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1
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3
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5
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6
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7
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8
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认知神经科学与形式认知模型的相互关系:物以类聚?

Reciprocal relations between cognitive neuroscience and formal cognitive models: opposites attract?

机构信息

Cognitive Science Center Amsterdam, University of Amsterdam, Plantage Muidergracht 24, 1018 TV Amsterdam, The Netherlands.

出版信息

Trends Cogn Sci. 2011 Jun;15(6):272-9. doi: 10.1016/j.tics.2011.04.002. Epub 2011 May 24.

DOI:10.1016/j.tics.2011.04.002
PMID:21612972
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3384559/
Abstract

Cognitive neuroscientists study how the brain implements particular cognitive processes such as perception, learning, and decision-making. Traditional approaches in which experiments are designed to target a specific cognitive process have been supplemented by two recent innovations. First, formal cognitive models can decompose observed behavioral data into multiple latent cognitive processes, allowing brain measurements to be associated with a particular cognitive process more precisely and more confidently. Second, cognitive neuroscience can provide additional data to inform the development of formal cognitive models, providing greater constraint than behavioral data alone. We argue that these fields are mutually dependent; not only can models guide neuroscientific endeavors, but understanding neural mechanisms can provide key insights into formal models of cognition.

摘要

认知神经科学家研究大脑如何执行特定的认知过程,如感知、学习和决策。传统的方法是设计实验来针对特定的认知过程,而最近有两种创新方法得到了补充。首先,形式认知模型可以将观察到的行为数据分解为多个潜在的认知过程,从而更精确、更有信心地将大脑测量结果与特定的认知过程联系起来。其次,认知神经科学可以提供额外的数据来为形式认知模型的发展提供信息,提供比行为数据更严格的约束。我们认为这两个领域是相互依存的;不仅模型可以指导神经科学的努力,而且对神经机制的理解可以为认知的形式模型提供关键的见解。