Lee Woo-Tek, Hazeltine Eliot, Jiang Jiefeng
Department of Psychological and Brain Sciences, University of Iowa, Iowa City, IA 52242.
Cognitive Control Collaborative, University of Iowa, Iowa City, IA 52242.
bioRxiv. 2025 Mar 15:2024.12.02.626403. doi: 10.1101/2024.12.02.626403.
Task knowledge can be encoded hierarchically such that complex tasks can be built by associating simpler tasks. This associative organization supports generalization to facilitate learning of related but novel complex tasks. To study how the brain implements generalization in hierarchical task learning, we trained human participants on two complex tasks that shared a simple task and tested them on novel complex tasks whose association could be inferred via the shared simple task. Behaviorally, we observed faster learning of the novel complex tasks than control tasks. Using electroencephalogram (EEG) data, we decoded constituent simple tasks when performing a complex task (i.e., EEG association effect). Crucially, the shared simple task, although not part of the novel complex task, could be reliably decoded from the novel complex task. This decoding strength was correlated with EEG association effect and behavioral generalization effect. The findings demonstrate how task learning can be accelerated by associative inference.
任务知识可以分层编码，这样复杂任务就可以通过关联更简单的任务构建而成。这种关联组织支持泛化，以促进对相关但新颖的复杂任务的学习。为了研究大脑如何在分层任务学习中实现泛化，我们让人类参与者在两个共享一个简单任务的复杂任务上进行训练，并在可以通过共享的简单任务推断其关联的新颖复杂任务上对他们进行测试。在行为上，我们观察到与对照任务相比，新颖复杂任务的学习速度更快。使用脑电图（EEG）数据，我们在执行复杂任务时解码了组成简单任务（即EEG关联效应）。至关重要的是，共享的简单任务虽然不是新颖复杂任务的一部分，但可以从新颖复杂任务中可靠地解码出来。这种解码强度与EEG关联效应和行为泛化效应相关。这些发现证明了关联推理如何能够加速任务学习。
