ArticleCogSci ... Annual Conference of the Cognitive Science Society. Cognitive Science Society (U.S.). Conference2021
Complementary Structure-Learning Neural Networks for Relational Reasoning.
Article in CogSci ... Annual Conference of the Cognitive Science Society. Cognitive Science Society (U.S.). Conference, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed, 2 citations in OpenAlex.
- A mathematical theory of relational generalization in transitive inference.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- A mathematical theory of relational generalization in transitive inference.bioRxiv : the preprint server for biology · 2024Article
- Emergent neural dynamics and geometry for generalization in a transitive inference task.PLoS computational biology · 2024Article
- Transforming social perspectives with cognitive maps.Social cognitive and affective neuroscience · 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
- The Structure of Systematicity in the Brain.Current directions in psychological science · 2022Article
- Complementary Structure-Learning Neural Networks for Relational Reasoning.CogSci ... Annual Conference of the Cognitive Science Society. Cognitive Science Society (U.S.). Conference · 2021Article
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 at 1 institution in 1 country.
Funding
Abstract
The neural mechanisms supporting flexible relational inferences, especially in novel situations, are a major focus of current research. In the complementary learning systems framework, pattern separation in the hippocampus allows rapid learning in novel environments, while slower learning in neocortex accumulates small weight changes to extract systematic structure from well-learned environments. In this work, we adapt this framework to a task from a recent fMRI experiment where novel transitive inferences must be made according to implicit relational structure. We show that computational models capturing the basic cognitive properties of these two systems can explain relational transitive inferences in both familiar and novel environments, and reproduce key phenomena observed in the fMRI experiment.
Indexed as
Identifiers
34617073PMC8491570W3160893950What 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.