ArticlePLoS computational biology2023
Modelling continual learning in humans with Hebbian context gating and exponentially decaying task signals.
Article in PLoS computational biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- A neural mechanism for online discovery of latent contexts.bioRxiv : the preprint server for biology · 2026Article
- Exploring neural manifolds across a wide range of intrinsic dimensions.PLoS computational biology · 2026Article
- Contextual inference through flexible integration of environmental features and behavioural outcomes.PLoS computational biology · 2026Article
- Order parameters and phase transitions of continual learning in deep neural networks.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Curriculum effects in multitask learning through the lens of contextual inference.Current opinion in neurobiology · 2025Review
- Article
- 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
- Limitation of switching sensory information flow in flexible perceptual decision making.Nature communications · 2025Article
- Semantic Anchors Facilitate Task Encoding in Continual Learning.Open mind : discoveries in cognitive science · 2025Article
- Toward a computational role for locus coeruleus/norepinephrine arousal systems.Current opinion in behavioral sciences · 2024Article
- 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
- Blocked training facilitates learning of multiple schemas.Communications psychology · 2024Article
- Toward the Emergence of Intelligent Control: Episodic Generalization and Optimization.Open mind : discoveries in cognitive science · 2024Article
- The Computational and Neural Bases of Context-Dependent Learning.Annual review of neuroscience · 2023Review
- 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
- Curriculum learning for human compositional generalization.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- A Neural Network Model of Continual Learning with Cognitive Control.CogSci ... Annual Conference of the Cognitive Science Society. Cognitive Science Society (U.S.). Conference · 2022Article
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Authors and funding
4 authors.
Funding
Abstract
Humans can learn several tasks in succession with minimal mutual interference but perform more poorly when trained on multiple tasks at once. The opposite is true for standard deep neural networks. Here, we propose novel computational constraints for artificial neural networks, inspired by earlier work on gating in the primate prefrontal cortex, that capture the cost of interleaved training and allow the network to learn two tasks in sequence without forgetting. We augment standard stochastic gradient descent with two algorithmic motifs, so-called "sluggish" task units and a Hebbian training step that strengthens connections between task units and hidden units that encode task-relevant information. We found that the "sluggish" units introduce a switch-cost during training, which biases representations under interleaved training towards a joint representation that ignores the contextual cue, while the Hebbian step promotes the formation of a gating scheme from task units to the hidden layer that produces orthogonal representations which are perfectly guarded against interference. Validating the model on previously published human behavioural data revealed that it matches performance of participants who had been trained on blocked or interleaved curricula, and that these performance differences were driven by misestimation of the true category boundary.
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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.