ArticleCurrent opinion in behavioral sciences2024
Modelling cognitive flexibility with deep neural networks.
Article in Current opinion in behavioral sciences, 2024. 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.
- The dynamic interplay between in-context and in-weight learning in humans and neural networks.ArXiv · 2025Article
- Parallel trade-offs in human cognition and neural networks: The dynamic interplay between in-context and in-weight learning.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Individual differences in sequential decision-making.bioRxiv : the preprint server for biology · 2025Article
- Practice reshapes the geometry and dynamics of task-tailored representations.Cerebral cortex (New York, N.Y. : 1991) · 2025Article
- Article
- Practice Reshapes the Geometry and Dynamics of Task-tailored Representations.bioRxiv : the preprint server for biology · 2024Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Neural networks trained with deep reinforcement learning can perform many complex tasks at similar levels to humans. However, unlike people, neural networks converge to a fixed solution during optimisation, limiting their ability to adapt to new challenges. In this opinion, we highlight three key new methods that allow neural networks to be posed as models of human cognitive flexibility. In the first, neural networks are trained in ways that allow them to learn complementary 'habit' and 'goal'-based policies. In another, flexibility is 'meta-learned' during pre-training from large and diverse data, allowing the network to adapt 'in context' to novel inputs. Finally, we discuss work in which deep networks are meta-trained to adapt their behaviour to the level of control they have over the environment. We conclude by discussing new insights about cognitive flexibility obtained from the training of large generative models with reinforcement learning from human feedback.
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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.