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.

Unexpected arousal modulates the influence of sensory noise on confidence

“…Additionally, although here we manipulate arousal and observe correlated changes in cardiac signals and confidence, the causal link to interoception must be established in future investigations, in which cardiac signals are directly manipulated independently of the central nervous system. This approach, coupled with hierarchical psycho-physiological computational modelling (de Berker et al, 2016), could further reveal the interoceptive computations underlying perceptual confidence.…”

Pharmacological Fingerprints of Contextual Uncertainty

“…Hierarchical Bayesian models have proven powerful for explaining the adaptation of behaviour to probabilistic contexts in volatile environments [1,15,17,70,72–74,84,104,105]. Here we developed a novel instantiation of the HGF model [9] with a focus on TMs and two components: a three-level perceptual model and a response model (Fig 3A).…”

“…The HGF has been successfully applied in several recent studies of learning under volatility [15,17,70–74]. We developed a novel instantiation of the HGF with two components: a three-level perceptual model of an agent’s mapping from environmental causes to sensory inputs and a response model that maps those inferred environmental causes to observed RT responses [75].…”

Mechanisms underlying memory enhancement in humans

“…Subsequently, visual artifact rejection was performed to remove bad interpolations. Condition-specific pupil diameter modulations were analyzed using a GLM approach as implemented previously (de Berker et al, 2016). To dissociate pupil variations due to cognitive processes vs. light-evoked pupil constrictions evoked by the appearance of visual stimuli, we modeled these two response components separately.…”

“…To test for an interaction between encoding and button press in pupil diameter, the raw pupil responses ( Figure 19A) were z-scored and submitted to a general linear model (GLM) using a basis function approach. Stimulus-locked pupillary responses were modeled with two basis functions, one pertaining to the light-reflex and a later cognitive component, as recently described (de Berker et al, 2016). The Erlang gamma function suggested in (Hoeks & Levelt, 1993) was used for the later cognitive component and the earlier visual component was parameterised by fitting to the pupil responses at encoding to subsequent familiar (K) responses, as statistical inference was not made on these trials.…”