The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Motor origin of temporal predictions in auditory attention
Open accessAbstract
Significance: How the motor system participates in auditory perception is unknown. In a magnetoencephalography experiment involving auditory temporal attention, we show that the left sensorimotor cortex encodes temporal predictions, which drive the precise temporal anticipation of forthcoming sensory inputs. This encoding is associated with bursts of beta (18–24 Hz) neural oscillations that are directed toward auditory regions. Our data also show that the production of overt movements improves the quality of temporal predictions and augments auditory task performance. These behavioral changes are associated with increased signaling of temporal predictions in right-lateralized frontoparietal associative regions. This study points at a covert form of auditory active sensing, and emphasizes the fundamental role of motor brain areas and actual motor behavior in sensory processing.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Control of goal-directed and stimulus-driven attention in the brain
Corbetta, M., & Shulman, G. L. (2002) · Nature Reviews Neuroscience · doi:10.1038/nrn755 · Cited by 12,881
Top-Down Versus Bottom-Up Control of Attention in the Prefrontal and Posterior Parietal Cortices
Buschman, T. J., & Miller, E. K. (2007) · Science · doi:10.1126/science.1138071 · Cited by 2,380
Visual areas exert feedforward and feedback influences through distinct frequency channels
Bastos, A. M., Vezoli, J., Bosman, C. A., Schoffelen, J.-M., Oostenveld, R., Dowdall, J. R., De Weerd, P., Kennedy, H., & Fries, P. (2015) · Neuron · doi:10.1016/j.neuron.2014.12.018 · Cited by 1,320
Sensorimotor synchronization: a review of recent research (2006–2012).
Repp, B. H., & Su, Y.-H. (2013) · Psychonomic Bulletin & Review · doi:10.3758/s13423-012-0371-2 · Cited by 1,204
van Kerkoerle, T., Self, M. W., Dagnino, B., Gariel-Mathis, M.-A., Poort, J., van der Togt, C., & Roelfsema, P. R. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1402773111 · Cited by 918
Corollary discharge across the animal kingdom
Crapse, T. B., & Sommer, M. A. (2008) · Nature Reviews Neuroscience · doi:10.1038/nrn2457 · Cited by 789
A synaptic and circuit basis for corollary discharge in the auditory cortex
Schneider, D. M., Nelson, A., & Mooney, R. (2014) · Nature · doi:10.1038/nature13724 · Cited by 580
Active sensation: Insights from the rodent vibrissa sensorimotor system
Kleinfeld, D., Ahissar, E., & Diamond, M. E. (2006) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2006.06.009 · Cited by 374
All in a Sniff: Olfaction as a Model for Active Sensing
Wachowiak, M. (2011) · Neuron · doi:10.1016/j.neuron.2011.08.030 · Cited by 353
Fast and Slow Oscillations in Human Primary Motor Cortex Predict Oncoming Behaviorally Relevant Cues
Saleh, M., Reimer, J., Penn, R., Ojakangas, C. L., & Hatsopoulos, N. G. (2010) · Neuron · doi:10.1016/j.neuron.2010.02.001 · Cited by 281
Cited by
A New Unifying Account of the Roles of Neuronal Entrainment
Lakatos, P., Gross, J., & Thut, G. (2019) · Current Biology · doi:10.1016/j.cub.2019.07.075 · Cited by 508
Korolczuk, I., Burle, B., Coull, J. T., & Śmigasiewicz, K. (2021) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01795 · Cited by 528
Keitel, A., Gross, J., & Kayser, C. (2018) · PLOS Biology · doi:10.1371/journal.pbio.2004473 · Cited by 312
Large-scale cortical networks for hierarchical prediction and prediction error in the primate brain
Chao, Z. C., Takaura, K., Wang, L., Fujii, N., & Dehaene, S. (2018) · Neuron · doi:10.1016/j.neuron.2018.10.004 · Cited by 238
Brief segments of neurophysiological activity enable individual differentiation
da Silva Castanheira, J., Orozco Perez, H. D., Misic, B., & Baillet, S. (2021) · Nature Communications · doi:10.1038/s41467-021-25895-8 · Cited by 101
Self-citation
Metadata
- Authors
- Morillon, B., & Baillet, S.
- Venue
- Proceedings of the National Academy of Sciences
- Published
- 2017-10-02
- Volume
- 114
- Issue
- 42
- Pages
- E8913-E8921
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- OpenAlex
- W2764005569
- Cited by
- 350
- References
- 12
- FWCI
- 15.17
- 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 ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Motor origin of temporal predictions in auditory attention. https://app.cochat.ai/papers/10.1073/pnas.1705373114
- The paper
- Morillon, B., & Baillet, S. (2017). Motor origin of temporal predictions in auditory attention. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.1705373114
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.
