ReviewAnnual review of neuroscience2023
The Computational and Neural Bases of Context-Dependent Learning.
Review in Annual review of neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
27 citing papers in PubMed.
- Reconciling global collaboration and data sovereignty in neuroscience.Nature neuroscience · 2026Review
- De novo motor learning as controller synthesis: a conceptual framework.Biological cybernetics · 2026Article
- Spontaneous emergence of context-dependent statistical learning in humans and neural networks.iScience · 2026Article
- The inherent capacity of neurons to learn order relations and support abstract reasoning.Nature communications · 2026Article
- Contextual cues and transition statistics drive expression of competing motor memories.iScience · 2026Article
- Categorization is 'baked' into the brain.Nature reviews. Neuroscience · 2026Review
- Past, present, and future of virtual reality-based biological science education (2010-2025): a BERTopic-driven analysis.BMC medical education · 2026Article
- The retrieval of previously learned motor memories is facilitated by the reinstatement of default mode network manifold structures.PLoS biology · 2026Article
- A minimal recurrent neural network models the robustness of interleaved practice on motor sequence learning.Scientific reports · 2026Article
- Task, person, and experiential characteristics drive the transfer of learning.Communications psychology · 2026Article
- Safety-relevant experience reshapes neural codes for ambiguous threat in adolescents.Social cognitive and affective neuroscience · 2026Article
- Infinite hidden Markov models can dissect the complexities of learning.Nature neuroscience · 2026Article
- Curriculum effects in multitask learning through the lens of contextual inference.Current opinion in neurobiology · 2025Review
- Neural mechanisms of flexible perceptual inference.PLoS computational biology · 2025Article
- Adaptation of hippocampal spatial and contextual representations to task structure.Science advances · 2025Article
- Episodic Memories Bias Contextual Threat Inferences after Aerobic and Mind-Body Exercise.Mental health and physical activity · 2025Article
- Transfer of motor learning is associated with patterns of activity in the default mode network.PLoS biology · 2025Article
- Genetic changes linked to two different syndromic forms of autism enhance reinforcement learning in early adolescent male but not female mice.bioRxiv : the preprint server for biology · 2025Article
- Do people only adjust ongoing movements vigorously when it is advantageous to do so?Experimental brain research · 2025Article
- What can ANS signals tell us about motor learning? An implication for better assessment of cognitive contribution to motor learning.Frontiers in behavioral neuroscience · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Flexible behavior requires the creation, updating, and expression of memories to depend on context. While the neural underpinnings of each of these processes have been intensively studied, recent advances in computational modeling revealed a key challenge in context-dependent learning that had been largely ignored previously: Under naturalistic conditions, context is typically uncertain, necessitating contextual inference. We review a theoretical approach to formalizing context-dependent learning in the face of contextual uncertainty and the core computations it requires. We show how this approach begins to organize a large body of disparate experimental observations, from multiple levels of brain organization (including circuits, systems, and behavior) and multiple brain regions (most prominently the prefrontal cortex, the hippocampus, and motor cortices), into a coherent framework. We argue that contextual inference may also be key to understanding continual learning in the brain. This theory-driven perspective places contextual inference as a core component of learning.
Indexed as
Identifiers
What 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.