Objective biomarkers for differential diagnosis in psychiatry are still scarce. Voice atypicalities characterize two prominent, often co-occurring psychiatric disorders: schizophrenia-spectrum disorders (SSD) and major depressive disorders (MDD). Given that voice recordings can be easily obtained, advanced speech analysis might facilitate the development of diagnostic biomarkers for SSD and MDD. Speech was recorded from a transdiagnostic sample comprising 47 SSD patients, 62 MDD patients, and 41 healthy controls (HC), during three different tasks: a semi-structured interview, a reading task and an empathy task. We evaluated the discriminative power of standardized speech parameters and compared the performance of the three tasks. The extended Geneva Acoustic Minimalistic Parameter Set (eGeMAPS) was extracted using openSMILE and fed into random forest (RF) algorithms with 10-fold crossvalidation. Model performances were evaluated using accuracy, F1-score, precision, and recall. Importance of specific predictors was assessed using Gini importance. In this three-class problem, a simple 1-minute video task reached best results with 57% accuracy. The acoustic parameters revealed distinct vocal profiles associated with each disorder. Considering the chance probability of 33%, our results show that automated speech analysis could predict diagnostic classes with good to high accuracy.

Impulsivity is a multidimensional construct often associated with unfavorable outcomes. Previous studies have implicated several electroencephalography (EEG) indices to impulsiveness, but results are heterogeneous and inconsistent. Using a data-driven approach, we identified EEG power features for the prediction of self-reported impulsiveness. To this end, EEG signals of 56 individuals (18 low impulsive, 20 intermediate impulsive, 18 high impulsive) were recorded during a risk-taking task. Extracted EEG power features from 62 electrodes were fed into various machine learning classifiers to identify the most relevant band. Robustness of the classifier was varied by stratified [Formula: see text]-fold cross validation. Alpha and beta band power showed best performance in the classification of impulsiveness (accuracy = 95.18% and 95.11%, respectively) using a random forest classifier. Subsequently, a sequential bidirectional feature selection algorithm was used to estimate the most relevant electrode sites. Results show that as little as 10 electrodes are sufficient to reliably classify impulsiveness using alpha band power ([Formula: see text]-measure = 94.50%). Finally, the Shapley Additive exPlanations (SHAP) analysis approach was employed to reveal the individual EEG features that contributed most to the model's output. Results indicate that frontal as well as posterior midline alpha power seems to be of most importance for the classification of impulsiveness.

Virtual-reality-based interactions with virtual agents (VAs) are likely subject to similar influences as human-human interactions. In either real or virtual social interactions, interactants try to maintain their personal space (PS), an ubiquitous, situative, flexible safety zone. Building upon larger PS preferences to humans and VAs with angry facial expressions, we extend the investigations to whole-body emotional expressions. In two immersive settings-HMD and CAVE-66 males were approached by an either happy, angry, or neutral male VA. Subjects preferred a larger PS to the angry VA when being able to stop him at their convenience (Sample task), replicating previous findings, and when being able to actively avoid him (Pass By task). In the latter task, we also observed larger distances in the CAVE than in the HMD.

Personal space (PS), the flexible protective zone maintained around oneself, is a key element of everyday social interactions. It, e.g., affects people's interpersonal distance and is thus largely involved when navigating through social environments. However, the PS is regulated dynamically, its size depends on numerous social and personal characteristics and its violation evokes different levels of discomfort and physiological arousal. Thus, gaining more insight into this phenomenon is important. We contribute to the PS investigations by presenting the results of a controlled experiment in a CAVE, focusing on German males in the age of 18 to 30 years. The PS preferences of 27 participants have been sampled while they were approached by either a single embodied, computer-controlled virtual agent (VA) or by a group of three VAs. In order to investigate the influence of a VA's emotions, we altered their facial expression between angry and happy. Our results indicate that the emotion as well as the number of VAs approaching influence the PS: larger distances are chosen to angry VAs compared to happy ones; single VAs are allowed closer compared to the group. Thus, our study is a foundation for social and behavioral studies investigating PS preferences.