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注意证据积累的动态变化可以解释为什么计算能力更强的人在风险下做出更好的决策。

Attentional dynamics of evidence accumulation explain why more numerate people make better decisions under risk.

机构信息

Chair for General Psychology II, Katholische Universität Eichstätt-Ingolstadt, Ostenstraße 25, 85072, Eichstätt, Germany.

Center for Adaptive Rationality, Max Planck Institute for Human Development, Lentzeallee 94, 14195, Berlin, Germany.

出版信息

Sci Rep. 2024 Aug 13;14(1):18788. doi: 10.1038/s41598-024-68969-5.

DOI:10.1038/s41598-024-68969-5
PMID:39138236
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11322310/
Abstract

In decisions under risk, more numerate people are typically more likely to choose the option with the highest expected value (EV) than less numerate ones. Prior research indicates that this finding cannot be explained by differences in the reliance on explicit EV calculation. The current work uses the attentional Drift Diffusion Model as a unified computational framework to formalize three candidate mechanisms of pre-decisional information search and processing-namely, attention allocation, amount of deliberation, and distorted processing of value-which may differ between more and less numerate people and explain differences in decision quality. Computational modeling of an eye-tracking experiment on risky choice demonstrates that numeracy is linked to how people allocate their attention across the options, how much evidence they require before committing to a choice, and also how strongly they distort currently non-attended information during preference formation. Together, especially the latter two mechanisms largely mediate the effect of numeracy on decision quality. Overall, the current work disentangles and quantifies latent aspects of the dynamics of preference formation, explicates how their interplay may give rise to manifest differences in decision quality, and thereby provides a fully formalized, mechanistic explanation for the link between numeracy and decision quality in risky choice.

摘要

在风险决策中,通常来说,算数能力更强的人比算数能力更弱的人更有可能选择预期价值(EV)最高的选项。先前的研究表明,这一发现不能用对明确 EV 计算的依赖程度的差异来解释。当前的工作使用注意漂移扩散模型作为一个统一的计算框架,将三个潜在的决策前信息搜索和处理机制形式化,即注意力分配、思考量和价值的扭曲处理,这些机制可能在算数能力强和弱的人之间存在差异,并解释决策质量的差异。对风险选择的眼动追踪实验的计算建模表明,算数能力与人们如何在选项之间分配注意力、在做出选择之前需要多少证据以及在偏好形成过程中对当前未关注信息的扭曲程度有关。总的来说,特别是后两种机制在很大程度上解释了算数能力对决策质量的影响。总的来说,本研究分解并量化了偏好形成动态的潜在方面,阐明了它们的相互作用如何导致决策质量的明显差异,从而为风险决策中算数能力和决策质量之间的联系提供了一个完全形式化的、机制化的解释。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d6da/11322310/ede55448346f/41598_2024_68969_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d6da/11322310/05e97463a920/41598_2024_68969_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d6da/11322310/5eafe8c44535/41598_2024_68969_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d6da/11322310/9dbc2e9a2f7a/41598_2024_68969_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d6da/11322310/ede55448346f/41598_2024_68969_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d6da/11322310/05e97463a920/41598_2024_68969_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d6da/11322310/5eafe8c44535/41598_2024_68969_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d6da/11322310/9dbc2e9a2f7a/41598_2024_68969_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d6da/11322310/ede55448346f/41598_2024_68969_Fig4_HTML.jpg

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