加权
心理学
认知
过程跟踪
累积前景理论
集合(抽象数据类型)
偏爱
认知心理学
计算机科学
统计
数学
神经科学
医学
政治
政治学
法学
放射科
程序设计语言
作者
Veronika Zilker,Thorsten Pachur
出处
期刊:Cognition
[Elsevier]
日期:2023-07-01
卷期号:236: 105441-105441
被引量:1
标识
DOI:10.1016/j.cognition.2023.105441
摘要
Probability weighting is one of the most powerful theoretical constructs in descriptive models of risky choice and constitutes a central component of cumulative prospect theory (CPT). Probability weighting has been shown to be related to two facets of attention allocation: one analysis showed that differences in the shape of CPT's probability-weighting function are linked to differences in how attention is allocated across attributes (i.e., probabilities vs. outcomes); another analysis (that used a different measure of attention) showed a link between probability weighting and differences in how attention is allocated across options. However, the relationship between these two links is unclear. We investigate to what extent attribute attention and option attention independently contribute to probability weighting. Reanalyzing data from a process-tracing study, we first demonstrate links between probability weighting and both attribute attention and option attention within the same data set and the same measure of attention. We then find that attribute attention and option attention are at best weakly related and have independent and distinct effects on probability weighting. Moreover, deviations from linear weighting mainly emerged when attribute attention or option attention were imbalanced. Our analyses enrich the understanding of the cognitive underpinnings of preferences and illustrate that similar probability-weighting patterns can be associated with very different attentional policies. This complicates an unambiguous psychological interpretation of psycho-economic functions. Our findings indicate that cognitive process models of decision making should aim to concurrently account for the effects of different facets of attention allocation on preference. In addition, we argue that the origins of biases in attribute attention and option attention need to be better understood.
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