一个活跃的推理模型的表感知和冲动移动到音乐的冲动
Tomas E Matthews1, Peter Vuust1,2, Jonathan Cannon3
1Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark.
Annals of the New York Academy of Sciences
|December 9, 2025
概括
动向节奏的冲动源于减少预测错误. 按照节奏移动,使用主动推理,有助于最小化这些错误,解释为什么我们觉得有必要跳舞.
科学领域:
- 认知神经科学 认知神经科学
- 计算式听觉神经科学 计算式听觉神经科学
- 音乐的心理学 音乐的心理学
背景情况:
- 想要按照特定的节奏移动的现象得到了充分的证据,但缺乏明确的机械解释.
- 预测处理理论提出,预测错误驱动了这种冲动,但仍然未被具体说明.
研究的目的:
- 使用贝叶斯模型和主动推理来运行预测处理帐户.
- 调查预测错误和感官反在冲动移动到节奏刺激中的作用.
主要方法:
- 开发了一个贝叶斯模型来推断节奏模板 (计量与未计量).
- 整合了一个主动推理规则,其中移动可以减少预测错误.
- 计算了surprisal (预测错误) 带有和没有计量仪,分析了delta surprisal.
主要成果:
- 惊喜随着节奏的复杂性线性增加.
- 德尔塔惊喜反映了以前的动作冲动评级,在在线研究中得到证实.
- 移动的冲动与通过运动反来减少基于仪表的预测错误的潜力相关.
结论:
- 转向音乐的冲动是由通过主动推理来减少基于仪表的预测错误的潜力解释的.
- 这项研究通过突出音乐体验中的积极推断来更新预测处理账户.
- 积极推断和基于预测的学习在我们如何与音乐节奏互动方面发挥着关键作用.
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