单变量
贴现
前额叶皮质
多元统计
人工神经网络
计算机科学
神经经济学
估价(财务)
人工智能
机器学习
心理学
认知心理学
认知
经济
神经科学
财务
作者
Yuan‐Wei Yao,Kunru Song,Nicolas W. Schuck,Xin Li,Xiaoyi Fang,Jintao Zhang,Hauke R. Heekeren,Rasmus Bruckner
出处
期刊:NeuroImage
[Elsevier BV]
日期:2023-08-12
卷期号:279: 120326-120326
被引量:13
标识
DOI:10.1016/j.neuroimage.2023.120326
摘要
Decisions that require taking effort costs into account are ubiquitous in real life. The neural common currency theory hypothesizes that a particular neural network integrates different costs (e.g., risk) and rewards into a common scale to facilitate value comparison. Although there has been a surge of interest in the computational and neural basis of effort-related value integration, it is still under debate if effort-based decision-making relies on a domain-general valuation network as implicated in the neural common currency theory. Therefore, we comprehensively compared effort-based and risky decision-making using a combination of computational modeling, univariate and multivariate fMRI analyses, and data from two independent studies. We found that effort-based decision-making can be best described by a power discounting model that accounts for both the discounting rate and effort sensitivity. At the neural level, multivariate decoding analyses indicated that the neural patterns of the dorsomedial prefrontal cortex (dmPFC) represented subjective value across different decision-making tasks including either effort or risk costs, although univariate signals were more diverse. These findings suggest that multivariate dmPFC patterns play a critical role in computing subjective value in a task-independent manner and thus extend the scope of the neural common currency theory.
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