DOI: 10.5061/dryad.nvx0k6dv0
title: Data from: Parallel processing in speech perception with local and global representations of linguistic context

description: 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.
description: <strong>MEG Data</strong> 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. <strong>Predictor
 Variables</strong> 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.
 <strong>Source Localization</strong>
 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.