ArticleeLife2020
Integrating prediction errors at two time scales permits rapid recalibration of speech sound categories.
Article in eLife, 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
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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
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Who cites it
5 citing papers in PubMed.
- A deep neural network model of audiovisual speech recognition reports the McGurk effect.Psychonomic bulletin & review · 2026Article
- A Deep Neural Network Model of Audiovisual Speech Recognition Reports the McGurk Effect.bioRxiv : the preprint server for biology · 2025Article
- Repeatedly experiencing the McGurk effect induces long-lasting changes in auditory speech perception.Communications psychology · 2024Article
- Article
- Audio-visual combination of syllables involves time-sensitive dynamics following from fusion failure.Scientific reports · 2020Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Speech perception presumably arises from internal models of how specific sensory features are associated with speech sounds. These features change constantly (e.g. different speakers, articulation modes etc.), and listeners need to recalibrate their internal models by appropriately weighing new versus old evidence. Models of speech recalibration classically ignore this volatility. The effect of volatility in tasks where sensory cues were associated with arbitrary experimenter-defined categories were well described by models that continuously adapt the learning rate while keeping a single representation of the category. Using neurocomputational modelling we show that recalibration of
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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.