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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).

Original publication

DOI

10.1037/xan0000269

Type

Journal article

Journal

J Exp Psychol Anim Learn Cogn

Publication Date

10/2020

Volume

46

Pages

367 - 384

Keywords

Adult, Animals, Behavior, Animal, Choice Behavior, Economics, Behavioral, Female, Humans, Macaca mulatta, Machine Learning, Magnetic Resonance Imaging, Male, Psychophysics, Reward, Species Specificity, Young Adult