ArticleeLife2017
The computational nature of memory modification.
Article in eLife, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 69 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.
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Who cites it
69 citing papers in PubMed, 186 citations in OpenAlex.
- Modeling human synchronization to rhythmic patterns with varying statistical regularities.Acta psychologica · 2026Article
- Re-exposure to reward re-evaluates related memories.Current biology : CB · 2026Article
- Memory erasure by dopamine-gated retrospective learning.bioRxiv : the preprint server for biology · 2026Article
- Dissociable memory modulation mechanisms facilitate fear amnesia at different timescales.eLife · 2025Article
- Misclassification in memory modification ineLife · 2025Article
- Extinction context is learned by pigeons, not given by the environment.Communications psychology · 2025Article
- Humans learn generalizable representations through efficient coding.Nature communications · 2025Article
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- Fear attenuation collaborations to optimize translation.Behavioral neuroscience · 2024Article
- Self-Improvising Memory: A Perspective on Memories as Agential, Dynamically Reinterpreting Cognitive Glue.Entropy (Basel, Switzerland) · 2024Article
- Learning leaves a memory trace in motor cortex.Current biology : CB · 2024Article
- Memory persistence: from fundamental mechanisms to translational opportunities.Translational psychiatry · 2024Review
- Fast adaptation to rule switching using neuronal surprise.PLoS computational biology · 2024Article
- Global remapping emerges as the mechanism for renewal of context-dependent behavior in a reinforcement learning model.Frontiers in computational neuroscience · 2024Article
- Drug memory reconsolidation: from molecular mechanisms to the clinical context.Translational psychiatry · 2023Review
- Predictions transform memories: How expected versus unexpected events are integrated or separated in memory.Neuroscience and biobehavioral reviews · 2023Review
- Structure learning principles of stereotype change.Psychonomic bulletin & review · 2023Review
- Enhancing reinforcement learning models by including direct and indirect pathways improves performance on striatal dependent tasks.PLoS computational biology · 2023Article
- The Computational and Neural Bases of Context-Dependent Learning.Annual review of neuroscience · 2023Review
9 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
4 authors at 3 institutions in 1 country.
Funding
Abstract
Retrieving a memory can modify its influence on subsequent behavior. We develop a computational theory of memory modification, according to which modification of a memory trace occurs through classical associative learning, but which memory trace is eligible for modification depends on a structure learning mechanism that discovers the units of association by segmenting the stream of experience into statistically distinct clusters (latent causes). New memories are formed when the structure learning mechanism infers that a new latent cause underlies current sensory observations. By the same token, old memories are modified when old and new sensory observations are inferred to have been generated by the same latent cause. We derive this framework from probabilistic principles, and present a computational implementation. Simulations demonstrate that our model can reproduce the major experimental findings from studies of memory modification in the Pavlovian conditioning literature.
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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.