扩散决策模型的神经不可推理性对于认知心理测量并不重要,但奥恩斯坦-乌伦贝克模型更好
Jia-Shun Wang1,2, Christopher Donkin3
1Department of Psychology, Ludwig-Maximilians-Universität München, Munich, Germany. jiashun.wang.student@gmail.com.
Psychonomic bulletin & review
|May 14, 2024
概括
扩散决策模型 (DDM) 通常与更神经可信的模型保持一致. 奥恩斯坦-乌伦贝克模型 (OUM) 为决策研究提供了一个神经基础的DDM替代方案.
科学领域:
- 认知心理测量 认知心理测量
- 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:
- 认知模型测量潜在的行为过程.
- 扩散决策模型 (DDM) 是决策中的一个标准工具.
- 最近的理论强调神经可信性,这是DDM缺乏的.
研究的目的:
- 评估DDM缺乏神经机制是否会影响其测量准确度.
- 将DDM与神经可信模型比较,例如Ising决策者 (IDM).
- 评估奥恩斯坦-乌伦贝克模型 (OUM) 作为一种潜在的替代方案.
主要方法:
- 将DDM和IDM与行为数据进行交叉匹配.
- 分析DDM,IDM和OUM的共享假设和解释.
- 从每个模型中得出的结论进行比较.
主要成果:
- DDM的结论在很大程度上与更神经可信的IDM的结论一致.
- 该OUM与IDM在自我激发方面有着相同的解释.
- 在共同的假设上,OUM和DDM得出了类似的结论.
结论:
- 即使与神经详细模型相比,DDM的核心发现也很强大.
- OUM提供了计算可操作性和神经可信性的平衡.
- 提出OUM作为一个可行的替代性认知心理测量工具DDM.
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