人类行为中的继任者表示的试验性学习
Ari E Kahn1, Dani S Bassett2, Nathaniel D Daw1,3
1Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.
bioRxiv : the preprint server for biology
|July 16, 2025
概括
人类通过继承者表示 (SR) 来学习世界的预测模型. 这项研究表明,学习比以前假设的更多地依赖于资格跟踪 (SR-TD(λ)) 与近期预测一起.
科学领域:
- 认知科学 认知科学
- 神经科学是一个神经科学.
- 强化学习是一种强化学习.
背景情况:
- 生物通过模拟世界统计结构,包括行动结果来学习.
- 继承代表 (SR) 预测未来的状态在多个时间段,对于决策至关重要.
- 人类如何获得SR是未知的,之前假设的时间差异启动 (SR-TD(0)) 缺乏经验测试.
研究的目的:
- 实证地测试和比较不同的学习规则,以获得继承者表示 (SR).
- 调查符合条件的轨迹 (SR-TD(λ)) 与时间差异启动 (SR-TD(0) 在SR获取中的作用.
- 了解复杂环境中人类学习的动态.
主要方法:
- 在长时间的图表序列学习任务中利用试验对试验的反应时间.
- 检查了继任者表示时间差异学习与资格痕迹 (SR-TD(λ)) 模型.
- 与其他学习模型进行了SR-TD (λ) 的比较,包括一个结合最新性预测的混合模型.
主要成果:
- 人类在图表学习任务中的行为最好用混合模型来解释.
- 这种混合模型将SR-TD (λ) 学习与一个额外的近期预测模型相结合.
- 一个显著的 λ 值表明资格跟踪在启动过程中发挥了主要作用.
结论:
- 人类SR获取主要涉及资格跟踪机制,挑战先前的假设.
- 个人同时学习继任者表示和低级近期预测.
- 研究结果提供了对人类预测表示学习背后的机制的见解.
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