Centre for Cognitive Neuroimaging, School of Psychology and Neuroscience, University of Glasgow.
Centre for Human Brain Health, School of Psychology, University of Birmingham.
Perspect Psychol Sci. 2024 Nov;19(6):993-1010. doi: 10.1177/17456916231191744. Epub 2023 Aug 29.
A central pursuit of cognitive neuroscience is to find neural mechanisms of cognition, with research programs favoring different strategies to look for them. But what is a neural mechanism, and how do we know we have captured them? Here I answer these questions through a framework that integrates Marr's levels with philosophical work on mechanism. From this, the following goal emerges: What needs to be explained are the computations of cognition, with explanation itself given by mechanism-composed of algorithms and parts of the brain that realize them. This reveals a delineation within cognitive neuroscience research. In the , the computations of cognition are linked to phenomena in the brain, narrowing down where and when mechanisms are situated in space and time. In the , it is established how computation emerges from organized interactions between parts-filling the premechanistic mold. I explain why a shift toward mechanistic modeling helps us meet our aims while outlining a road map for doing so. Finally, I argue that the explanatory scope of neural mechanisms can be approximated by effect sizes collected across studies, not just conceptual analysis. Together, these points synthesize a mechanistic agenda that allows subfields to connect at the level of theory.
认知神经科学的一个核心追求是找到认知的神经机制，研究计划倾向于采用不同的策略来寻找这些机制。但是，什么是神经机制，我们如何知道我们已经捕捉到了它们？在这里，我通过一个将马鲁的层次结构与关于机制的哲学工作相结合的框架来回答这些问题。由此，出现了以下目标：需要解释的是认知的计算，而解释本身则由由算法和实现它们的大脑部分组成的机制提供。这揭示了认知神经科学研究中的一个划分。在 中，认知的计算与大脑中的现象联系起来，缩小了机制在空间和时间上的位置和时间。在 中，建立了计算如何从部分之间有组织的相互作用中出现——填补了前机制的模式。我解释了为什么向机械建模的转变有助于我们实现目标，同时概述了实现这一目标的路线图。最后，我认为可以通过跨研究收集的效应大小来近似神经机制的解释范围，而不仅仅是概念分析。综上所述，这些观点综合了一个机械论议程，允许子领域在理论层面上进行连接。
