模拟感官减弱作为贝叶斯因果推理在两个数据集
Anna-Lena Eckert1, Elena Fuehrer2, Christina Schmitter3
1Department of Psychology, Theoretical Cognitive Science Group, Philipps-Universität Marburg, Marburg, Germany.
PloS one
|January 24, 2025
概括
感官衰减 (SA),即抑制自我生成的感官输入,是使用贝叶斯因果推理 (BCI) 建模的. 这种计算方法解释了大脑如何区分自我生成和外部感官信息.
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.
- 感官处理 感官处理
背景情况:
- 区分自我生成和外部感官信息对于环境相互作用至关重要.
- 与外部刺激相比,自我运动的感觉后果通常会引起减弱的神经和行为反应.
- 感官衰减 (SA) 建议发生在感官信息的内部原因被推断出来时.
研究的目的:
- 提出和验证基于贝叶斯因果推理 (BCI) 的感觉衰减 (SA) 的计算模型.
- 调查推断的内部原因在感官衰减中的作用.
- 在不同的实验范式中模拟SA的经验模式.
主要方法:
- 开发了一种序列贝叶斯因果推理 (BCI) 模型.
- 利用一个层次的马尔科夫模型 (HMM) 和变异的消息传递模拟.
- 优化了两个实验的参与者特定模型参数,涉及触觉和延迟检测任务.
主要成果:
- 在这两项实验中,BCI模型成功地捕获了感官衰减的经验模式.
- 参与者特定的模型参数显示了数据和模型预测之间的良好一致.
- 该模型准确地预测了实验1中的触觉检测和实验2中的延迟检测.
结论:
- 贝叶斯因果推理 (BCI) 为模拟人类感官衰减提供了一个强大的框架.
- 计算机模型的SA可以统一的发现跨传感模式和实验范式.
- 这种方法可以提高对精神分裂症等疾病感官处理缺陷的理解.
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