Recent dual-process models of decision making have suggested that emotion plays an important role in decision making; however, the impact of incidental moods (i.e., emotions unrelated to the immediate situation) on decisions remains poorly explored. This question was investigated by inducing 2 basic emotional states (amusement or sadness) that were compared with a neutral-emotion control group. Decision making was assessed with a well-studied social task, the Ultimatum Game. In this task, participants had to make decisions to either accept or reject monetary offers from other players, offers that varied in their degree of unfairness. Emotion was induced with short movie clips. Induced sadness interacted with offer fairness, with higher sadness resulting in lower acceptance rates of unfair offers. Induced amusement was not associated with any significant biases in decision making. These results demonstrate that even subtle incidental moods can play an important role in biasing decision making. Implications of these results in regards to the emotion, cognitive neuroscience, and clinical literatures are discussed.

Although the role of emotion in social economic decision-making has been increasingly recognized, the impact of mood disorders, such as depression, on such decisions has been surprisingly neglected. To address this gap, fifteen depressed and twenty-three nondepressed individuals completed a wellknown economic task, in which they had to accept or reject monetary offers from other players. Although depressed individuals reported a more negative emotional reaction to unfair offers, they accepted significantly more of these offers than did controls. A positive relationship was observed in the depressed group, but not in controls, between acceptance rates of unfair offers and resting cardiac vagal control, a physiological index of emotion regulation capacity. The discrepancy between depressed individuals' increased emotional reactions to unfair offers and their decisions to accept more of these offers contrasts with recent findings that negative mood in nondepressed individuals can lead to lower acceptance rates. This suggests distinct biasing processes in depression, which may be related to higher reliance on regulating negative emotion.

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, we examined neural processing characteristics in human occasional stimulant users (OSU), a population at risk for dependence. A total of 158 nondependent OSU and 47 stimulant-naive control subjects (CS) were recruited and completed a stop signal task while undergoing functional magnetic resonance imaging (fMRI). A Bayesian ideal observer model was used to predict probabilistic expectations of inhibitory demand, P(stop), on a trial-to-trial basis, based on experienced trial history. Compared with CS, OSU showed attenuated neural activation related to P(stop) magnitude in several areas, including left prefrontal cortex and left caudate. OSU also showed reduced neural activation in the dorsal anterior cingulate cortex (dACC) and right insula in response to an unsigned Bayesian prediction error representing the discrepancy between stimulus outcome and the predicted probability of a stop trial. These results indicate that, despite minimal overt behavioral manifestations, OSU use fewer brain processing resources to predict and update the need for response inhibition, processes that are critical for adjusting and optimizing behavioral performance, which may provide a biomarker for the development of substance dependence.

There is some evidence that neuroimaging can be used to predict relapse among abstinent methamphetamine-dependent (MD) individuals. However, it remains unclear what cognitive and neural processes contribute to relapse. This investigation examined whether insula activation during risk-taking decisions-a process shown to be disrupted in MD-is able to predict susceptibility for relapse. Sixty-eight MD enrolled in a treatment program during early abstinence completed a risk-taking task during functional magnetic resonance imaging. Sixty-three of the sixty-eight individuals were followed up 1 year after the study. Of these, 18 MD reported relapse. The 45 abstinent MD showed patterns of insula activation during risky decisions that resembled those found in prior studies of healthy controls, consisting of lower insula activation during safe decisions paired with higher activation during risky decisions. In contrast, the 18 relapsed MD showed similar insula activation during safe and risky decisions. An increase in one standard deviation in the difference in insula activation between risky and safe choices was associated with a 0.34 odds ratio for relapse at any given time. A median split of insula activation (difference between risky and safe) showed that individuals in the bottom half were two times more likely to relapse. In addition, a model that included several other brain regions increased prediction accuracy compared with insula-based model alone. These results suggest that failure to differentially activate the insula as a function of risk is a part of an altered risk-processing network associated with an increased susceptibility to relapse.

Delineating the processes that contribute to the progression and maintenance of substance dependence is critical to understanding and preventing addiction. Several previous studies have shown inhibitory control deficits in individuals with stimulant use disorder. We used a Bayesian computational approach to examine potential neural deficiencies in the dynamic predictive processing underlying inhibitory function among recently abstinent methamphetamine-dependent individuals (MDIs), a population at high risk of relapse. Sixty-two MDIs were recruited from a 28-day inpatient treatment program at the San Diego Veterans Affairs Medical Center and compared with 34 healthy control subjects. They completed a stop-signal task during functional magnetic resonance imaging. A Bayesian ideal observer model was used to predict individuals’ trial-to-trial probabilistic expectations of inhibitory response, P(stop), to identify group differences specific to Bayesian expectation and prediction error computation. Relative to control subjects, MDIs were more likely to make stop errors on difficult trials and had attenuated slowing following stop errors. MDIs further exhibited reduced sensitivity as measured by the neural tracking of a Bayesian measure of surprise (unsigned prediction error), which was evident across all trials in the left posterior caudate and orbitofrontal cortex (Brodmann area 11), and selectively on stop error trials in the right thalamus and inferior parietal lobule. MDIs are less sensitive to surprising task events, both across trials and upon making commission errors, which may help explain why these individuals may not engage in switching strategy when the environment changes, leading to adverse consequences.

Although recent economic models of human decision making have recognised the role of emotion as an important biasing factor, the impact of incidental emotion on decisions has remained poorly explored. To address this question, we jointly explored the role of emotional valence (i.e., positive vs. negative) and motivational direction (i.e., approach vs. avoidance) on performance in a well-known economic task, the Ultimatum Game. Participants had to either accept or reject monetary offers from other players, offers that vary in their degree of unfairness. A main effect of motivational direction, but not valence, was observed, with withdrawal-based emotion (disgust and serenity) prompting more rejections relative to approach-based emotion (anger and amusement) and a neutral state. These results further confirm that subtle incidental moods can bias decision making, and suggest that motivational state may be a useful framework to study such decisions. Implications with regard to emotion, cognitive neuroscience, and clinical psychology are discussed.

Recent research in neuroeconomics suggests that social economic decision-making may be best understood as a dual-systems process, integrating the influence of deliberative and affective subsystems. However, most of this research has focused on young adults and it remains unclear whether our current models extend to healthy aging. To address this question, we investigated the behavioral and neural basis of simple economic decisions in 18 young and 20 older healthy adults. Participants made decisions which involved accepting or rejecting monetary offers from human and non-human (computer) partners in an Ultimatum Game, while undergoing functional magnetic resonance imaging (fMRI). The partners’ proposals involved splitting an amount of money between the two players, and ranged from $1 to $5 (from a $10 pot). Relative to young adults, older participants expected more equitable offers and rejected moderately unfair offers ($3) to a larger extent. Imaging results revealed that, relative to young participants, older adults had higher activations in the left dorsolateral prefrontal cortex (DLPFC) when receiving unfair offers ($1–$3). Age group moderated the relationship between left DLPFC activation and acceptance rates of unfair offers. In contrast, older adults showed lower activation of bilateral anterior insula in response to unfair offers. No age group difference was observed when participants received fair ($5) offers. These findings suggest that healthy aging may be associated with a stronger reliance on computational areas subserving goal maintenance and rule shifting (DLPFC) during interactive economic decision-making. Consistent with a well-documented “positivity effect”, older age may also decrease recruitment of areas involved in emotion processing and integration (anterior insula) in the face of social norm violation.