ArticleiScience2026
Ultraslow brain dynamics as neurophysiological markers of information sampling during learning.
Article in iScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
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Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To learn, we must actively sample information from our environment: selecting, prioritizing, and extracting relevant cues. Here, we investigated whether ultraslow event-related potentials (ERPs) could track information sampling as participants learned new words from sentence contexts that provided varying amounts of information about their meaning, requiring them to actively extract learning-relevant cues. Ultraslow negative ERPs emerged while participants engaged in learning. Although ultraslow brain activity was not modulated by the level of informativeness of the contexts, individual differences in learning success were associated with steeper ultraslow voltage slopes. Specifically, participants with better learning outcomes exhibited a steeper ultraslow slope while learning from informative contexts, suggesting greater engagement and exploitation of relevant information. Together, these findings identify ultraslow brain activity as candidate neurophysiological markers of information sampling and provide evidence that individual differences in these dynamics relate to successful learning.
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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.