ubiquitously characterized by debilitating social impairments. These difficulties are thought to emerge from aberrant social inference. In order to elucidate the underlying computational mechanisms, patients diagnosed with major depressive disorder (N = 29), schizophrenia (N = 31), and borderline personality disorder (N = 31) as well as healthy controls (N = 34) performed a probabilistic reward learning task in which participants could learn from social and non-social information. Patients with schizophrenia and borderline personality disorder performed more poorly on the task than healthy controls and patients with major depressive disorder. Broken down by domain, borderline personality disorder patients performed better in the social compared to the non-social domain. In contrast, controls and major depressive disorder patients showed the opposite pattern and schizophrenia patients showed no difference between domains. In effect, borderline personality disorder patients gave up a possible overall performance advantage by concentrating their learning in the social at the expense of the non-social domain. We used computational modeling to assess learning and decision-making parameters estimated for each participant from their behavior. This enabled additional insights into the underlying learning and decision-making mechanisms. Patients with borderline personality disorder showed slower learning from social and non-social information and an exaggerated sensitivity to changes in environmental volatility, both in the non-social and the social domain, but more so in the latter. Regarding decision-making the modeling revealed that compared to controls and major depression patients, patients with borderline personality disorder and schizophrenia showed a stronger reliance on social relative to non-social information when making choices. Depressed patients did not differ significantly from controls in this respect. Overall, our results are consistent with the notion of a general interpersonal hypersensitivity in borderline personality disorder and schizophrenia based on a shared computational mechanism characterized by an over-reliance on beliefs about others in making decisions and by an exaggerated need to make sense of others during learning specifically in borderline personality disorder.

Blunted medial prefrontal cortico-limbic reward-related effective connectivity and depression.

Major depressive disorder is a leading cause of disability and significant mortality, yet mechanistic understanding remains limited. Over the past decade evidence has accumulated from case-control studies that depressive illness is associated with blunted reward activation in the basal ganglia and other regions such as the medial prefrontal cortex. However it is unclear whether this finding can be replicated in a large number of subjects. The functional anatomy of the medial prefrontal cortex and basal ganglia has been extensively studied and the former has excitatory glutamatergic projections to the latter. Reduced effect of glutamatergic projections from the prefrontal cortex to the nucleus accumbens has been argued to underlie motivational disorders such as depression, and many prominent theories of major depressive disorder propose a role for abnormal cortico-limbic connectivity. However, it is unclear whether there is abnormal reward-linked effective connectivity between the medial prefrontal cortex and basal ganglia related to depression. While resting state connectivity abnormalities have been frequently reported in depression, it has not been possible to directly link these findings to reward-learning studies. Here, we tested two main hypotheses. First, mood symptoms are associated with blunted striatal reward prediction error signals in a large community-based sample of recovered and currently ill patients, similar to reports from a number of studies. Second, event-related directed medial prefrontal cortex to basal ganglia effective connectivity is abnormally increased or decreased related to the severity of mood symptoms. Using a Research Domain Criteria approach, data were acquired from a large community-based sample of subjects who participated in a probabilistic reward learning task during event-related functional MRI. Computational modelling of behaviour, model-free and model-based functional MRI, and effective connectivity dynamic causal modelling analyses were used to test hypotheses. Increased depressive symptom severity was related to decreased reward signals in areas which included the nucleus accumbens in 475 participants. Decreased reward-related effective connectivity from the medial prefrontal cortex to striatum was associated with increased depressive symptom severity in 165 participants. Decreased striatal activity may have been due to decreased cortical to striatal connectivity consistent with glutamatergic and cortical-limbic related theories of depression and resulted in reduced direct pathway basal ganglia output. Further study of basal ganglia pathophysiology is required to better understand these abnormalities in patients with depressive symptoms and syndromes.

Grant Report on Social Reward Learning in Schizophrenia †.

We report on the ongoing R21 project “Social Reward Learning in Schizophrenia”. Impairments in social cognition are a hallmark of schizophrenia. However, little work has been done on social reward learning deficits in schizophrenia. The overall goal of the project is to assess social reward learning in schizophrenia. A probabilistic reward learning (PRL) task is being used in the MRI scanner to evaluate reward learning to negative and positive social feedback. Monetary reward learning is used as a comparison to assess specificity. Behavioral outcomes and brain areas, included those involved in reward, are assessed in patients with schizophrenia or schizoaffective disorder and controls. It is also critical to determine whether decreased expected value (EV) of social stimuli and/or reward prediction error (RPE) learning underlie social reward learning deficits to inform potential treatment pathways. Our central hypothesis is that the pattern of social learning deficits is an extension of a more general reward learning impairment in schizophrenia and that social reward learning deficits critically contribute to deficits in social motivation and pleasure. We hypothesize that people with schizophrenia will show impaired behavioral social reward learning compared to controls, as well as decreased ventromedial prefrontal cortex (vmPFC) EV signaling at time of choice and decreased striatal RPE signaling at time of outcome, with potentially greater impairment to positive than negative feedback. The grant is in its second year. It is hoped that this innovative approach may lead to novel and more targeted treatment approaches for social cognitive impairments, using cognitive remediation and/or brain stimulation.

