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

Citation

Brodbeck, Christian et al. (2022), Data from: Parallel processing in speech perception with local and global representations of linguistic context, Dryad, Dataset, https://doi.org/10.5061/dryad.nvx0k6dv0

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.

Usage Notes

MEG Data

MEG data is in FIFF format and can be opened with MNE-Python. Data has been directly converted from the acquisition device native format without any preprocessing. Events contained in the data indicate the stimuli in numerical order. Subjects R2650 and R2652 heard stimulus 11b instead of 11.

Predictor Variables

The original audio files are copyrighted and cannot be shared, but the make_audio folder contains make_clips.py which can be used to extract the exact clips from the commercially available audiobook (ISBN 978-1480555280).

The predictors directory contains all the predictors used in the original study as pickled eelbrain objects. They can be loaded in Python with the eelbrain.load.unpickle function.

The TextGrids directory contains the TextGrids aligned to the audio files.

Source Localization

The localization.zip file contains files needed for source localization. Structural brain models used in the published analysis are reconstructed by scaling the FreeSurfer fsaverage brain (distributed with FreeSurfer) based on each subject's `MRI scaling parameters.cfg` file. This can be done using the `mne.scale_mri` function. Each subject's MEG folder contains a `subject-trans.fif` file which contains the coregistration between MEG sensor space and (scaled) MRI space, which is used to compute the forward solution.

Funding

University of Maryland, Award: BBI Seed grant