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将理论模型与功能神经成像相结合。

Integrating Theoretical Models with Functional Neuroimaging.

作者信息

Pratte Michael S, Tong Frank

机构信息

Department of Psychology, Mississippi State University; Department of Psychology and the Vanderbilt Vision Research Center, Vanderbilt University.

Department of Psychology and the Vanderbilt Vision Research Center, Vanderbilt University.

出版信息

J Math Psychol. 2017 Feb;76(B):80-93. doi: 10.1016/j.jmp.2016.06.008. Epub 2016 Jul 25.

Abstract

The development of mathematical models to characterize perceptual and cognitive processes dates back almost to the inception of the field of psychology. Since the 1990s, human functional neuroimaging has provided for rapid empirical and theoretical advances across a variety of domains in cognitive neuroscience. In more recent work, formal modeling and neuroimaging approaches are being successfully combined, often producing models with a level of specificity and rigor that would not have been possible by studying behavior alone. In this review, we highlight examples of recent studies that utilize this combined approach to provide novel insights into the mechanisms underlying human cognition. The studies described here span domains of perception, attention, memory, categorization, and cognitive control, employing a variety of analytic and model-inspired approaches. Across these diverse studies, a common theme is that individually tailored, creative solutions are often needed to establish compelling links between multi-parameter models and complex sets of neural data. We conclude that future developments in model-based cognitive neuroscience will have great potential to advance our theoretical understanding and ability to model both low-level and high-level cognitive processes.

摘要

用于描述感知和认知过程的数学模型的发展几乎可以追溯到心理学领域的开端。自20世纪90年代以来,人类功能神经成像为认知神经科学的各个领域带来了快速的实证和理论进展。在最近的研究中,形式建模和神经成像方法正在成功地结合起来,常常产生具有一定特异性和严谨性的模型,而仅通过研究行为是不可能做到这一点的。在这篇综述中,我们重点介绍了最近一些利用这种综合方法对人类认知背后机制提供新见解的研究实例。这里描述的研究涵盖了感知、注意力、记忆、分类和认知控制等领域,采用了各种分析方法和受模型启发的方法。在这些不同的研究中,一个共同的主题是,通常需要量身定制的创造性解决方案,以在多参数模型和复杂的神经数据集之间建立令人信服的联系。我们得出结论,基于模型的认知神经科学的未来发展在推进我们对低层次和高层次认知过程的理论理解以及建模能力方面将具有巨大潜力。

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