Semantic Anchors Facilitate Task Encoding in Continual
Learning
AbstractHumans 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
embeddings and response labels allowed for more robust task rule encoding and
less (catastrophic) forgetting and interference. Our results show that providing
semantically rich task settings and response labels resulted in less task
forgetting (Experiment 1), both when using pictorial symbols or words as labels
(Experiment 2), or when contrasted with visually matched shape labels without
inherent meaning (Experiment 4). 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. Finally, using artificial recurrent neural networks fitted
to our participants’ task performance, we found that task separation
during learning was more predictive of learning and task performance in the
semantically rich conditions. 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.
