通过计算建模,评估贝叶斯的故意结合因果推理模型.
1Graduate School of Humanities and Sociology and Faculty of Letters, The University of Tokyo, Tokyo, Japan. kino31513@l.u-tokyo.ac.jp.
Scientific reports
|February 5, 2024
概括
这项研究使用计算模型来解释故意绑定,即行为与其后果之间的时间感知缩短. 贝叶斯因果推理模型比传统方法更好地解释了这一现象,表明它源于行动结果预期.
科学领域:
- 认知神经科学 认知神经科学
- 计算心理学 计算心理学
- 人与计算机的交互
背景情况:
- 意图绑定是代理的关键指标,它描述了行为与其感官后果之间的主观时间压缩.
- 潜在的精确神经和计算机制故意绑定仍然不太了解.
- 贝叶斯因果推理 (BCI) 提供了一个有希望的理论框架,但需要强大的经验验证.
研究的目的:
- 通过计算建模和定量评估潜在的故意绑定机制.
- 测试贝叶斯因果推理 (BCI) 模型在解释观察到的时间估计数据方面的有效性.
- 调查BCI在推断事件时间和因果关系方面的算法基础.
主要方法:
- 开发和实施各种用于故意绑定的计算模型.
- 将计算模型与时间估计的个人参与者数据相匹配.
- 模型性能的定量比较,包括BCI和最大概率估计 (MLE).
主要成果:
- 在解释时间估计方面,BCI模型显著超过了传统模型.
- 确定因果信念和时间预测作为有助于故意结合的关键参数.
- 估计参数表示时间压缩源于预期即时的行动后果关系.
结论:
- 计算建模,特别是BCI,为剖析故意绑定机制提供了一个强大的工具.
- 这些发现支持了行动结果预期在主观时间感知中的作用.
- 概率匹配可能是因果不确定性下的事件时间的启发性重建的基础.
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