There is evidence that specific regions of the face such as the eyes are particularly relevant for the decoding of emotional expressions, but it has not been examined whether scan paths of observers vary for facial expressions with different emotional content. In this study, eye-tracking was used to monitor scanning behavior of healthy participants while looking at different facial expressions. Locations of fixations and their durations were recorded, and a dominance ratio (i.e., eyes and mouth relative to the rest of the face) was calculated. Across all emotional expressions, initial fixations were most frequently directed to either the eyes or the mouth. Especially in sad facial expressions, participants more frequently issued the initial fixation to the eyes compared with all other expressions. In happy facial expressions, participants fixated the mouth region for a longer time across all trials. For fearful and neutral facial expressions, the dominance ratio indicated that both the eyes and mouth are equally important. However, in sad and angry facial expressions, the eyes received more attention than the mouth. These results confirm the relevance of the eyes and mouth in emotional decoding, but they also demonstrate that not all facial expressions with different emotional content are decoded equally. Our data suggest that people look at regions that are most characteristic for each emotion.

Psychosocial stressors induce autonomic nervous system (ANS) responses in multiple body systems that are linked to health risks. Much work has focused on the common effects of stress, but ANS responses in different body systems are dissociable and may result from distinct patterns of cortical-subcortical interactions. Here, we used machine learning to develop multivariate patterns of fMRI activity predictive of heart rate (HR) and skin conductance level (SCL) responses during social threat in humans (N ϭ 18). Overall, brain patterns predicted both HR and SCL in cross-validated analyses successfully (r HR ϭ 0.54, r SCL ϭ 0.58, both p Ͻ 0.0001). These patterns partly reflected central stress mechanisms common to both responses because each pattern predicted the other signal to some degree (r HR¡SCL ϭ 0.21 and r SCL¡HR ϭ 0.22, both p Ͻ 0.01), but they were largely physiological response specific. Both patterns included positive predictive weights in dorsal anterior cingulate and cerebellum and negative weights in ventromedial PFC and local pattern similarity analyses within these regions suggested that they encode common central stress mechanisms. However, the predictive maps and searchlight analysis suggested that the patterns predictive of HR and SCL were substantially different across most of the brain, including significant differences in ventromedial PFC, insula, lateral PFC, pre-SMA, and dmPFC. Overall, the results indicate that specific patterns of cerebral activity track threat-induced autonomic responses in specific body systems. Physiological measures of threat are not interchangeable, but rather reflect specific interactions among brain systems.

Maladaptive social behavior is one of the defining characteristics of psychopathic personality disorder. Nevertheless, maladaptive social behavior has only rarely been observed among psychopaths in experimentally controlled situations. The authors assessed the behavior of criminal psychopaths from high-security psychiatric hospitals in a computer simulation of a social dilemma situation. The psychopaths showed a markedly higher proneness to competitive (i.e., noncooperative) behavior than did healthy adults from the general population. The odds ratio between defection and being a psychopath was estimated at 7.86 in the sample. The probability to choose selfish instead of cooperative behavior was significantly linked to the following subscales of the Psychopathy Personality Inventory-Revised (S. O. Lilienfeld & M. R. Widows, 2005): rebellious nonconformity, Machiavellian egocentricity, and the total score. On average, the psychopathic participants accumulated higher gain and more strongly exploited their counterpart than did the healthy participants.

Some self-report measures of personality and personality disorders, including the widely used Psychopathic Personality Inventory-Revised (PPI-R), are lengthy and time-intensive. In recent work, we introduced an automated genetic algorithm (GA)-based method for abbreviating psychometric measures. In Study 1, we used this approach to generate a short (40-item) version of the PPI-R using 3 large-N German student samples (total N = 1,590). The abbreviated measure displayed high convergent correlations with the original PPI-R, and outperformed an alternative measure constructed using a conventional approach. Study 2 tested the convergent and discriminant validity of this short version in a fourth student sample (N = 206) using sensation-seeking and sensitivity to reward and punishment scales, again demonstrating similar convergent and discriminant validity for the PPI-R-40 compared with the full version. In a fifth community sample of North American participants acquired using Amazon Mechanical Turk, the PPI-R-40 showed similarly high convergent correlations, demonstrating stability across language, culture, and data-collection method. Taken together, these studies suggest that the GA approach is a viable method for abbreviating measures of psychopathy, and perhaps personality measures in general.