一个基于扩散的框架,用于建模系统的,时间变化的认知过程
Manikya Alister1, Nathan J Evans2
1Melbourne School of Psychological Sciences, University of Melbourne.
Psychological review
|January 8, 2026
概括
认知过程模型通常假定常数参数,但心理状态随着时间的推移而变化. 我们的新ParAcT-DDM框架考虑了这些时间变化的参数,提高了决策研究的准确性.
科学领域:
- 认知心理学 认知心理学
- 计算神经科学是一种神经科学.
- 决策科学 决策科学 决策科学
背景情况:
- 认知模型通常使用静态参数,无法捕捉任务期间的动态心理变化.
- 练习,学习和无聊会随着时间的推移系统地改变认知状态.
- 现有的模型没有充分地将时间动态纳入决策框架.
研究的目的:
- 引入和验证参数跨时间扩散决策模型 (ParAcT-DDM) 框架.
- 模拟扩散决策模型参数 (漂移率和值) 的时间变化.
- 通过使用实证数据,评估ParAcT-DDM与标准扩散模型的性能.
主要方法:
- 开发了ParAcT-DDM,允许扩散模型参数随时间变化 (试验或区块变化).
- 专注于模拟漂移率 (效率) 和值 (谨慎) 的变化.
- 在四个现有数据集上实证测试ParAcT-DDM变体,包括实验前实践数据.
主要成果:
- 在所有测试的数据集中,ParAcT-DDM变体的表现明显优于标准扩散模型.
- 有证据表明,即使在旨在最小化实践效应的实验中,时间变化的认知过程也会发生.
- 标准扩散模型在存在时间变化的过程时产生偏差的参数估计.
结论:
- ParAcT-DDM框架提供了一种更强大的方法,通过结合时间动态来建模认知过程.
- 考虑时间变化的参数对于认知和决策研究中的准确推断至关重要.
- ParAcT-DDM通过解决未建模的时间变化引起的系统偏差来提高认知建模的可靠性.
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