ArticleNature human behaviour2026
A habit and working memory model as an alternative account of human reward-based learning.
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 6 papers.
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Who cites it
6 citing papers in PubMed.
- Dopamine D2-receptor blockade in humans disrupts the effect of effort on learning.PLoS biology · 2026Trial
- Human curriculum learning of a cue combination task.Nature human behaviour · 2026Article
- Flexible route planning and rapid structure learning by mice in complex environments.bioRxiv : the preprint server for biology · 2026Article
- AI-Discovered Cognitive Models Reveal Novel Insights into Human and Animal Learning.bioRxiv : the preprint server for biology · 2026Article
- Hybrid neural-cognitive models reveal how memory shapes human reward learning.Nature human behaviour · 2026Article
- Adaptive integration of model-based and model-free strategies in human reinforcement learning of reachable space.bioRxiv : the preprint server for biology · 2026Article
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Abstract
Reinforcement learning (RL) algorithms have had tremendous success accounting for reward-based learning across species, including instrumental learning in contextual bandit tasks, and they capture variance in brain signals. However, reward-based learning in humans recruits multiple processes, including memory and choice perseveration; their contributions can easily be mistakenly attributed to RL computations. Here I investigate how much of reward-based learning behaviour is supported by RL computations in a context where other processes can be factored out. Reanalysis and computational modelling of 7 datasets (n = 594) in diverse samples show that in this instrumental context, reward-based learning is best explained by a combination of a fast working-memory-based process and a slower habit-like associative process, neither of which can be interpreted as a standard RL-like algorithm on its own. My results raise important questions for the interpretation of RL algorithms as capturing a meaningful process across brain and behaviour.
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