The human amygdala is reliably activated by facial expressions [1], but the precise functional relevance of such activity change is not well understood, because most previous studies did not allow for separating effects of the emotional expression from the distribution of specific facial features and neglected corresponding attentional processes. Findings on rare patients with bilateral amygdala damage indicate that the amygdala might be involved in triggering shifts of overt attention towards specific facial features such as the eyes [2]. Moreover, it was reported that healthy individuals show a preference for attending to the eye region across different emotional expressions [3]. This early attentional bias was linked to amygdala activity [4], and was found to be most pronounced for fearful faces and less pronounced for happy facial expressions [3,5]. These findings indicate that healthy individuals show a tendency to automatically attend to facial features that are diagnostic of the current emotional state of conspecifics [6]. Here, we examined an otherwise healthy, male adult individual (MW) with unilateral right-sided amygdala loss in a novel, eye-tracking-based face perception task in order to clarify the functional role of the amygdala complex in driving attentional orienting. Compared to a sample of matched controls, MW showed an isolated deficit in reflexive gaze shifts towards diagnostic emotional facial features during brief stimulus presentations as compared to normal performance during longer viewing periods. These results suggest that the amygdala is implicated in quickly detecting diagnostic facial features in the visual periphery and driving reflexive saccades towards these locations.

Impulsivity plays a key role in decision-making under uncertainty. It is a significant contributor to problem and pathological gambling (PG). Standard assessments of impulsivity by questionnaires, however, have various limitations, partly because impulsivity is a broad, multi-faceted concept. What remains unclear is which of these facets contribute to shaping gambling behavior. In the present study, we investigated impulsivity as expressed in a gambling setting by applying computational modeling to data from 47 healthy male volunteers who played a realistic, virtual slot-machine gambling task. Behaviorally, we found that impulsivity, as measured independently by the 11th revision of the Barratt Impulsiveness Scale (BIS-11), correlated significantly with an aggregate read-out of the following gambling responses: bet increases (BIs), machines switches (MS), casino switches (CS), and double-ups (DUs). Using model comparison, we compared a set of hierarchical Bayesian belief-updating models, i.e., the Hierarchical Gaussian Filter (HGF) and Rescorla–Wagner reinforcement learning (RL) models, with regard to how well they explained different aspects of the behavioral data. We then examined the construct validity of our winning models with multiple regression, relating subject-specific model parameter estimates to the individual BIS-11 total scores. In the most predictive model (a three-level HGF), the two free parameters encoded uncertainty-dependent mechanisms of belief updates and significantly explained BIS-11 variance across subjects. Furthermore, in this model, decision noise was a function of trial-wise uncertainty about winning probability. Collectively, our results provide a proof of concept that hierarchical Bayesian models can characterize the decision-making mechanisms linked to the impulsive traits of an individual. These novel indices of gambling mechanisms unmasked during actual play may be useful for online prevention measures for at-risk players and future assessments of PG.