Narcissistic neuroticism and elevated reward learning

Narcissism can be conceptualized as a combination of maladaptive variants of Five-Factor Model facets which load onto three of the five factors—antagonism (i.e., low agreeableness), agentic extraversion, and narcissistic neuroticism. Much research examines approach and avoidance motivations in narcissism; however, few studies assess these processes beyond self-report. In this study we examined how antagonism, agentic extraversion, and narcissistic neuroticism relate to reward learning—thought to be a component of approach motivation. A sample of 73 nonpsychiatric participants (71% female) completed a behavioral computer-administered probabilistic reward learning task involving monetary winnings. Narcissism was measured using the Five-Factor Narcissism Inventory (FFNI), which is a measure of grandiose and vulnerable narcissism using meta-analytically related Five-Factor Model facets that were modified to reflect narcissistic themes. Results indicated that narcissistic neuroticism related to greater average reward learning (i.e., response bias) across the task. Examination at the facet level indicated that FFNI Need for Admiration was the only facet that related significantly to response bias. This finding suggests that vulnerable narcissists may be more responsive to positive self-relevant feedback because of an increased sensitivity and ability to learn implicitly from reward.

Reinforcement learning across the rat estrous cycle

Reinforcement learning, the process by which an organism flexibly adapts behavior in response to reward and punishment, is vital for the proper execution of everyday behaviors, and its dysfunction has been implicated in a wide variety of mental disorders. Here, we use computational trial-by-trial analysis of data of female rats performing a probabilistic reward learning task and demonstrate that core computational processes underlying value-based decision making fluctuate across the estrous cycle, providing a neuroendocrine substrate by which gonadal hormones may influence adaptive behavior.

Attenuation of dopamine-modulated prefrontal value signals underlies probabilistic reward learning deficits in old age.

Probabilistic reward learning is characterised by individual differences that become acute in aging. This may be due to age-related dopamine (DA) decline affecting neural processing in striatum, prefrontal cortex, or both. We examined this by administering a probabilistic reward learning task to younger and older adults, and combining computational modelling of behaviour, fMRI and PET measurements of DA D1 availability. We found that anticipatory value signals in ventromedial prefrontal cortex (vmPFC) were attenuated in older adults. The strength of this signal predicted performance beyond age and was modulated by D1 availability in nucleus accumbens. These results uncover that a value-anticipation mechanism in vmPFC declines in aging, and that this mechanism is associated with DA D1 receptor availability.

Deficits in reinforcement learning but no link to apathy in patients with schizophrenia

Negative symptoms in schizophrenia have been linked to selective reinforcement learning deficits in the context of gains combined with intact loss-avoidance learning. Fundamental mechanisms of reinforcement learning and choice are prediction error signaling and the precise representation of reward value for future decisions. It is unclear which of these mechanisms contribute to the impairments in learning from positive outcomes observed in schizophrenia. A recent study suggested that patients with severe apathy symptoms show deficits in the representation of expected value. Considering the fundamental relevance for the understanding of these symptoms, we aimed to assess the stability of these findings across studies. Sixty-four patients with schizophrenia and 19 healthy control participants performed a probabilistic reward learning task. They had to associate stimuli with gain or loss-avoidance. In a transfer phase participants indicated valuation of the previously learned stimuli by choosing among them. Patients demonstrated an overall impairment in learning compared to healthy controls. No effects of apathy symptoms on task indices were observed. However, patients with schizophrenia learned better in the context of loss-avoidance than in the context of gain. Earlier findings were thus partially replicated. Further studies are needed to clarify the mechanistic link between negative symptoms and reinforcement learning.

