ArticleComputational brain & behavior2026
Time-Local Transformer.
Article in Computational brain & behavior, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Human language processing, characterized by its ability to capture long-range dependencies in sequential inputs, operates under the constraints of limited working memory. In contrast, state-of-the-art transformer models in artificial intelligence rely on access to the fixed context window, which deviates from the dynamic nature of human cognition. Here, we propose a novel approach to reconcile this disparity by integrating a computational model of working memory into the transformer architecture. This biologically-inspired modification constructs a time-local transformer, capable of learning complex dependencies without needing the full input history. Our findings demonstrate that this approach still preserves the capacity of transformers for effective sequence processing. This work is a step towards developing AI models that align more closely with the principles of human brain function, opening new avenues for understanding the neural underpinnings of language and cognition.
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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.