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Proactive Sensing of Periodic and Aperiodic Auditory Patterns
Open accessAbstract
The ability to predict when something will happen facilitates sensory processing and the ensuing computations. Building on the observation that neural activity entrains to periodic stimulation, leading neurophysiological models imply that temporal predictions rely on oscillatory entrainment. Although they provide a sufficient solution to predict periodic regularities, these models are challenged by a series of findings that question their suitability to account for temporal predictions based on aperiodic regularities. Aiming for a more comprehensive model of how the brain anticipates 'when' in auditory contexts, we emphasize the capacity of motor and higher-order top-down systems to prepare sensory processing in a proactive and temporally flexible manner. Focusing on speech processing, we illustrate how this framework leads to new hypotheses.
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Functional constraints in the evolution of brain circuits
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Metadata
- Authors
- Rimmele, J. M., Morillon, B., Poeppel, D., & Arnal, L. H.
- Venue
- Trends in Cognitive Sciences
- Publisher
- Elsevier BV
- Published
- 2018-10
- Volume
- 22
- Issue
- 10
- Pages
- 870-882
- ISSN
- 1364-6613 (ISSN-L)
- OpenAlex
- W2894443551
- Cited by
- 267
- References
- 129
- FWCI
- 3.43
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
