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).

中文翻译：

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实验揭示了多成分选择选项的随机偏好。


现实的日常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，只是更喜欢某些苹果而不是其他苹果。这种单维偏好关系如何涉及多成分选择选项？在这里，我们测量了随机选择如何揭示对两种成分奶昔的偏好。偏好被直观地绘制为无差异曲线，表示 2 个组件的有序整合作为权衡：放弃 1 个组件的部分以获得另一个组件的 1 个额外单位，而不改变偏好。有序、不重叠的曲线满足留一法测试，遵循机器学习解码器的预测，并与单维 Becker-DeGroot-Marschak (BDM) 类似拍卖的 2 分量奖励出价相关。这种准确性表明决策过程以系统的方式将多个奖励成分整合到单维估计中。在物种间比较中，人类的表现与经验丰富的实验室猴子的表现相匹配，这是通过捆绑组件之间关键权衡的准确性来衡量的。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学概念及其用于解释人类和非人类灵长类动物选择的方便图形方案的有效性。研究结果鼓励对正常、非理性和病态的经济选择进行正式的行为和神经研究。 （PsycInfo 数据库记录 (c) 2020 APA，保留所有权利）。