Becchio Cristina, Pullar Kiri, Scaliti Eugenio, Panzeri Stefano
Department of Neurology, University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany.
Department of Neurology, University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany; Institute for Neural Information Processing, Center for Molecular Neurobiology Hamburg, University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany.
Phys Life Rev. 2024 Dec;51:442-458. doi: 10.1016/j.plrev.2024.11.009. Epub 2024 Nov 15.
Recent years have seen an explosion of interest in naturalistic behaviour and in machine learning tools for automatically tracking it. However, questions about what to measure, how to measure it, and how to relate naturalistic behaviour to neural activity and cognitive processes remain unresolved. In this Perspective, we propose a general experimental and computational framework - kinematic coding - for measuring how information about cognitive states is encoded in structured patterns of behaviour and how this information is read out by others during social interactions. This framework enables the design of new experiments and the generation of testable hypotheses that link behaviour, cognition, and neural activity at the single-trial level. Researchers can employ this framework to identify single-subject, single-trial encoding and readout computations and address meaningful questions about how information encoded in bodily motion is transmitted and communicated.
近年来,人们对自然行为以及用于自动追踪自然行为的机器学习工具产生了浓厚兴趣。然而,关于测量什么、如何测量以及如何将自然行为与神经活动和认知过程联系起来的问题仍未得到解决。在本观点文章中,我们提出了一个通用的实验和计算框架——运动编码,用于测量认知状态信息是如何在结构化行为模式中编码的,以及在社交互动过程中他人是如何读取这些信息的。该框架能够设计新的实验并生成可检验的假设,这些假设在单试次水平上关联了行为、认知和神经活动。研究人员可以利用这个框架来识别单受试者、单试次的编码和读出计算,并解决有关身体运动中编码的信息如何传输和交流的有意义的问题。