Bayesian decision-making under stress-preserved weighting of prior and likelihood information

Sabrina Trapp, Iris Vilares

Research output: Contribution to journalArticlepeer-review

Abstract

A rich body of empirical work has addressed the question of how stress changes the way we memorize, learn, and make high-level decisions in complex scenarios. There is evidence that stress also changes the way we perceive the world, indicating influences on decision-making at lower levels. Surprisingly, as of yet, little research has been conducted in this domain. A few studies suggest that under stress, humans tend to eschew existing knowledge, and instead focus on novel input or information from bottom-up. Decision-making in the perceptual domain has been modeled with Bayesian frameworks. Here, existing knowledge about structures and statistics of our environment is referred to as prior, whereas sensory data are termed likelihood. In this study, we directly assessed whether stress, as induced by the socially evaluated cold pressure task (SECPT), would modulate low-level decisions, specifically the weight given to sensory information, and how people reacted to changes in prior and sensory uncertainty. We found that while the stress-inducing procedure successfully elicited subjective stress ratings aswell as stress relevant physiological paramters, it did not change participants’ average reliance on sensory information. Furthermore, it did not affect participants’ sensitivity to changes in prior and sensory uncertainty, with both groups able to detect it and modulate their behavior accordingly, in a way predicted by Bayesian statistics. Our results suggest that, contrary to our predictions, stress may not directly affect lower-level sensory-motor decisions. We discuss the findings in context of time scales of the stress reaction, linked to different neural and functional consequences.

Original languageEnglish (US)
Article number21456
JournalScientific reports
Volume10
Issue number1
DOIs
StatePublished - Dec 2020

Bibliographical note

Publisher Copyright:
© 2020, The Author(s).

PubMed: MeSH publication types

  • Journal Article
  • Research Support, Non-U.S. Gov't

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