贝叶斯式惊喜预测人类事件细分在故事听力
Manoj Kumar1, Ariel Goldstein2,3, Sebastian Michelmann1
1Princeton Neuroscience Institute, Princeton University.
Cognitive science
|October 23, 2023
概括
事件细分理论表明预测错误信号事件边界. 这项研究发现贝叶斯式惊喜,而不是简单的惊喜,与在听故事期间的人类事件细分有关.
科学领域:
- 认知心理学 认知心理学
- 计算神经科学是一种计算神经科学.
- 自然语言处理自然语言处理.
背景情况:
- 事件细分理论提出,大预测错误标志着离散体验之间的过渡.
- 了解事件细分的神经和计算机制对于认知科学至关重要.
研究的目的:
- 使用计算模型在听故事时测试事件细分理论.
- 调查预测错误指标和人类识别的事件边界之间的关系.
主要方法:
- 利用GPT-2语言模型计算三个故事的词级预测错误时间序列.
- 收集了人类参与者的事件边界标记,同时他们听到相同的故事.
- 使用回归模型将计算预测错误测量与人类细分数据相关联.
主要成果:
- 事件边界与贝叶斯惊喜的短暂增加有显著的相关性.
- 一个更简单的预测错误测量,surprisal,没有显示与事件边界的显著关联.
- 证明了在认知细分中预测错误的不同操作定义之间的区别.
结论:
- 研究结果支持预测错误作为事件细分的关键机制的作用.
- 贝叶斯惊喜作为人类事件边界检测的更相关的计算相关物而不是惊喜.
- 强调了在认知研究中精确定义和测量预测误差的重要性.
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