Semantic anchors facilitate task encoding in continual learning

Humans are remarkably efficient at learning new tasks, in large part by relying on the integration of previously learned knowledge. However, research on task learning typically focuses on the learning of abstract task rules on minimalist stimuli, to study behavior independent of the learning history that humans come equipped with (i.e., semantic knowledge). In contrast, several theories suggest that the use of semantic knowledge and labels may help the learning of new task information. Here, we tested whether providing existing, semantically rich task rules and response labels allowed for more robust task encoding and less (catastrophic) forgetting and interference. Our results show that providing semantically rich task rules and response labels resulted in less task forgetting (Experiment 1), both when using pictorial symbols or words as labels (Experiment 2). Using artificial recurrent neural networks fitted to task behavior, we show that the semantically rich learning conditions resulted in more separated task representations during learning. Finally, using a subsequent value-based decision-making task and reinforcement learning modeling (Experiment 3), we demonstrate how the learned embedding of novel stimuli in semantically rich, representations, further allowed for a more efficient, feature-specific processing when learning new task information. Together, our findings show the benefit of using semantically rich task rules and response labels during novel task learning, thereby offering important insights into why humans excel in continual learning and are less susceptible to catastrophic forgetting compared to most artificial agents.