人工神经网络用于计算认知模型中的模型识别和参数估计
Milena Rmus1, Ti-Fen Pan1, Liyu Xia2
1Department of Psychology, University of California, Berkeley, Berkeley, California, United States of America.
PLoS computational biology
|May 15, 2024
概括
这项研究引入了人工神经网络 (ANN),以适应认知模型,绕过复杂的概率计算. 这种方法可以对以前难以处理的认知模型进行定量分析,从而推进计算认知科学.
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:
- 计算认知模型正式化认知过程,并通过参数量化个体差异.
- 模型比较确定了最能解释经验数据的理论,通常依赖于概率估计.
- 许多复杂的认知模型由于计算难以处理的概率计算而未得到充分探索.
研究的目的:
- 开发一种用于拟合认知模型的新方法,可以绕过计算密集的概率估计.
- 允许对具有难以处理的概率的认知模型进行定量调查,包括那些具有试验间依赖性的认知模型.
- 为了促进参数估计和模型识别,用于更广泛的认知理论.
主要方法:
- 利用人工神经网络 (ANN) 直接将数据映射到认知模型的身份和参数.
- 绕过传统的概率估计,解决计算难以处理的问题.
- 在认知模型上测试了ANN方法,这些认知模型具有强大的试验间依赖性,例如强化学习模型.
主要成果:
- 通过ANN方法成功执行了参数估计和模型识别.
- 即使对于传统基于概率的装配方法难以处理的模型,也证明了有效性.
- 在具有挑战性的模型上验证了ANN方法,例如强化学习,这种模型因试验间依赖而闻名.
结论:
- 人工神经网络为适应复杂的认知模型提供了可行的和可访问的工具.
- 这种基于模拟的方法扩大了可接受定量分析的认知模型的范围.
- 该方法增强了测试各种认知理论和理解个人差异的能力.
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