ReviewCommunications biology2026
Generative AI as a transformational logic for cognitive neuroscience.
Review in Communications biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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.
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2 authors.
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Abstract
Cognitive neuroscience faces a paradox: neural data are abundant, yet conceptual synthesis has stalled because dominant contrast-based approaches show where activity differs but not how cognitive operations relate or transform. Here, we propose a generative-transformational logic grounded in AI and neural geometry, treating cognition as lawful mappings among neural states. Generative models can learn latent transformations linking states across tasks, contexts, and individuals. Because transformation success is testable, this framework enables counterfactual simulation and connects data-driven modeling with theory-driven inference. It moves cognitive neuroscience from mapping correlates toward algorithmic explanations of how the brain generates and reorganizes cognition over time.
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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.