用神经网络估计器识别认知模型的潜变序列
Ti-Fen Pan1, Jing-Jing Li2, Bill Thompson3
1Department of Psychology, University of California, Berkeley, USA. tfpan@berkeley.edu.
Behavior research methods
|August 28, 2025
概括
这项研究引入了一种基于模拟的新方法,使用循环神经网络从认知模型中提取动态潜变量,即使是那些具有复杂,难以处理的可能性,从而推进认知过程研究.
科学领域:
- 认知科学
- 计算神经科学
- 机器学习
背景情况:
- 提取时间变化的潜变量对于理解动态认知过程至关重要.
- 目前的方法仅限于特定的认知模型,
研究的目的:
- 开发一种基于模拟的方法,使用循环神经网络 (RNN) 来推断潜在的变量序列.
- 克服现有的认知模型的局限性.
- 能够更广泛地探索计算认知模型.
主要方法:
- 一种基于模拟的方法,利用循环神经网络 (RNN).
- 将实验数据直接映射到潜变量空间.
- 使用模拟数据进行培训和验证.
主要成果:
- 在模拟中实现了潜在变量序列的竞争性性能.
- 在现实数据集上证明了适用性.
- 这种方法对于个别数据是实用的,可以概括,并且可以适应连续/离散的潜空间.
结论:
- 提出的方法扩大了研究人员可以分析的认知模型范围.
- 通过在复杂模型中进行推断,方便测试更广泛的认知理论.
- 结合RNN和模拟数据进行可靠的潜变量提取.
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