Humans and neural networks show similar patterns of transfer and interference during continual learning

Learning multiple tasks in succession is a challenge for artificial and biological agents alike. However, it is often claimed that artificial agents fail to learn new tasks without overwriting previous ones, while humans succeed. Here, using a canonical sequential rule-learning task in which learners acquire Task~A, then learn Task~B, and are finally re-tested on Task~A, we see conserved patterns of transfer and interference in humans and neural networks. When successive tasks are similar (compared to dissimilar), both types of learner benefit more from transferring prior knowledge to Task~B, but demonstrate greater interference when retested on Task~A. Examining the hidden representations in neural networks, we observe this interference arises because networks utilise their existing solutions to accelerate learning of similar tasks, whilst corrupting those solutions for their previous use. In humans, we also observe striking individual differences where some participants (`lumpers') show interference after sequentially learning two similar tasks, while others (`splitters') avoid interference. These two strategies are associated with opposite patterns of performance benefits: `lumpers' are better at learning shared structure across stimuli and generalising to new settings, while `splitters' are better at remembering unique features of the stimuli. By varying the training regime for neural networks (`rich' versus `lazy'), we can recreate these differences, pushing networks towards low-dimensional representations which capitalise on shared structure, or high-dimensional representations maximising discrimination between inputs. Overall, our findings reveal shared computational principles relating transfer and interference in both systems, governed by global factors like task similarity and individual differences in structuring knowledge.