Our understanding of cognition has been advanced by two traditionally nonoverlapping and non-interacting groups. Mathematical psychologists rely on behavioral data to evaluate formal models of cognition, whereas cognitive neuroscientists rely on statistical models to understand patterns of neural activity, often without any attempt to make a connection to the mechanism supporting the computation. Both approaches suffer from critical limitations as a direct result of their focus on data at one level of analysis (cf. Marr, 1982), and these limitations have inspired researchers to attempt to combine both neural and behavioral measures in a cross-level integrative fashion. The importance of solving this problem has spawned several entirely new theoretical and statistical frameworks developed by both mathematical psychologists and cognitive neuroscientists. However, with each new approach comes a particular set of limitations and benefits. In this article, we survey and characterize several approaches for linking brain and behavioral data. We organize these approaches on the basis of particular cognitive modeling goals: (1) using the neural data to constrain a behavioral model, (2) using the behavioral model to predict neural data, and (3) fitting both neural and behavioral data simultaneously. Within each goal, we highlight a few particularly successful approaches for accomplishing that goal, and discuss some applications. Finally, we provide a conceptual guide to choosing among various analytic approaches in performing model-based cognitive neuroscience.

中文翻译：

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基于模型的认知神经科学的分析方法

我们对认知的理解是由两个传统上不重叠和不相互作用的群体推动的。数学心理学家依靠行为数据来评估正式的认知模型，而认知神经科学家则依靠统计模型来理解神经活动模式，通常不尝试与支持计算的机制建立联系。这两种方法都存在严重的局限性，因为它们直接关注于一个分析级别的数据（参见 Marr，1982），这些局限性激励研究人员尝试以跨级别的综合方式将神经和行为测量结合起来。解决这个问题的重要性催生了数学心理学家和认知神经科学家开发的几个全新的理论和统计框架。然而，每一种新方法都会带来一系列特定的限制和好处。在本文中，我们调查并描述了连接大脑和行为数据的几种方法。我们根据特定的认知建模目标组织这些方法：（1）使用神经数据来约束行为模型，（2）使用行为模型来预测神经数据，以及（3）同时拟合神经数据和行为数据。在每个目标中，我们重点介绍了实现该目标的一些特别成功的方法，并讨论了一些应用程序。最后，我们提供了在执行基于模型的认知神经科学时选择各种分析方法的概念指南。