Decoding semantic representations in mind and brain
Decoding semantic representations in mind and brain
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DOI：
10.1016/j.tics.2022.12.006
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发表时间：
2023-01
影响因子：
19.9
通讯作者：
Saskia L. Frisby;A. Halai;Christopher R. Cox;M. L. Lambon Ralph;T. Rogers
中科院分区：
文献类型：
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作者：
Saskia L. Frisby;A. Halai;Christopher R. Cox;M. L. Lambon Ralph;T. Rogers
认知神经科学的一个关键目标是了解支持语义记忆的神经认知系统。最近的神经影像学数据的多变量分析，大大有助于这方面的努力，但这些新方法的快速发展，使其难以跟踪的多样性的结果，并了解如何以及为什么他们有时会导致矛盾的结论。我们通过回顾语义表征的认知理论及其神经实例来应对这一挑战。然后，我们考虑当代的神经解码方法，并评估每种方法都可能检测到哪些类型的表示。分析表明，为什么结果是异质性的，并确定认知理论，数据收集和分析，可以帮助更好地连接神经成像语义认知的机械理论之间的关键联系。
A key goal for cognitive neuroscience is to understand the neurocognitive systems that support semantic memory. Recent multivariate analyses of neuroimaging data have contributed greatly to this effort, but the rapid development of these novel approaches has made it difficult to track the diversity of findings and to understand how and why they sometimes lead to contradictory conclusions. We address this challenge by reviewing cognitive theories of semantic representation and their neural instantiation. We then consider contemporary approaches to neural decoding and assess which types of representation each can possibly detect. The analysis suggests why the results are heterogeneous and identifies crucial links between cognitive theory, data collection, and analysis that can help to better connect neuroimaging to mechanistic theories of semantic cognition.