Decoding task representations that support generalization in hierarchical task

Abstract

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. Significance StatementHumans can generalize knowledge of existing tasks to accelerate the learning of new tasks. We hypothesize that this generalization is achieved by building complex tasks that associate simple (sub)tasks that can be reused. Using electroencephalogram (EEG) data, we showed that constituent simple tasks can be decoded from the EEG data of humans learning new complex tasks. Crucially, when participants represent complex tasks as associations between multiple simple tasks, the simple tasks can be decoded from the new complex task, even when they are not part of the new complex task. These findings demonstrate the importance of the reinstatement of simple tasks in task learning through generalization.