Few studies have explored neural mechanisms of reward learning in ASD despite evidence of behavioral impairments of predictive abilities in ASD. To investigate the neural correlates of reward prediction errors in ASD, 16 adults with ASD and 14 typically developing controls performed a prediction error task during fMRI scanning. Results revealed greater activation in the ASD group in the left paracingulate gyrus during signed prediction errors and the left insula and right frontal pole during thresholded unsigned prediction errors. Findings support atypical neural processing of reward prediction errors in ASD in frontostriatal regions critical for prediction coding and reward learning. Results provide a neural basis for impairments in reward learning that may contribute to traits common in ASD (e.g., intolerance of unpredictability).

Self-reflection and thinking about the thoughts and behaviors of others are important skills for humans to function in the social world. These two processes overlap in terms of the component processes involved, and share overlapping functional organizations within the human brain, in particular within the medial prefrontal cortex (MPFC). Several functional models have been proposed to explain these two processes, but none has directly explored the extent to which they are distinctly represented within different parts of the brain. This study used multivoxel pattern classification to quantify the separability of self- and other-related thought in the MPFC and expanded this question to the entire brain. Using a large-scale mega-analytic dataset, spanning three separate studies (n = 142), we find that self- and other-related thought can be reliably distinguished above chance within the MPFC, posterior cingulate cortex and temporal lobes. We highlight subcomponents of the ventral MPFC that are particularly important in representing self-related thought, and subcomponents of the orbitofrontal cortex robustly involved in representing other-related thought. Our findings indicate that representations of self- and other-related thought in the human brain are described best by a distributed pattern rather than stark localization or a purely ventral to dorsal linear gradient in the MPFC.

Jury decisions are among the most consequential social decisions in which bias plays a notable role. While courts take a number of measures to reduce the influence of bias on decisions about case strength or deserved punishment based on evidence introduced during a trial, jurors may still incorporate personal biases based on knowledge, experience, emotion, and beliefs independent of evidence. One common form of this bias, crime-type bias, is the extent to which the perceived strength of a case depends on the severity of the crime. A number of explanations from psychology and law point to the role of moral judgment, social cognition, and affect as core processes of bias. However, behavioral evidence alone makes these explanations difficult to distinguish. To overcome this challenge, we used fMRI to record brain activation patterns of mock jurors as they read a series of criminal scenarios and rated the strength of the cases and deserved punishment. Compared to patterns of brain activation derived from large neuroimaging databases, mock jurors’ neural activation patterns related to crime-type bias were most similar to patterns associated with social cognition (such as those associated with mentalizing and racial bias) but not affect or moral judgment. Further, results indicated that crime-type bias could be explained by variability in victim harm. Our results support a central role for social cognition in juror decision making and suggest that crime-type bias may arise from similar mechanisms that precipitate other biases like stereotypes about culture or race.

We describe a risk protocol that combines the rigor of economic studies of risk with the ecological validity of tasks from psychology. Despite a wealth of experimental contributions on risk preferences, stemming from a variety of elicitation tasks, the external validity of standard measures of risk is questionable. In this study we focus on a risk task-the Balloon Analogue Risk Task (BART)-which is highly successful in predicting health-related risk behaviors such as alcohol use, drug use, smoking, unprotected sex, driving without a seatbelt, and stealing. The BART is not commonly used by economic scholars because of concerns that participants may not adequately comprehend uncertainty associated with the task and because of the resulting difficulty in relating participants' choices to standard risk models. To answer these concerns and build on associations with real world risk, we designed a modified BART, which we will refer to as the Balloon Economic Risk Protocol (BERP). In this protocol, participants observe the distribution of pop points prior to the task to create a more consistent knowledge base. We then use a belief elicitation technique to produce a user-generated prior distribution of balloon pops. Using these measures, we compare participants' behavior to the expected-value optimum to provide a link to standard models of risk. In accordance with past economic literature, we found that participants' BERP-generated risk preferences revealed mild risk aversion on average, and correlated with a self-report questionnaire on drinking, drug use, and smoking behavior.

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