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Computational and behavioral markers of model‐based decision making in childhood.

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Title: Computational and behavioral markers of model‐based decision making in childhood.
Authors: Smid, Claire R. (AUTHOR), Kool, Wouter (AUTHOR), Hauser, Tobias U. (AUTHOR), Steinbeis, Nikolaus (AUTHOR)
Source: Developmental Science. Mar2023, Vol. 26 Issue 2, p1-14. 14p.
Subjects: Decision making in children, Model-based reasoning, Reasoning in children, Decision making
Abstract: Human decision‐making is underpinned by distinct systems that differ in flexibility and associated cognitive cost. A widely accepted dichotomy distinguishes between a cheap but rigid model‐free system and a flexible but costly model‐based system. Typically, humans use a hybrid of both types of decision‐making depending on environmental demands. However, children's use of a model‐based system during decision‐making has not yet been shown. While prior developmental work has identified simple building blocks of model‐based reasoning in young children (1–4 years old), there has been little evidence of this complex cognitive system influencing behavior before adolescence. Here, by using a modified task to make engagement in cognitively costly strategies more rewarding, we show that children aged 5–11‐years (N = 85), including the youngest children, displayed multiple indicators of model‐based decision making, and that the degree of its use increased throughout childhood. Unlike adults (N = 24), however, children did not display adaptive arbitration between model‐free and model‐based decision‐making. Our results demonstrate that throughout childhood, children can engage in highly sophisticated and costly decision‐making strategies. However, the flexible arbitration between decision‐making strategies might be a critically late‐developing component in human development. [ABSTRACT FROM AUTHOR]
Copyright of Developmental Science is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Computational and behavioral markers of model‐based decision making in childhood.
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  Data: Human decision‐making is underpinned by distinct systems that differ in flexibility and associated cognitive cost. A widely accepted dichotomy distinguishes between a cheap but rigid model‐free system and a flexible but costly model‐based system. Typically, humans use a hybrid of both types of decision‐making depending on environmental demands. However, children's use of a model‐based system during decision‐making has not yet been shown. While prior developmental work has identified simple building blocks of model‐based reasoning in young children (1–4 years old), there has been little evidence of this complex cognitive system influencing behavior before adolescence. Here, by using a modified task to make engagement in cognitively costly strategies more rewarding, we show that children aged 5–11‐years (N = 85), including the youngest children, displayed multiple indicators of model‐based decision making, and that the degree of its use increased throughout childhood. Unlike adults (N = 24), however, children did not display adaptive arbitration between model‐free and model‐based decision‐making. Our results demonstrate that throughout childhood, children can engage in highly sophisticated and costly decision‐making strategies. However, the flexible arbitration between decision‐making strategies might be a critically late‐developing component in human development. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Developmental Science is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1111/desc.13295
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        Text: English
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              Text: Mar2023
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              Y: 2023
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