计算认知神经科学中的推理目标和表示决策:从漂移扩散模型中吸取教训
Ari Khoudary1,2,3, Megan A K Peters1,2,3,4,5, Aaron M Bornstein1,2,3
1Department of Cognitive Sciences, University of California, Irvine, Irvine, California, USA.
The European journal of neuroscience
|April 9, 2025
概括
研究人员需要一个哲学工具包来理解计算认知模型,如漂移扩散模型 (DDM). 这个工具包有助于解释基于用户目标的模型,解决有关决策值的争论.
科学领域:
- 认知神经科学 认知神经科学
- 科学哲学的哲学科学哲学
- 计算精神病学是一种计算精神病学.
背景情况:
- 计算认知模型增强了认知神经科学中的严谨性.
- 了解模型的应用和解释对研究人员至关重要.
- 需要一种哲学方法来应对超科学挑战.
研究的目的:
- 为计算认知模型开发一个哲学工具包.
- 澄清用户推理目标在模型应用中的作用.
- 分析漂移扩散模型 (DDM) 和决策值辩论.
主要方法:
- 对建模实践的哲学分析.
- 漂移扩散模型 (DDM) 的概念分析.
- 检查用户推理目标和模型开发社区.
主要成果:
- 两个DDM结构为加速选择行为提供了互补的解释.
- 模型解释 (节制,规范) 反映了用户社区 (心理测量学家,决策科学家).
- 为选择合适的DDM结构提供了一个启发式.
结论:
- 哲学为理解和应用计算认知模型提供了一个框架.
- 用户推理目标是模型解释和应用的核心.
- 本书通过突出补充的解释来解决关于DDM决策门的辩论.
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