在人类规模上建模人类活动理解:预测,细分和分类.
Tan T Nguyen1, Matthew A Bezdek1, Samuel J Gershman2
1Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO 63130, USA.
PNAS nexus
|October 24, 2024
概括
这项研究开发了一个计算模型,通过学习事件方案来模拟人类事件细分. 模型 模型的模型
科学领域:
- 认知科学 认知科学
- 计算神经科学是一种神经科学.
- 人工智能的人工智能
背景情况:
- 人类事件理解包括将连续活动细分为离散事件.
- 事件细分和模型切换背后的认知机制尚未完全理解.
- 现有的模型往往缺乏学习事件模式或模拟人类类型细分的能力.
研究的目的:
- 开发一种能够学习事件模式并预测人类活动的计算模型.
- 调查事件模型在人类类细分中过渡的计算机制.
- 为了比较预测不确定性和预测错误作为事件细分的触发因素.
主要方法:
- 构建了一个混合循环神经网络和贝叶斯推理架构.
- 在自然主义的人类活动数据上训练模型.
- 对人类细分和分类数据进行评估模型性能.
主要成果:
- 该模型学会了通过类似人类的细分和分类性能来预测人类活动.
- 预测不确定性和预测错误变体都学会了对事件进行细分和分类.
- 预测不确定性变体显示出与人类细分和分类更接近的匹配.
结论:
- 计算模型可以学习事件模式,并模拟人类事件细分.
- 基于预测不确定性或错误的事件模型过渡是人类事件理解的合理机制.
- 这些发现表明,预测不确定性是人类事件细分的一个关键因素.
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