Evidence map›Paper›PMID 35060904›Full record

ArticleeLife2022

Parallel processing in speech perception with local and global representations of linguistic context.

Christian Brodbeck, Shohini Bhattasali, Aura A L Cruz Heredia, Philip Resnik, Jonathan Z Simon, Ellen Lau

Abstract read
In one paragraph

Article in eLife, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers.

0numbers the graph read from it
0cells of the map it votes in
50citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

50 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Neural and Behavioral Changes in Older Adults from Auditory-Cognitive Training.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2025
    Article
  14. Article
  15. Article
  16. Article
  17. Neural Dynamics of the Processing of Speech Features: Evidence for a Progression of Features from Acoustic to Sentential Processing.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025
    Article
  18. Article
  19. Neural Bases of Proactive and Predictive Processing of Meaningful Subword Units in Speech Comprehension.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025
    Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Christian BrodbeckDepartment of Psychological Sciences, University of Connecticut, Storrs, United States.ORCID https://orcid.org/0000-0001-8380-639X
Shohini BhattasaliDepartment of Linguistics, University of Maryland, College Park, United States.ORCID https://orcid.org/0000-0002-6767-6529
Aura A L Cruz HerediaDepartment of Linguistics, University of Maryland, College Park, United States.
Philip ResnikDepartment of Linguistics, University of Maryland, College Park, United States.ORCID https://orcid.org/0000-0002-6130-8602
Jonathan Z SimonInstitute for Systems Research, University of Maryland, College Park, United States.ORCID https://orcid.org/0000-0003-0858-0698
Ellen LauDepartment of Linguistics, University of Maryland, College Park, United States.

Funding

Auditory Scene Analysis and Temporal Cortical ComputationsR01DC014085 · NIDCD · UNIV OF MARYLAND, COLLEGE PARK · PI SIMON, JONATHAN Z. · 2015 to 2019
$1.5M
NIDCD NIH HHS R01 DC014085
6 · The paper itself

Abstract

Speech processing is highly incremental. It is widely accepted that human listeners continuously use the linguistic context to anticipate upcoming concepts, words, and phonemes. However, previous evidence supports two seemingly contradictory models of how a predictive context is integrated with the bottom-up sensory input: Classic psycholinguistic paradigms suggest a two-stage process, in which acoustic input initially leads to local, context-independent representations, which are then quickly integrated with contextual constraints. This contrasts with the view that the brain constructs a single coherent, unified interpretation of the input, which fully integrates available information across representational hierarchies, and thus uses contextual constraints to modulate even the earliest sensory representations. To distinguish these hypotheses, we tested magnetoencephalography responses to continuous narrative speech for signatures of local and unified predictive models. Results provide evidence that listeners employ both types of models in parallel. Two local context models uniquely predict some part of early neural responses, one based on sublexical phoneme sequences, and one based on the phonemes in the current word alone; at the same time, even early responses to phonemes also reflect a unified model that incorporates sentence-level constraints to predict upcoming phonemes. Neural source localization places the anatomical origins of the different predictive models in nonidentical parts of the superior temporal lobes bilaterally, with the right hemisphere showing a relative preference for more local models. These results suggest that speech processing recruits both local and unified predictive models in parallel, reconciling previous disparate findings. Parallel models might make the perceptual system more robust, facilitate processing of unexpected inputs, and serve a function in language acquisition.

Indexed as

LanguageLinguisticsSpeech PerceptionBrainComprehensionFemaleHumansLinear ModelsMagnetoencephalographyMaleSensationTemporal LobeYoung AdultentropyhumanMEGneurosciencesurprisaltemporal response functions

Identifiers

PMID35060904
PMCPMC8830882

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.