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.
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.