The standard relationship between choice frequency and choice time is violated in multi-attribute preferential choice

IF 2.2 4区 心理学 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Journal of Mathematical Psychology Pub Date : 2023-08-01 DOI:10.1016/j.jmp.2023.102775
Guy E. Hawkins, Gavin Cooper, Jon-Paul Cavallaro
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引用次数: 1

Abstract

Many decision making theories assume a principle of sequentially sampling decision-relevant evidence from the stimulus environment, where sampled evidence is dynamically accumulated toward a threshold to trigger a decision in favour of the threshold-crossing option. A core prediction of sequential sampling models is that options more likely to be chosen are chosen more quickly. This result has been empirically supported hundreds of times for low-level speeded perceptual decisions — the traditional domain of sequential sampling models. More recently, sequential sampling models have been generalised and applied to higher-level preferential, or value-based, decisions — decisions for which there is no objectively correct option. Preferential options are typically composed of multiple attributes, like a phone defined by its price, camera quality, memory capacity, and so on. Here, we show that decisions for such multi-attribute preferential options with defined features violate the core prediction of sequential sampling models: options more likely to be chosen are not chosen more quickly. We find this invariance across 4 data sets spanning multi-attribute choices made in unconstrained conditions, under time pressure, and for multi-attribute options with artificial or marketplace compositions. The result remains whether the relationship between choice frequency and choice time is inspected at the lower level of component attributes or the higher level of whole options. Our finding places critical constraints on the capacity to generalise sequential sampling models from low-level perceptual decisions to high-level multi-attribute preferential choice.

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多属性优先选择违反了选择频率与选择时间的标准关系
许多决策理论假设从刺激环境中顺序采样决策相关证据的原则,其中采样证据动态地积累到一个阈值,以触发有利于阈值跨越选项的决策。顺序抽样模型的一个核心预测是,更有可能被选择的选项被更快地选择出来。这一结果已经在传统的顺序采样模型领域——低速度感知决策中得到了数百次的实证支持。最近,顺序抽样模型已被推广并应用于更高级别的优先或基于价值的决策-这些决策在客观上没有正确的选择。优先选项通常由多个属性组成,比如由价格、相机质量、内存容量等定义的手机。在这里,我们证明了这种具有定义特征的多属性优先选项的决策违反了顺序采样模型的核心预测:更有可能被选择的选项并没有更快地被选择。我们在4个数据集中发现了这种不变性,这些数据集跨越了在无约束条件下、在时间压力下做出的多属性选择,以及人工或市场组合的多属性选择。结果取决于选择频率和选择时间之间的关系是在组件属性的较低层次上检查还是在整个选项的较高层次上检查。我们的发现对将顺序抽样模型从低级感知决策推广到高级多属性优先选择的能力提出了关键限制。
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来源期刊
Journal of Mathematical Psychology
Journal of Mathematical Psychology 医学-数学跨学科应用
CiteScore
3.70
自引率
11.10%
发文量
37
审稿时长
20.2 weeks
期刊介绍: The Journal of Mathematical Psychology includes articles, monographs and reviews, notes and commentaries, and book reviews in all areas of mathematical psychology. Empirical and theoretical contributions are equally welcome. Areas of special interest include, but are not limited to, fundamental measurement and psychological process models, such as those based upon neural network or information processing concepts. A partial listing of substantive areas covered include sensation and perception, psychophysics, learning and memory, problem solving, judgment and decision-making, and motivation. The Journal of Mathematical Psychology is affiliated with the Society for Mathematical Psychology. Research Areas include: • Models for sensation and perception, learning, memory and thinking • Fundamental measurement and scaling • Decision making • Neural modeling and networks • Psychophysics and signal detection • Neuropsychological theories • Psycholinguistics • Motivational dynamics • Animal behavior • Psychometric theory
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