Shared sensitivity to data distribution during learning in humans and transformer networks

Do humans learn like transformers? We trained both humans (n = 530) and transformer networks on a rule learning task where they had to respond to a query in a sequence. At test, we measured 'in-context' learning (generalize the rule to novel queries) and 'in-weights' learning (recall past experiences from memory). Manipulating the diversity and redundancy of examples in the training distribution, we found that humans and transformer networks respond in very similar ways. In both types of learner, redundancy and diversity trade off in driving in-weights and in-context learning, respectively, whereas a composite distribution with a balanced mix of redundancy and diversity allows the two strategies to be used in tandem. However, we also found that while humans benefit from dynamic training schedules that emphasize diverse examples early, transformers do not. So, while the same data-distributional properties promote learning in humans and transformer networks, only people benefit from curricula.