在计算认知神经科学中的模型美德
Saskia Heijnen1, Jan Sleutels1, Roy de Kleijn1
1Leiden University.
Journal of cognitive neuroscience
|May 13, 2024
概括
在认知神经科学中缺乏评估计算模型的标准,通过确定六个关键的"模型美德"来解决这个问题. 这些优点,包括经验准确性和可信性,是相互关联的,对建模者来说是取决于背景的.
科学领域:
- 认知神经科学 认知神经科学
- 计算机建模 计算建模
背景情况:
- 计算模型在认知神经科学中非常丰富.
- 缺乏评估这些模型的明确框架.
- 本研究探讨了评估模型质量的标准的需要.
研究的目的:
- 识别和检查计算模型的可取性质 (优点).
- 研究这些美德之间的相互关系.
- 为评估认知控制中的计算模型提供一个框架.
主要方法:
- 专注于认知控制领域.
- 确定了六个关键的模型优点:经验准确性,经验范围,功能分析,因果细节,生物可信性和心理可信性.
- 分析了已发表的Stroop建模工作,并与专家建模师进行了定性采访.
主要成果:
- 模范美德是相互关联的.
- 每个美德的感知价值取决于建模者的具体目标.
- 这些相互依赖和目标依赖往往没有在文献中明确说明.
结论:
- 研究人员应该更明确地说明他们的建模选择及其理由.
- 鉴定的优点为更严格的评估框架提供了一个起点.
- 这些发现旨在刺激对认知神经科学中模型评估的讨论.
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