Palmeri Thomas J, Love Bradley C, Turner Brandon M
Vanderbilt University, United States.
University College London, United Kingdom.
J Math Psychol. 2017 Feb;76(Pt B):59-64. doi: 10.1016/j.jmp.2016.10.010. Epub 2016 Nov 23.
This special issue explores the growing intersection between mathematical psychology and cognitive neuroscience. Mathematical psychology, and cognitive modeling more generally, has a rich history of formalizing and testing hypotheses about cognitive mechanisms within a mathematical and computational language, making exquisite predictions of how people perceive, learn, remember, and decide. Cognitive neuroscience aims to identify neural mechanisms associated with key aspects of cognition using techniques like neurophysiology, electrophysiology, and structural and functional brain imaging. These two come together in a powerful new approach called , which can both inform cognitive modeling and help to interpret neural measures. Cognitive models decompose complex behavior into representations and processes and these latent model states can be used to explain the modulation of brain states under different experimental conditions. Reciprocally, neural measures provide data that help constrain cognitive models and adjudicate between competing cognitive models that make similar predictions about behavior. As examples, brain measures are related to cognitive model parameters fitted to individual participant data, measures of brain dynamics are related to measures of model dynamics, model parameters are constrained by neural measures, model parameters or model states are used in statistical analyses of neural data, or neural and behavioral data are analyzed jointly within a hierarchical modeling framework. We provide an introduction to the field of model-based cognitive neuroscience and to the articles contained within this special issue.
本期特刊探讨了数学心理学与认知神经科学之间日益增长的交叉领域。数学心理学，以及更广泛的认知建模，在使用数学和计算语言对认知机制的假设进行形式化和测试方面有着丰富的历史，能够对人们如何感知、学习、记忆和决策做出精确预测。认知神经科学旨在使用神经生理学、电生理学以及结构和功能脑成像等技术，识别与认知关键方面相关的神经机制。这两者结合形成了一种强大的新方法，称为 ，它既可以为认知建模提供信息，又有助于解释神经测量结果。认知模型将复杂行为分解为表征和过程，这些潜在的模型状态可用于解释不同实验条件下脑状态的调制。反之，神经测量提供的数据有助于约束认知模型，并在对行为做出类似预测的相互竞争的认知模型之间进行裁决。例如，脑测量与拟合个体参与者数据的认知模型参数相关，脑动力学测量与模型动力学测量相关，模型参数受神经测量约束，模型参数或模型状态用于神经数据的统计分析，或者在分层建模框架内联合分析神经和行为数据。我们对基于模型的认知神经科学领域以及本期特刊中的文章进行了介绍。
