Transfer learning improves the performance of the target task by leveraging the data of a specific source task: the closer the relationship between the source and the target tasks, the greater the performance improvement by transfer learning. In neuroscience, the relationship between cognitive tasks is usually represented by similarity of activated brain regions or neural representation. However, no study has linked transfer learning and neuroscience to reveal the relationship between cognitive tasks. In this study, we propose a transfer learning framework to reflect the relationship between cognitive tasks, and compare the task relations reflected by transfer learning and by the overlaps of brain regions (e.g., neurosynth). Our results of transfer learning create cognitive taskonomy to reflect the relationship between cognitive tasks which is well in line with the task relations derived from neurosynth. Transfer learning performs better in task decoding with fMRI data if the source and target cognitive tasks activate similar brain regions. Our study uncovers the relationship of multiple cognitive tasks and provides guidance for source task selection in transfer learning for neural decoding based on small-sample data.
翻译:通过利用特定源任务的数据,转移学习提高了目标任务的业绩:源与目标任务之间的关系越密切,通过转移学习提高绩效的工作就越多。在神经科学方面,认知任务之间的关系通常以活跃脑区域或神经代表的相似性为代表。然而,没有一项研究将转移学习和神经科学联系起来,以揭示认知任务之间的关系。在本研究中,我们建议建立一个转移学习框架,以反映认知任务之间的关系,比较转移学习和大脑区域重叠(例如神经合成)所反映的任务关系。我们转移学习的结果创造了认知任务,以反映与神经合成产生的任务关系完全一致的认知任务之间的关系。如果源和目标针对认知任务启动类似的大脑区域,转移学习在与FMRI数据分离方面表现得更好。我们的研究揭示了多种认知任务的关系,并为基于小型合成数据进行神经脱分解的转移学习中的源任务选择提供了指导。