ArticleNature communications2020
Forward models of repetition suppression depend critically on assumptions of noise and granularity.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Article
- A Unifying Model for Discordant and Concordant Results in Human Neuroimaging Studies of Facial Viewpoint Selectivity.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024Article
- Intra-V1 functional networks and classification of observed stimuli.Frontiers in neuroinformatics · 2024Article
- Dampened sensory representations for expected input across the ventral visual stream.Oxford open neuroscience · 2022Article
- Enhanced inter-regional coupling of neural responses and repetition suppression provide separate contributions to long-term behavioral priming.Communications biology · 2021Article
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Authors and funding
2 authors.
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Abstract
PubMed holds no abstract for this paper.
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What 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.