Stochastic satisficing account of confidence in uncertain value-based decisions.

Every day we make choices under uncertainty; choosing what route to work or which queue in a supermarket to take, for example. It is unclear how outcome variance, e.g. uncertainty about waiting time in a queue, affects decisions and confidence when outcome is stochastic and continuous. How does one...

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Main Authors: Uri Hertz, Bahador Bahrami, Mehdi Keramati
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2018-01-01
Series:PLoS ONE
Online Access:http://europepmc.org/articles/PMC5886535?pdf=render
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spelling doaj-835b53b4456c4948b79e0ea8f7c8e5952020-11-25T02:08:05ZengPublic Library of Science (PLoS)PLoS ONE1932-62032018-01-01134e019539910.1371/journal.pone.0195399Stochastic satisficing account of confidence in uncertain value-based decisions.Uri HertzBahador BahramiMehdi KeramatiEvery day we make choices under uncertainty; choosing what route to work or which queue in a supermarket to take, for example. It is unclear how outcome variance, e.g. uncertainty about waiting time in a queue, affects decisions and confidence when outcome is stochastic and continuous. How does one evaluate and choose between an option with unreliable but high expected reward, and an option with more certain but lower expected reward? Here we used an experimental design where two choices' payoffs took continuous values, to examine the effect of outcome variance on decision and confidence. We found that our participants' probability of choosing the good (high expected reward) option decreased when the good or the bad options' payoffs were more variable. Their confidence ratings were affected by outcome variability, but only when choosing the good option. Unlike perceptual detection tasks, confidence ratings correlated only weakly with decisions' time, but correlated with the consistency of trial-by-trial choices. Inspired by the satisficing heuristic, we propose a "stochastic satisficing" (SSAT) model for evaluating options with continuous uncertain outcomes. In this model, options are evaluated by their probability of exceeding an acceptability threshold, and confidence reports scale with the chosen option's thus-defined satisficing probability. Participants' decisions were best explained by an expected reward model, while the SSAT model provided the best prediction of decision confidence. We further tested and verified the predictions of this model in a second experiment. Our model and experimental results generalize the models of metacognition from perceptual detection tasks to continuous-value based decisions. Finally, we discuss how the stochastic satisficing account of decision confidence serves psychological and social purposes associated with the evaluation, communication and justification of decision-making.http://europepmc.org/articles/PMC5886535?pdf=render
collection DOAJ
language English
format Article
sources DOAJ
author Uri Hertz
Bahador Bahrami
Mehdi Keramati
spellingShingle Uri Hertz
Bahador Bahrami
Mehdi Keramati
Stochastic satisficing account of confidence in uncertain value-based decisions.
PLoS ONE
author_facet Uri Hertz
Bahador Bahrami
Mehdi Keramati
author_sort Uri Hertz
title Stochastic satisficing account of confidence in uncertain value-based decisions.
title_short Stochastic satisficing account of confidence in uncertain value-based decisions.
title_full Stochastic satisficing account of confidence in uncertain value-based decisions.
title_fullStr Stochastic satisficing account of confidence in uncertain value-based decisions.
title_full_unstemmed Stochastic satisficing account of confidence in uncertain value-based decisions.
title_sort stochastic satisficing account of confidence in uncertain value-based decisions.
publisher Public Library of Science (PLoS)
series PLoS ONE
issn 1932-6203
publishDate 2018-01-01
description Every day we make choices under uncertainty; choosing what route to work or which queue in a supermarket to take, for example. It is unclear how outcome variance, e.g. uncertainty about waiting time in a queue, affects decisions and confidence when outcome is stochastic and continuous. How does one evaluate and choose between an option with unreliable but high expected reward, and an option with more certain but lower expected reward? Here we used an experimental design where two choices' payoffs took continuous values, to examine the effect of outcome variance on decision and confidence. We found that our participants' probability of choosing the good (high expected reward) option decreased when the good or the bad options' payoffs were more variable. Their confidence ratings were affected by outcome variability, but only when choosing the good option. Unlike perceptual detection tasks, confidence ratings correlated only weakly with decisions' time, but correlated with the consistency of trial-by-trial choices. Inspired by the satisficing heuristic, we propose a "stochastic satisficing" (SSAT) model for evaluating options with continuous uncertain outcomes. In this model, options are evaluated by their probability of exceeding an acceptability threshold, and confidence reports scale with the chosen option's thus-defined satisficing probability. Participants' decisions were best explained by an expected reward model, while the SSAT model provided the best prediction of decision confidence. We further tested and verified the predictions of this model in a second experiment. Our model and experimental results generalize the models of metacognition from perceptual detection tasks to continuous-value based decisions. Finally, we discuss how the stochastic satisficing account of decision confidence serves psychological and social purposes associated with the evaluation, communication and justification of decision-making.
url http://europepmc.org/articles/PMC5886535?pdf=render
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