Negative affect-driven impulsivity as hierarchical model-based overgeneralization.

IF 16.7 1区 心理学 Q1 BEHAVIORAL SCIENCES Trends in Cognitive Sciences Pub Date : 2025-02-06 DOI:10.1016/j.tics.2025.01.002
Aysenur Okan, Michael N Hallquist
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引用次数: 0

Abstract

'If your mouth is burned by milk, you blow before you eat yogurt' ('Sütten ağzı yanan yoğurdu üfleyerek yer'). This Turkish proverb advises caution based on past experiences when similar situations are encountered. However, although we may infer similarities across experiences, each situation is a complex combination of many features, and generalizing across situations based on perceived similarities may not achieve desired outcomes when obtaining them depends on more subtle or overlooked features. Here, we examine how models of generalization can uncover the model-based (MB) processes underlying reactive and rigid behaviors traditionally considered model-free (MF). Our novel conceptualization suggests that emotionally driven impulsive behaviors stem from a propensity to overgeneralize based on surface-level similarities, hindering the incorporation of other informative, discriminant cues.

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来源期刊
Trends in Cognitive Sciences
Trends in Cognitive Sciences 医学-行为科学
CiteScore
27.90
自引率
1.50%
发文量
156
审稿时长
6-12 weeks
期刊介绍: Essential reading for those working directly in the cognitive sciences or in related specialist areas, Trends in Cognitive Sciences provides an instant overview of current thinking for scientists, students and teachers who want to keep up with the latest developments in the cognitive sciences. The journal brings together research in psychology, artificial intelligence, linguistics, philosophy, computer science and neuroscience. Trends in Cognitive Sciences provides a platform for the interaction of these disciplines and the evolution of cognitive science as an independent field of study.
期刊最新文献
Computational rationality and developmental neurodivergence. Negative affect-driven impulsivity as hierarchical model-based overgeneralization. Choice overload and its consequences for animal decision-making. Large-scale interactions in predictive processing: oscillatory versus transient dynamics. Using precision approaches to improve brain-behavior prediction.
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