Highly arousing, affective stimuli have adverse effects on cognition and performance. Perception of affective stimuli is, however, highly subjective and may impact on the interaction of emotion and cognition. Here, we tested the impact of high- versus low-threatening stimuli on response inhibition as a function of perceived threat intensity. Response inhibition was probed using a stop-signal paradigm in 62 healthy adults. We used stop-signals that had previously been paired with an unpleasant electrodermal stimulation (i.e., high-threat stimuli) or that had never been paired with electrodermal stimulation (i.e., low-threat stimuli). High-threat stimuli did not affect stopping performance in general. Only participants who perceived the high-threat stimuli as highly painful showed impaired response inhibition on high-threat trials relative to low-threat trials. Participants who perceived the high-threat as mildly painful, however, showed improved response inhibition on high-threat trials. This effect was not moderated by the current anxious state. This suggests that the impact of negative affective stimuli on cognition critically depends on subjective threat perception. Ratings of affective stimuli should be included in studies probing the emotion–cognition interaction because subjective perception might strongly impact on that interaction.
Resilience is the maintenance and/or quick recovery of mental health during and after periods of adversity. It is conceptualized to result from a dynamic process of successful adaptation to stressors. Up to now, a large number of resilience factors have been proposed, but the mechanisms underlying resilience are not yet understood. To shed light on the complex and time-varying processes of resilience that lead to a positive long-term outcome in the face of adversity, the Longitudinal Resilience Assessment (LORA) study has been established. In this study, 1191 healthy participants are followed up at 3- and 18-month intervals over a course of 4.5 years at two study centers in Germany. Baseline and 18-month visits entail multimodal phenotyping, including the assessment of mental health status, sociodemographic and lifestyle variables, resilience factors, life history, neuropsychological assessments (of proposed resilience mechanisms), and biomaterials (blood for genetic and epigenetic, stool for microbiome, and hair for cortisol analysis). At 3-monthly online assessments, subjects are monitored for subsequent exposure to stressors as well as mental health measures, which allows for a quantitative assessment of stressor-dependent changes in mental health as the main outcome. Descriptive analyses of mental health, number of stressors including major life events, daily hassles, perceived stress, and the ability to recover from stress are here presented for the baseline sample. The LORA study is unique in its design and will pave the way for a better understanding of resilience mechanisms in humans and for further development of interventions to successfully prevent stress-related disorder.
Deep learning approaches can uncover complex patterns in data. In particular, variational autoencoders (VAEs) achieve this by a non-linear mapping of data into a low-dimensional latent space. Motivated by an application to psychological resilience in the Mainz Resilience Project (MARP), which features intermittent longitudinal measurements of stressors and mental health, we propose an approach for individualized, dynamic modeling in this latent space. Specifically, we utilize ordinary differential equations (ODEs) and develop a novel technique for obtaining person-specific ODE parameters even in settings with a rather small number of individuals and observations, incomplete data, and a differing number of observations per individual. This technique allows us to subsequently investigate individual reactions to stimuli, such as the mental health impact of stressors. A potentially large number of baseline characteristics can then be linked to this individual response by regularized regression, e.g., for identifying resilience factors. Thus, our new method provides a way of connecting different kinds of complex longitudinal and baseline measures via individualized, dynamic models. The promising results obtained in the exemplary resilience application indicate that our proposal for dynamic deep learning might also be more generally useful for other application domains.