Unknown

Dataset Information

0

Computations of uncertainty mediate acute stress responses in humans.


ABSTRACT: The effects of stress are frequently studied, yet its proximal causes remain unclear. Here we demonstrate that subjective estimates of uncertainty predict the dynamics of subjective and physiological stress responses. Subjects learned a probabilistic mapping between visual stimuli and electric shocks. Salivary cortisol confirmed that our stressor elicited changes in endocrine activity. Using a hierarchical Bayesian learning model, we quantified the relationship between the different forms of subjective task uncertainty and acute stress responses. Subjective stress, pupil diameter and skin conductance all tracked the evolution of irreducible uncertainty. We observed a coupling between emotional and somatic state, with subjective and physiological tuning to uncertainty tightly correlated. Furthermore, the uncertainty tuning of subjective and physiological stress predicted individual task performance, consistent with an adaptive role for stress in learning under uncertain threat. Our finding that stress responses are tuned to environmental uncertainty provides new insight into their generation and likely adaptive function.

SUBMITTER: de Berker AO 

PROVIDER: S-EPMC4820542 | biostudies-literature | 2016 Mar

REPOSITORIES: biostudies-literature

altmetric image

Publications

Computations of uncertainty mediate acute stress responses in humans.

de Berker Archy O AO   Rutledge Robb B RB   Mathys Christoph C   Marshall Louise L   Cross Gemma F GF   Dolan Raymond J RJ   Bestmann Sven S  

Nature communications 20160329


The effects of stress are frequently studied, yet its proximal causes remain unclear. Here we demonstrate that subjective estimates of uncertainty predict the dynamics of subjective and physiological stress responses. Subjects learned a probabilistic mapping between visual stimuli and electric shocks. Salivary cortisol confirmed that our stressor elicited changes in endocrine activity. Using a hierarchical Bayesian learning model, we quantified the relationship between the different forms of sub  ...[more]

Similar Datasets

| S-EPMC2093988 | biostudies-literature
| S-EPMC6542186 | biostudies-literature
| S-EPMC7382235 | biostudies-literature
| S-EPMC3503485 | biostudies-literature
| S-EPMC2373314 | biostudies-other
| S-EPMC2906860 | biostudies-other
| S-EPMC4679610 | biostudies-literature
| S-EPMC8493594 | biostudies-literature
| S-EPMC8275695 | biostudies-literature