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

Trapp S, Vilares I (2020)
Scientific reports 10: 21456 .

Zeitschriftenaufsatz | Veröffentlicht | Englisch
 
Download
OA 110.12 KB
Autor*in
Trapp, SabrinaUniBi; Vilares, Iris
Abstract / Bemerkung
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 as well 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.
Erscheinungsjahr
2020
Zeitschriftentitel
Scientific reports
Band
10
Art.-Nr.
21456
eISSN
2045-2322
Page URI
https://pub.uni-bielefeld.de/record/2949425

Zitieren

Trapp S, Vilares I. Bayesian decision-making under stress-preserved weighting of prior and likelihood information. Scientific reports. 2020;10: 21456 .
Trapp, S., & Vilares, I. (2020). Bayesian decision-making under stress-preserved weighting of prior and likelihood information. Scientific reports, 10, 21456 . doi:10.1038/s41598-020-76493-5
Trapp, S., and Vilares, I. (2020). Bayesian decision-making under stress-preserved weighting of prior and likelihood information. Scientific reports 10:21456 .
Trapp, S., & Vilares, I., 2020. Bayesian decision-making under stress-preserved weighting of prior and likelihood information. Scientific reports, 10: 21456 .
S. Trapp and I. Vilares, “Bayesian decision-making under stress-preserved weighting of prior and likelihood information.”, Scientific reports, vol. 10, 2020, : 21456 .
Trapp, S., Vilares, I.: Bayesian decision-making under stress-preserved weighting of prior and likelihood information. Scientific reports. 10, : 21456 (2020).
Trapp, Sabrina, and Vilares, Iris. “Bayesian decision-making under stress-preserved weighting of prior and likelihood information.”. Scientific reports 10 (2020): 21456 .
Alle Dateien verfügbar unter der/den folgenden Lizenz(en):
Creative Commons Namensnennung 4.0 International Public License (CC-BY 4.0):
Volltext(e)
Access Level
OA Open Access
Zuletzt Hochgeladen
2020-12-17T13:48:26Z
MD5 Prüfsumme
e56ac479a7734fc2d4ef6bf2ceaee749

Export

Markieren/ Markierung löschen
Markierte Publikationen

Open Data PUB

Web of Science

Dieser Datensatz im Web of Science®

Quellen

PMID: 33293616
PubMed | Europe PMC

Suchen in

Google Scholar