The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Low-Frequency Cortical Entrainment to Speech Reflects Phoneme-Level Processing
Open accessAbstract
The human ability to understand speech is underpinned by a hierarchical auditory system whose successive stages process increasingly complex attributes of the acoustic input. It has been suggested that to produce categorical speech perception, this system must elicit consistent neural responses to speech tokens (e.g., phonemes) despite variations in their acoustics. Here, using electroencephalography (EEG), we provide evidence for this categorical phoneme-level speech processing by showing that the relationship between continuous speech and neural activity is best described when that speech is represented using both low-level spectrotemporal information and categorical labeling of phonetic features. Furthermore, the mapping between phonemes and EEG becomes more discriminative for phonetic features at longer latencies, in line with what one might expect from a hierarchical system. Importantly, these effects are not seen for time-reversed speech. These findings may form the basis for future research on natural language processing in specific cohorts of interest and for broader insights into how brains transform acoustic input into meaning.
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Cites
Delorme, A., & Makeig, S. (2004) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2003.10.009 · Cited by 24,371
The cortical organization of speech processing
Hickok, G., & Poeppel, D. (2007) · Nature Reviews Neuroscience · doi:10.1038/nrn2113 · Cited by 5,495
Mechanisms underlying selective neuronal tracking of attended speech at a “cocktail party”
Zion Golumbic, E. M., Ding, N., Bickel, S., Lakatos, P., Schevon, C. A., McKhann, G. M., Goodman, R. R., Emerson, R., Mehta, A. D., Simon, J. Z., et al. (2013) · Neuron · doi:10.1016/j.neuron.2012.12.037 · Cited by 909
Brain mechanisms in early language acquisition
Kuhl, P. K. (2010) · Neuron · doi:10.1016/j.neuron.2010.08.038 · Cited by 798 · cited 1×
“While we have speculated that our findings may have specific contributions from STS, the concordance with ECoG fromSTG suggests that the analysis frameworkwe have outlined may represent an important mechanism for applying findings from the ECoG community into research with a wider variety of subjects including infants…”
Speech rhythms and multiplexed oscillatory sensory coding in the human brain
Gross, J., Hoogenboom, N., Thut, G., Schyns, P., Panzeri, S., Belin, P., & Garrod, S. (2013) · PLoS Biology · doi:10.1371/journal.pbio.1001752 · Cited by 643
Superior Temporal Sulcus—It's My Area: Or Is It?
Hein, G., & Knight, R. T. (2008) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.2008.20148 · Cited by 486
Iverson, P., & Kuhl, P. K. (1995) · The Journal of the Acoustical Society of America · doi:10.1121/1.412280 · Cited by 352
Cited by
A hierarchy of linguistic predictions during natural language comprehension
Heilbron, M., Armeni, K., Schoffelen, J.-M., Hagoort, P., & de Lange, F. P. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2201968119 · Cited by 343
Meyer, L. (2018) · European Journal of Neuroscience · doi:10.1111/ejn.13748 · Cited by 328
Keitel, A., Gross, J., & Kayser, C. (2018) · PLOS Biology · doi:10.1371/journal.pbio.2004473 · Cited by 312
Towards reconstructing intelligible speech from the human auditory cortex
Akbari, H., Khalighinejad, B., Herrero, J. L., Mehta, A. D., & Mesgarani, N. (2019) · Scientific Reports · doi:10.1038/s41598-018-37359-z · Cited by 221
EEG of the Dancing Brain: Decoding Sensory, Motor, and Social Processes during Dyadic Dance
Bigand, F., Bianco, R., Abalde, S. F., Nguyen, T., & Novembre, G. (2025) · The Journal of Neuroscience · doi:10.1523/jneurosci.2372-24.2025 · Cited by 21
Metadata
- Authors
- Di Liberto, G. M., O’Sullivan, J. A., & Lalor, E. C.
- Venue
- Current Biology
- Publisher
- Elsevier BV
- Published
- 2015-10
- Volume
- 25
- Issue
- 19
- Pages
- 2457-2465
- ISSN
- 0960-9822 (ISSN-L)
- OpenAlex
- W2137172783
- Cited by
- 578
- References
- 7
- FWCI
- 42.88
- Type
- journal article
- Language
- en
- Open access
- bronze
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