Differential contributions of the two cerebral hemispheres to temporal and spectral speech feedback control

Proper speech production requires auditory speech feedback control. Models of speech production associate this function with the right cerebral hemisphere while the left hemisphere is proposed to host speech motor programs. However, previous studies have investigated only spectral perturbations of the auditory speech feedback. Since auditory perception is known to be lateralized, with right-lateralized analysis of spectral features and left-lateralized processing of temporal features, it is unclear whether the observed right-lateralization of auditory speech feedback processing reflects a preference for speech feedback control or for spectral processing in general. Here we use a behavioral speech adaptation experiment with dichotically presented altered auditory feedback and an analogous fMRI experiment with binaurally presented altered feedback to confirm a right hemisphere preference for spectral feedback control and to reveal a left hemisphere preference for temporal feedback control during speaking. These results indicate that auditory feedback control involves both hemispheres with differential contributions along the spectro-temporal axis.

Nature Research wishes to improve the reproducibility of the work that we publish. This form provides structure for consistency and transparency in reporting. For further information on Nature Research policies, seeAuthors & Referees and theEditorial Policy Checklist .

Statistics
For all statistical analyses, confirm that the following items are present in the figure legend, table legend, main text, or Methods section.

n/a Confirmed
The exact sample size (n) for each experimental group/condition, given as a discrete number and unit of measurement A statement on whether measurements were taken from distinct samples or whether the same sample was measured repeatedly The statistical test(s) used AND whether they are one-or two-sided Only common tests should be described solely by name; describe more complex techniques in the Methods section.
A description of all covariates tested A description of any assumptions or corrections, such as tests of normality and adjustment for multiple comparisons A full description of the statistical parameters including central tendency (e.g. means) or other basic estimates (e.g. regression coefficient) AND variation (e.g. standard deviation) or associated estimates of uncertainty (e.g. confidence intervals) For null hypothesis testing, the test statistic (e.g. F, t, r) with confidence intervals, effect sizes, degrees of freedom and P value noted Give P values as exact values whenever suitable.

For Bayesian analysis, information on the choice of priors and Markov chain Monte Carlo settings
For hierarchical and complex designs, identification of the appropriate level for tests and full reporting of outcomes Estimates of effect sizes (e.g. Cohen's d, Pearson's r), indicating how they were calculated Our web collection on statistics for biologists contains articles on many of the points above.

Software and code
Policy information about availability of computer code Data collection

Data analysis
For manuscripts utilizing custom algorithms or software that are central to the research but not yet described in published literature, software must be made available to editors/reviewers. We strongly encourage code deposition in a community repository (e.g. GitHub). See the Nature Research guidelines for submitting code & software for further information.

Data
Policy information about availability of data All manuscripts must include a data availability statement. This statement should provide the following information, where applicable: -Accession codes, unique identifiers, or web links for publicly available datasets -A list of figures that have associated raw data -A description of any restrictions on data availability Field-specific reporting Please select the one below that is the best fit for your research. If you are not sure, read the appropriate sections before making your selection. Each trial started with the 2 seconds long acquisition of one functional image. Image acquisition was followed by a pause of 0.5-1.5s after which the CVC pseudoword or the non-speech stimulus was visually presented for 2 seconds. After another pause of 2.5-3.5 seconds, the next image was acquired resulting in a total trial length of 8 seconds. Resting_state: Two 7min runs. One before and one after speech adaptation.
Audio recording of participants utterance. Extraction of F1, COG vowel and fricative lengthes. LMMs to asses whether participants changed their production. whole brain standard SPM 12 spatial preprocessing pipeline + ArtRepair motion correction (Realignment of functional images using rigid body transformation, co-registration, smoothing of images with an isotropic 4mm full-width at half-maximum Gaussian kernel to prepare images for additional motion adjustment with Art Repair, motion adjustment of functional images with ArtRepair to reduce interpolation errors from the realignment step, normalization of functional images to a symmetric brain template via parameters from segmentation of structural scans, final smoothing of images with an isotropic 7 mm full-width at half-maximum Gaussian kernel.
Additional denoising steps for resting state data are described below.

Normalization via segmentation
Data was normalized with a symmetric version of the standard Montreal Neurological Institute (MNI) brain template within the Talairach and Tournoux reference frame. The symmetric version was created by averaging the original version with its R/L flipped version.
Motion correction using rigid body transformation.
Resting state data was further denoised to reduce the impact of physiological noise and motion on results. Physiological noise was removed with the anatomical component-based noise correction method (aCompCor) and 16 orthogonal time-courses in subject-specific WM and CSF ROIs. Further, subject-specific motion parameters and their first derivative (scan-to-scan motion), task-effects and subject-specific time points identified as outliers (scan-to-scan global signal change > 9 and movement more than 2 mm) were regressed out. To isolate low frequency fluctuations, resting-state data were bandpass filtered (0.008-0.09 Hz) scan-to-scan global signal change > 9 and movement more than 2 mm Mass univariate approach Task The GLM contained 3 regressors of interest, modelling the three auditory feedback conditions (no perturbation, vowel perturbation, consonant perturbation). Due to the additional motion adjustment step during preprocessing movementrelated effects were not modelled additionally. Condition-specific regressors were obtained by convoluting the onset and duration of conditions (modelled by boxcar functions) with the canonical hemodynamic response function. To account for the use of a sparse sampling protocol, we adjusted microtime resolution and onset (SPM.T = 64, SPM.T0 =