Dopaminergic Enhancement of Striatal Response to Reward in Major Depression

Major depressive disorder is characterized by reduced reward-related striatal activation and dysfunctional reward learning, putatively reflecting decreased dopaminergic signaling. The goal of this study was to test whether a pharmacological challenge designed to facilitate dopaminergic transmission can enhance striatal responses to reward and improve reward learning in depressed individuals. In a double-blind placebo-controlled design, 46 unmedicated depressed participants and 43 healthy control participants were randomly assigned to receive either placebo or a single low dose (50 mg) of the D2/D3 receptor antagonist amisulpride, which is believed to increase dopamine signaling through presynaptic autoreceptor blockade. To investigate the effects of increased dopaminergic transmission on reward-related striatal function and behavior, a monetary incentive delay task (in conjunction with functional MRI) and a probabilistic reward learning task were administered at absorption peaks of amisulpride. Depressed participants selected previously rewarded stimuli less frequently than did control participants, indicating reduced reward learning, but this effect was not modulated by amisulpride. Relative to depressed participants receiving placebo (and control participants receiving amisulpride), depressed participants receiving amisulpride exhibited increased striatal activation and potentiated corticostriatal functional connectivity between the nucleus accumbens and the midcingulate cortex in response to monetary rewards. Stronger corticostriatal connectivity in response to rewards predicted better reward learning among depressed individuals receiving amisulpride as well as among control participants receiving placebo. Acute enhancement of dopaminergic transmission potentiated reward-related striatal activation and corticostriatal functional connectivity in depressed individuals but had no behavioral effects. Taken together, the results suggest that targeted pharmacological treatments may normalize neural correlates of reward processing in depression; despite such acute effects on neural function, behavioral modification may require more chronic exposure. This is consistent with previous reports that antidepressant effects of amisulpride in depression emerged after sustained administration.

Salience attribution and its relationship to cannabis-induced psychotic symptoms.

Cannabis is a widely used drug associated with increased risk for psychosis. The dopamine hypothesis of psychosis postulates that altered salience processing leads to psychosis. We therefore tested the hypothesis that cannabis users exhibit aberrant salience and explored the relationship between aberrant salience and dopamine synthesis capacity. We tested 17 cannabis users and 17 age- and sex-matched non-user controls using the Salience Attribution Test, a probabilistic reward-learning task. Within users, cannabis-induced psychotic symptoms were measured with the Psychotomimetic States Inventory. Dopamine synthesis capacity, indexed as the influx rate constant K i cer , was measured in 10 users and six controls with 3,4-dihydroxy-6-[18F]fluoro-l-phenylalanine positron emission tomography. There was no significant difference in aberrant salience between the groups [F 1,32 = 1.12, p = 0.30 (implicit); F 1,32 = 1.09, p = 0.30 (explicit)]. Within users there was a significant positive relationship between cannabis-induced psychotic symptom severity and explicit aberrant salience scores (r = 0.61, p = 0.04) and there was a significant association between cannabis dependency/abuse status and high implicit aberrant salience scores (F 1,15 = 5.8, p = 0.03). Within controls, implicit aberrant salience was inversely correlated with whole striatal dopamine synthesis capacity (r = -0.91, p = 0.01), whereas this relationship was non-significant within users (difference between correlations: Z = -2.05, p = 0.04). Aberrant salience is positively associated with cannabis-induced psychotic symptom severity, but is not seen in cannabis users overall. This is consistent with the hypothesis that the link between cannabis use and psychosis involves alterations in salience processing. Longitudinal studies are needed to determine whether these cognitive abnormalities are pre-existing or caused by long-term cannabis use.

Theta-band oscillatory activity differs between gamblers and nongamblers comorbid with attention-deficit hyperactivity disorder in a probabilistic reward-learning task

Problemgambling is thought to be comorbid with attention-deficit hyperactivity disorder (ADHD). We tested whether gamblers and ADHD patients exhibit similar reward-related brain activity in response to feedback in a gambling task. A series of brain electrical responses can be observed in the electroencephalogram (EEG) and the stimulus-locked event-related potentials (ERP), when participants in a gambling task are given feedback regardless of winning or losing the previous bet. Here, we used a simplified computerized version of the Iowa Gambling Task (IGT) to assess differences in reinforcement-driven choice adaptation between unmedicated ADHD patients with or without problem gambling traits and contrasted with a sex- and age-matched control group. EEG was recorded from the participants while they were engaged in the task which contained two choice options with different net payouts and win/loss probabilities. Learning trend which shows the ability to acquire and use knowledge of the reward outcomes to obtain a positive financial outcome was not observed in ADHD gamblers versus nongamblers. Induced theta-band (4–8Hz) power over frontal cortex was significantly higher in gamblers versus nongamblers in all different high-risk/low-risk win/lose conditions. Whereas induced low alpha (9–11Hz) power at frontal electrodes could only differentiate high-risk lose between gamblers and nongamblers but not the other three conditions between the two groups. The results indicate that ADHD nongamblers do not share with problem gamblers underlying deficits in reward learning. These pilot data highlight the need for studies of ADHD in gambling to elucidate how motivational states are represented during feedback processing.