扩散模型的漂移率参数主要反映了证据积累的效率,而不是速度
Alexander Weigard1,2, M Fiona Molloy3, Chandra Sripada3,4,5
1Department of Psychiatry, University of Michigan, Rachel Upjohn Building, 4250 Plymouth Road, Ann Arbor, MI, 48109, USA. asweigar@med.umich.edu.
Psychonomic bulletin & review
|February 26, 2026
概括
扩散决策模型漂移率 (v) 参数的个体差异反映了证据积累的效率,而不是速度. 这挑战了将v等同于传统的处理速度,并将其与更高的认知能力联系起来.
科学领域:
- 认知心理学 认知心理学
- 心理测量 心理测量 心理测量
- 计算神经科学是一种计算神经科学.
背景情况:
- 扩散决策模型 (DDM) 漂移率 (v) 参数与认知个体差异和更高层次的功能有关.
- v通常被解释为处理速度,但这种映射是有争议的.
- 赛车积累器模型为证据积累提供了更细致的观点.
研究的目的:
- 调查DDM的v参数是否反映了证据积累速度 (SEA) 或证据积累效率 (EEA).
- 为了比较DDM和赛车积累器模型,以计算认知个体差异.
- 在处理速度和认知能力方面重新评估v的解释.
主要方法:
- 应用DDM和赛车积累器模型在三个独立数据集中的三个任务中.
- 分析了v参数与SEA和EEA的措施之间的关系.
- 与一般认知能力,工作记忆和执行功能相关的v,SEA和EEA相关.
主要成果:
- 在所有任务中,v参数与EEA比SEA更为密切相关.
- EEA与一般认知能力,工作记忆和执行功能有着一致的相关性.
- SEA解释了认知测量的不到1%的差异.
结论:
- 在DDM的v参数中的个体差异主要反映EEA,而不是SEA.
- 将v等同于传统处理速度的常见做法受到质疑.
- 研究结果支持证据积累效率与高级认知能力之间的联系.
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