Lifetime stressor exposure is associated with altered trust formation and social learning in dynamic interactions
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Updated: Nov 4, 2026

A Modified Trier Social Stress Test for Vulnerable Mexican American Adolescents
Published on: July 10, 2017
Jonas P Nitschke1, Nace Mikus1,2, Livia Scanferla1,3
1Department of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of Vienna, Vienna 1010, Austria.
Abstract:
Although trust is fundamental to social interactions, how lifetime stressor exposure relates to the cognitive processes underlying trust remains poorly understood. Using the Stress and Adversity Inventory (STRAIN) to assess cumulative lifetime stressor exposure, combined with an adaptation of the repeated trust game and computational modeling (Hierarchical Gaussian Filtering), we investigated how stressors experienced over the life course relate to three core aspects of trust dynamics-initial trust formation, learning about others' intentions, and adaptation to changes in trustworthiness-in 168 adults. Greater lifetime stress was associated with stronger negativity biases, lower initial trust, and heightened uncertainty about others' intentions, increases that were general rather than specific to partner type. These more pessimistic priors were not accompanied by poorer reading of partners: Higher lifetime stress was associated with greater differentiation between prosocial and selfish trustees, driven by reduced investment in the selfish trustee alone. Computational analyses further indicated greater belief volatility, consistent with faster model-implied updating of perceived trustworthiness. These findings provide novel evidence that lifetime stress is associated with a profile consistent with recalibration, rather than a degradation, of the cognitive mechanisms underlying trust, with potential consequences for relationship formation and social functioning. They also provide a computationally grounded framework for understanding associations between adversity and social cognition and generate testable predictions for future experimental work.
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