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

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....

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Sabrina Trapp, Iris Vilares
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/7ba0ab0f234d463b9c264b7978fbcdb5
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:7ba0ab0f234d463b9c264b7978fbcdb5
record_format	dspace
spelling	oai:doaj.org-article:7ba0ab0f234d463b9c264b7978fbcdb52021-12-02T12:33:04ZBayesian decision-making under stress-preserved weighting of prior and likelihood information10.1038/s41598-020-76493-52045-2322https://doaj.org/article/7ba0ab0f234d463b9c264b7978fbcdb52020-12-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-76493-5https://doaj.org/toc/2045-2322Abstract 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.Sabrina TrappIris VilaresNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-11 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Sabrina Trapp Iris Vilares Bayesian decision-making under stress-preserved weighting of prior and likelihood information
description	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 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.
format	article
author	Sabrina Trapp Iris Vilares
author_facet	Sabrina Trapp Iris Vilares
author_sort	Sabrina Trapp
title	Bayesian decision-making under stress-preserved weighting of prior and likelihood information
title_short	Bayesian decision-making under stress-preserved weighting of prior and likelihood information
title_full	Bayesian decision-making under stress-preserved weighting of prior and likelihood information
title_fullStr	Bayesian decision-making under stress-preserved weighting of prior and likelihood information
title_full_unstemmed	Bayesian decision-making under stress-preserved weighting of prior and likelihood information
title_sort	bayesian decision-making under stress-preserved weighting of prior and likelihood information
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/7ba0ab0f234d463b9c264b7978fbcdb5
work_keys_str_mv	AT sabrinatrapp bayesiandecisionmakingunderstresspreservedweightingofpriorandlikelihoodinformation AT irisvilares bayesiandecisionmakingunderstresspreservedweightingofpriorandlikelihoodinformation
_version_	1718393867154227200

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

Ejemplares similares