Exploring the path of persisting dysfunctional expectations—Development of the immunization scale IMS

Ewen, Anne-Catherine I.; Rief, Winfried; Wilhelm, Marcel

doi:10.3389/fpsyg.2022.1033078

Cited by 2 publications

(1 citation statement)

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“…The inability to draw "correct" conclusions from a given observation may also be the consequence of a lack of socioemotional skills, a common impairment in clinical populations (Nestor et al, 2022;Segrin, 2000;Wolkenstein et al, 2011). Many patients further connect positive interpersonal experiences to randomness rather than true benevolence (Ewen et al, 2022). In conclusion, perturbations to the agent's A matrix has the consequence that observations do not have a systematic or sensible effect on state inference.…”

Section: Transdiagnostic Processesmentioning

confidence: 99%

Active Inference of Interpersonal Trust Within and Across Mental Disorders

Eckert,

Pawlowski,

Rief

et al. 2023

Preprint

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Background. Interpersonal problems are common in mental disorders like depression and social anx-iety disorder. However, there is a lack of formal models to explain idiosyncrasies in patients' inter-personal functioning. Following modern computational accounts of perception and action, interper-sonal experience and behavior results from implicit inference about hidden environmental properties.Methods. We simulate interpersonal decision-making in a “trust game” using agent-based modelling. Agents decide to either keep or invest an initial wager to a trustee. If the agents invests, the trustee can cooperate with or exploit the agent. Agents hold a generative model of the environment and perform active inference of the context (cooperative vs. hostile). First, we simulate agents with in-creased uncertainty, fatalistic expectations, loss aversion, or pessimism (transdiagnostic biases). We further simulate depression and social anxiety as a combination of transdiagnostic biases. Results. Agents with biased expectations or preferences showed idiosyncratic interpersonal inference. For example, they more readily infer to be in a hostile context and avoid interpersonal interactions. Biases also affected the total earned rewards, with socially anxious and depressed agents receiving higher average rewards in hostile contexts compared to "healthy" agents (i.e., agents with an accu-rate-to-optimistic generative model), but “healthy” agents showing superior performance in volatile environments. Discussion. Our simulations formalize complex interpersonal phenomena within active inference. Interpersonal biases in mental disorders can be formalized and investigated with respect to their functional role. The model has potential applications in psychopathological research, personalized diagnostics, and individualized treatment planning. Further empirical work is necessary to validate the model.

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