ArticleCogSci ... Annual Conference of the Cognitive Science Society. Cognitive Science Society (U.S.). Conference2022
A Neural Network Model of Continual Learning with Cognitive Control.
Article in CogSci ... Annual Conference of the Cognitive Science Society. Cognitive Science Society (U.S.). Conference, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 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
- Development and validation of predictive models for diabetic retinopathy using machine learning.PloS one · 2025Article
- Rapid context inference in a thalamocortical model using recurrent neural networks.Nature communications · 2024Article
- Reconciling shared versus context-specific information in a neural network model of latent causes.Scientific reports · 2024Article
- A recurrent neural network model of prefrontal brain activity during a working memory task.PLoS computational biology · 2023Article
- Continual task learning in natural and artificial agents.Trends in neurosciences · 2023Review
- Modelling continual learning in humans with Hebbian context gating and exponentially decaying task signals.PLoS computational biology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Neural networks struggle in continual learning settings from catastrophic forgetting: when trials are blocked, new learning can overwrite the learning from previous blocks. Humans learn effectively in these settings, in some cases even showing an advantage of blocking, suggesting the brain contains mechanisms to overcome this problem. Here, we build on previous work and show that neural networks equipped with a mechanism for cognitive control do not exhibit catastrophic forgetting when trials are blocked. We further show an advantage of blocking over interleaving when there is a bias for active maintenance in the control signal, implying a tradeoff between maintenance and the strength of control. Analyses of map-like representations learned by the networks provided additional insights into these mechanisms. Our work highlights the potential of cognitive control to aid continual learning in neural networks, and offers an explanation for the advantage of blocking that has been observed in humans.
Indexed as
Identifiers
37223441PMC10205096What OpenQuestion holds
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.