Identifikation von transdiagnostischen computationalen Biomarkern des Gehirns für soziale Interaktionsstörungen mittels Hyperscanning-Bildgebung
Allgemeine, Kognitive und Mathematische Psychologie
Zusammenfassung der Projektergebnisse
The aim of the fellowship was the development of a computational model of social interaction disorders based on multi-brain data. I intended to a) explain transdiagnostic social behavioural aberrations in the same framework as unremarkable behaviour and b) provide procedure to derive relevant therapeutic efforts. I proposed that human social interaction can be conceptualized as a Bayesian computation in dyadic contexts. Unimpaired decision-making results in the synchronization of social brain networks representing beliefs about the interacting other as a biological read-out of successful social contact. Social interaction difficulties in the presence of psychiatric illness can be parametrized in the Bayesian context as arising from false prior beliefs, a failure to update beliefs, incomplete use of available environmental (social) information, or a combination of these. I was successful in the extension of Bayesian dynamic causal modelling to two-brain data to identify within- and between-brain function. Directed causal relations were hypothesized in these models – i.e., from the sender to the receiver of information – and compared with models that precluded this connection through Bayesian model comparison. The approach selects the model with the highest model evidence (the accuracy of data prediction based on the model, penalized by its complexity) as best explaining the data. In further work we developed a model of decision making under stressful conditions; specifically fear exposure under different cognitive states. Here, we aimed to deepen the understanding of interactions between thoughts, feelings, and behaviors, as conceptualized by cognitive-behavioral therapy (CBT). We presented a neurocomputational (active inference) model of CBT that allows formal simulations of interactions between cognitive interventions (i.e., cognitive restructuring) and behavioral interventions (i.e., exposure) in producing adaptive behavior change (i.e., reducing maladaptive avoidance behavior). When conscious beliefs about safety/danger have strong interactions with affective/behavioral outcomes, behavioral change during exposure therapy is mediated by changes in these beliefs, preventing generalization. In contrast, when these interactions are weakened, and cognitive restructuring only induces belief uncertainty (as opposed to strong safety beliefs), behavior change leads to generalized learning (i.e., “over-writing” the implicit beliefs about action-outcome mappings that directly produce avoidance). The individual is therefore equipped to face any new context, safe or dangerous, remaining in a content state without the need for avoidance behavior – increasing resilience. Followingly, we advanced our model of decision making under mental health difficulties. Specifically, we were interested in modelling cognitive biases in the form of dichotomous beliefs about others, whereby others are not imbued with possessing mixtures of opposing properties, as commonly present in BPD. By contrast, we associate psychological health with a fine-grained representation of internal states, constrained by an integrated prior, corresponding to notions of ‘character’. In one line of work, I used this model to present an agent with dichotomous beliefs about others (“good” or “bad” people), using task data on interpersonal trust development between strangers. Additionally, we formalized these accounts as an oversimplified categorical model of others’ internal, intentional, states. We show how a resulting idealization and devaluation of others can be stabilized by attributing unexpected behavior to fictive external factors. Finally, the model predicts that extreme appraisals of self or other are associated with causal attribution errors.
Projektbezogene Publikationen (Auswahl)
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Computational mechanisms of substance use: recent evidence and its relevance to addiction medicine. Current Addiction Reports, 8(4), 509-519.
Smith, R., Taylor, S., Bilek, E
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Simulating the computational mechanisms of cognitive and behavioral psychotherapeutic interventions: Insights from active inference. Scientific Reports, 11(1), 1-16.
Smith, R., Moutoussis, M., Bilek, E.
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Directed coupling in multi-brain networks underlies generalized synchrony during social exchange. NeuroImage, 252, 119038.
Bilek, E., Zeidman, P., Kirsch, P., Tost, H., Meyer-Lindenberg, A., & Friston, K.
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Neural responses to instructed positive couple interaction: an fMRI study on compliment sharing. Social Cognitive and Affective Neuroscience, 18(1)
Eckstein, M., Stößel, G., Gerchen, M. F., Bilek, E., Kirsch, P., & Ditzen, B.
