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

Abstract
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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 non-identical 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.

Data availability

The raw data and predictors used in this study are available for download from Dryad at https://doi.org/10.5061/dryad.nvx0k6dv0

The following data sets were generated

1. Brodbeck C
2. Bhattasali S
3. Cruz Heredia A
4. Resnik P
5. Simon J
6. Lau E
(2022) Data from: Parallel processing in speech perception with local and global representations of linguistic context
Dryad Digital Repository, doi:10.5061/dryad.nvx0k6dv0.

http://dx.doi.org/10.5061/dryad.nvx0k6dv0

Article and author information

Author details

Christian Brodbeck

Department of Psychological Sciences, University of Connecticut, Storrs, United States

For correspondence
christianbrodbeck@me.com

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0001-8380-639X
Shohini Bhattasali

Department of Linguistics, University of Maryland, College Park, United States

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0002-6767-6529
Aura AL Cruz Heredia

Department of Psychology, University of Pennsylvania, Philadelphia, United States

Competing interests
The authors declare that no competing interests exist.
Philip Resnik

Department of Linguistics, University of Maryland, College Park, United States

Competing interests
The authors declare that no competing interests exist.
Jonathan Z Simon

Institute for Systems Research, University of Maryland, College Park, United States

Competing interests
The authors declare that no competing interests exist.
Ellen Lau

Department of Linguistics, University of Maryland, College Park, United States

Competing interests
The authors declare that no competing interests exist.

Funding

University of Maryland (BBI Seed Grant)

Jonathan Z Simon
Ellen Lau

National Science Foundation (BCS-1749407)

Ellen Lau

National Institutes of Health (R01DC014085)

Jonathan Z Simon

National Science Foundation (SMA-1734892)

Jonathan Z Simon

Office of Naval Research (MURI Award N00014-18-1-2670)

Philip Resnik

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Human subjects: The study was approved by the IRB of the University of Maryland under the protocol titled "MEG Studies of Speech and Language Processing" (reference # 01153), on August 22, 2018 and September 9, 2019 (approval duration: 1 year). All participants provided written informed consent prior to the start of the experiment.

Copyright

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.