在主动推理框架下,人类决策中的新奇性和变异性的神经相关性
Shuo Zhang1,2, Yan Tian1, Quanying Liu2
1Centre for Cognitive and Brain Sciences and Department of Psychology, University of Macau, Macau, China.
eLife
|March 21, 2025
概括
积极推断通过最小化自由能源来解释勘探-开采权衡. 这项研究发现了神经证据,支持在不确定性下决策的积极推断.
科学领域:
- 认知神经科学 认知神经科学
- 计算精神病学是一种计算精神病学.
背景情况:
- 积极推断统一了感知,决策和学习.
- 它提供了一个平衡勘探和开采的框架,通过尽量减少预期的自由能源.
研究的目的:
- 研究大脑如何在主动推理框架内表示和解决价值和不确定性.
- 探索预期的自由能量和决策不确定性的神经相关性.
主要方法:
- 25名参与者在接受电脑电图 (EEG) 记录的同时完成了上下文双臂强盗任务.
- 通过比较积极推断和强化学习模型的模型证据来分析行为数据.
- 在传感器和源级别分析了EEG数据.
主要成果:
- 与强化学习模型相比,主动推理模型在新性和可变性下更好地解释了人类决策.
- 脑电图分析显示了新奇性 (正面,中部,顶部) 和变异性 (正面,中部) 的明显神经相关性.
- 预期的自由能量被编码在额头极和中额头环,在重叠的大脑区域中表示不确定性.
结论:
- 该研究提供了行为和神经证据,支持主动推断作为人类决策过程的模型.
- 它成功地将预期的自由能量和不确定性在主动推理框架内的神经编码分离出来.
- 研究结果提供了关于在不确定性条件下做出的决定背后的神经机制的见解.
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