Self-reported psychopathic traits and socio-emotional function in 9-12 year old children from the community

The aim of this thesis was to enhance our understanding of the concept of psychopathic traits in preadolescent children. It did so by investigating a new assessment tool providing a previously unexplored perspective on psychopathic traits in preadolescent children: that of the child itself. This is important because children are in the unique position to report on feelings, attitudes and behaviors across a range of situations, including the home, the classroom and the playground. Furthermore, it sought to provide a deeper understanding of the nature of psychopathic traits and their relations to problematic socio-emotional functioning. It was concluded that psychopathic traits can be indeed measured reliably and meaningfully through self-report in 9 to 12 year olds. Furthermore, children with high levels of psychopathic traits were shown to suffer from impaired social functioning emotionally, motivationally, and interpersonally. These problems may be important targets for future interventions for this group. Finally, it was demonstrated that that children with psychopathic traits are prone to act aggressively, but also that this aggression is dynamic and is dependent upon circumstances. Their aggression can be attenuated by a salient display of others__ distress. These results suggest that empathy based treatment techniques may reduce aggression in children with psychopathic traits.UBL - phd migration 201

Measuring affective state:Subject-dependent and-independent prediction based on longitudinal multimodal sensing

Current sensors offering passive and continuous monitoring of behavioral patterns potentially enable real-time affective state monitoring. Previous research on affective state prediction with multimodal sensing in daily life has shown only small-to-moderate effects. One reason for this limited success might be the large variability across individuals. Current research is often of short duration, preventing proper within-individual modeling. With an extensive longitudinal data collection of nine months, this research focuses on individual-level predictions of valence and arousal in daily life. Sixteen PhD candidates from The Netherlands provided data about their affective states (self-reported valence and arousal), physiology (Oura rings) and behavioral patterns (AWARE framework for mobile phone data). Supporting our hypothesis, subject-dependent random forest (RF) models significantly outperformed subject-independent leave-one-subject-out (LOSO) models in predicting self-reported valence and arousal. The subject-dependent models achieved an average Spearman's rho correlation of 0.30 [0.14-0.60] for valence and 0.36 [0.16-0.69] for arousal. In many cases, participants' a priori indicated informative sources matched with the feature importance. Making use of participants' self-knowledge might thus help to reduce the amount of data to be collected. For future work, longer-term changes in affective state and combinations of features for estimating real behavioral patterns should be explored.</p

Quantum Statistical Physics of Glasses at Low Temperatures

We present a quantum statistical analysis of a microscopic mean-field model of structural glasses at low temperatures. The model can be thought of as arising from a random Born von Karman expansion of the full interaction potential. The problem is reduced to a single-site theory formulated in terms of an imaginary-time path integral using replicas to deal with the disorder. We study the physical properties of the system in thermodynamic equilibrium and develop both perturbative and non-perturbative methods to solve the model. The perturbation theory is formulated as a loop expansion in terms of two-particle irreducible diagrams, and is carried to three-loop order in the effective action. The non-perturbative description is investigated in two ways, (i) using a static approximation, and (ii) via Quantum Monte Carlo simulations. Results for the Matsubara correlations at two-loop order perturbation theory are in good agreement with those of the Quantum Monte Carlo simulations. Characteristic low-temperature anomalies of the specific heat are reproduced, both in the non-perturbative static approximation, and from a three-loop perturbative evaluation of the free energy. In the latter case the result so far relies on using Matsubara correlations at two-loop order in the three-loop expressions for the free energy, as self-consistent Matsubara correlations at three-loop order are still unavailable. We propose to justify this by the good agreement of two-loop Matsubara correlations with those obtained non-perturbatively via Quantum Monte Carlo simulations.Comment: 13 pages, 6 figure

Towards user-adapted training paradigms: physiological responses to physical threat during cognitive task performance

Feedback of physiological responses have a great potential to support virtual training paradigms aimed to increase cognitive task performance under stressful threatening conditions. In the current study, we examined the sensitivity of a range of physiological indicators derived from electrodermal activity (EDA), blood pressure (BP) and heart rate (HR) to measure stress as induced by the threat of an electric shock (ES). In contrast to previous work that studied physiological stress responses compared to a rest condition, we compared conditions with high cognitive load combined with stress caused by threat of an ES, to conditions with high cognitive load without such stress. Twenty-five participants performed a cognitively demanding task in an experimental setup. At certain 10 s time intervals, indicated by a continuous tone, participants were either asked to do their best and increase cognitive task performance (non-threat condition), or they were told that they could receive an ES during this interval if cognitive task performance was not high enough (threat condition). Physiological measures, task performance and self-reported measures of stress and workload were analysed. Task performance and self-reported measures of stress and workload were roughly the same in both conditions. Especially EDA measures were affected by the threat of an ES. Threat and non-threat conditions could be distinguished with an across-participant classifier using EDA and BP features with an accuracy of 70%. These results suggest that EDA and BP can be used to evaluate stress coping training paradigms or to individually adapt the stress levels in virtual training environments.Stress-related psychiatric disorders across the life spa

Towards continuous mental state detection in everyday settings: investigating between-subjects variations in a longitudinal study

Maintaining mental health can be quite challenging, especially when exposed to stressful situations. In many cases, mental health problems are recognized too late to effectively intervene and prevent adverse outcomes. Recent advances in the availability and reliability of wearable technologies offer opportunities for continuously monitoring mental states, which may be used to improve a person’s mental health. Previous studies attempting to detect and predict mental states with different modalities have shown only small to moderate effect sizes. This limited success may be due to the large variability between individuals regarding e.g., ways of coping with stress or behavioral patterns associated with positive or negative feelings. A study was set up for the detection of mental states based on longitudinal wearable and contextual sensing, targeted at investigating between-subjects variations in terms of predictors of mental states and variations in how predictors relate to mental states. At the end of March 2022, 16 PhD candidates from the Netherlands started to participate in the study. Over nine months, we collected data in terms of their daily mental states (valence and arousal), continuous physiological data (Oura ring) and smartphone data (AWARE framework including GPS and smartphone usage). From the raw data, we aggregated daily values for each participant in terms of sleep, physical activity, mental states, phone usage and GPS movement. First results (six months into the study at the time of writing) indicate that almost all participants show a large variability in ratings of daily mental states, which is a prerequisite for predictive modeling. Direction, strength and standard deviations of Spearman correlations between valence, arousal and the different variables suggest that several predictors of valence and arousal are more subject dependent than others. In future analyses, we will test and compare different versions of predictive modeling to highlight the potential of wearable technologies for mental state monitoring and the personalized prediction of the development of mental problems

The effects of changes in the order of verbal labels and numerical values on children's scores on attitude and rating scales

Research with adults has shown that variations in verbal labels and numerical scale values on rating scales can affect the responses given. However, few studies have been conducted with children. The study aimed to examine potential differences in children’s responses to Likert-type rating scales according to their anchor points and scale direction, and to see whether or not such differences were stable over time. 130 British children, aged 9 to 11, completed six sets of Likert-type rating scales, presented in four different ways varying the position of positive labels and numerical values. The results showed, both initially and 8-12 weeks later, that presenting a positive label or a high score on the left of a scale led to significantly higher mean scores than did the other variations. These findings indicate that different arrangements of rating scales can produce different results which has clear implications for the administration of scales with children