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实验揭示了对多成分选择选项的随机偏好。

Experimentally revealed stochastic preferences for multicomponent choice options.

机构信息

Department of Physiology, Development and Neuroscience.

出版信息

J Exp Psychol Anim Learn Cogn. 2020 Oct;46(4):367-384. doi: 10.1037/xan0000269. Epub 2020 Jul 27.

DOI:10.1037/xan0000269
PMID:32718155
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7547871/
Abstract

Realistic, everyday rewards contain multiple components. An apple has taste and size. However, we choose in single dimensions, simply preferring some apples to others. How can such single-dimensional preference relationships refer to multicomponent choice options? Here, we measured how stochastic choices revealed preferences for 2-component milkshakes. The preferences were intuitively graphed as indifference curves that represented the orderly integration of the 2 components as trade-off: parts of 1 component were given up for obtaining 1 additional unit of the other component without a change in preference. The well-ordered, nonoverlapping curves satisfied leave-one-out tests, followed predictions by machine learning decoders and correlated with single-dimensional Becker-DeGroot-Marschak (BDM) auction-like bids for the 2-component rewards. This accuracy suggests a decision process that integrates multiple reward components into single-dimensional estimates in a systematic fashion. In interspecies comparisons, human performance matched that of highly experienced laboratory monkeys, as measured by accuracy of the critical trade-off between bundle components. These data describe the nature of choices of multicomponent choice options and attest to the validity of the rigorous economic concepts and their convenient graphic schemes for explaining choices of human and nonhuman primates. The results encourage formal behavioral and neural investigations of normal, irrational, and pathological economic choices. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

摘要

现实生活中的奖励包含多个组成部分。苹果有味道和大小。然而,我们在单一维度上进行选择,只是简单地更喜欢某些苹果而不是其他苹果。那么,这种单一维度的偏好关系如何与多成分选择选项相关联呢?在这里,我们测量了随机选择如何揭示对 2 成分奶昔的偏好。偏好直观地绘制成无差异曲线,代表了 2 个组成部分的有序整合作为权衡:放弃 1 个组成部分的部分,以获得另 1 个组成部分的额外单位,而不会改变偏好。有序、不重叠的曲线满足逐个排除测试,遵循机器学习解码器的预测,并与 2 成分奖励的单维度 Becker-DeGroot-Marschak(BDM)拍卖式出价相关。这种准确性表明,决策过程以系统的方式将多个奖励组成部分整合到单维度估计中。在种间比较中,人类的表现与经验丰富的实验室猴子相匹配,这可以通过捆绑成分之间关键权衡的准确性来衡量。这些数据描述了多成分选择选项的选择性质,并证明了严格的经济概念及其方便的图形方案对于解释人类和非人类灵长类动物的选择是有效的。结果鼓励对正常、非理性和病理性经济选择进行正式的行为和神经学研究。(PsycInfo 数据库记录(c)2020 APA,保留所有权利)。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a0ed/7547871/3cc44ed01a4e/xan_46_4_367_fig6a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a0ed/7547871/be0a0433c07d/xan_46_4_367_fig1a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a0ed/7547871/79052b58205f/xan_46_4_367_fig2a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a0ed/7547871/b42a999db00e/xan_46_4_367_fig3a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a0ed/7547871/07a6a4351fc0/xan_46_4_367_fig4a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a0ed/7547871/c21122254099/xan_46_4_367_fig5a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a0ed/7547871/3cc44ed01a4e/xan_46_4_367_fig6a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a0ed/7547871/be0a0433c07d/xan_46_4_367_fig1a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a0ed/7547871/79052b58205f/xan_46_4_367_fig2a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a0ed/7547871/b42a999db00e/xan_46_4_367_fig3a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a0ed/7547871/07a6a4351fc0/xan_46_4_367_fig4a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a0ed/7547871/c21122254099/xan_46_4_367_fig5a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a0ed/7547871/3cc44ed01a4e/xan_46_4_367_fig6a.jpg

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The neural computation of inconsistent choice behavior.不一致选择行为的神经计算。
Nat Commun. 2019 Apr 5;10(1):1583. doi: 10.1038/s41467-019-09343-2.
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A Critical Role for Human Ventromedial Frontal Lobe in Value Comparison of Complex Objects Based on Attribute Configuration.
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