ArticleNature human behaviour2026
Shared sensitivity to data distribution during learning in humans and transformer networks.
Article in Nature human behaviour, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Linguistics and human brain: a perspective of computational neuroscience.Cognitive neurodynamics · 2026Review
- Human curriculum learning of a cue combination task.Nature human behaviour · 2026Article
- The curriculum effect in visual learning: The role of readout dimensionality.PLoS computational biology · 2026Article
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Authors and funding
2 authors.
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Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.