Uploaded March 2023 | Updated September 2026, 1 week ago
Dr. Heather Kosakowski and Prof. Nancy Kanwisher describe their latest journal article being published in Developmental Science regarding a study of selective responses to music in infant brains.
onlinelibrary.wiley.com/journal/14677687
Research Highlights
• Responses to music, speech, and control sounds matched for the spectrotemporal modulation-statistics of each sound were measured from 2- to 11-week-old sleeping infants using fMRI.
• Auditory cortex was significantly activated by these stimuli in 19 out of 36 sleeping infants.
• Selective responses to music compared to the three other stimulus classes were found in non-primary auditory cortex but not in nearby Heschl's Gyrus.
• Selective responses to speech were not observed in planned analyses but were observed in
unplanned, exploratory analyses.
Abstract
Prior studies have observed selective neural responses in adult human auditory cortex to music and speech that cannot be explained by the differing lower-level acoustic properties of these stimuli. Does infant cortex exhibit similarly selective responses to music and speech shortly after birth? To answer this question, we attempted to collect functional magnetic resonance imaging (fMRI) data from 45 sleeping infants (2.0- to 11.9-weeks-old) while they listened to monophonic instrumental lullabies and infant-directed speech produced by a mother. To match acoustic variation between music and speech sounds we (1) recorded music from instruments that had a similar spectral range as female infant-directed speech, (2) used a novel excitation-matching algorithm to match the cochleagrams of music and speech stimuli, and (3) synthesized “model-matched” stimuli that were matched in spectrotemporal modulation statistics to (yet perceptually distinct from) music or speech. Of the 36 infants we collected usable data from, 19 had significant activations to sounds overall compared to scanner noise. From these infants, we observed a set of voxels in non-primary auditory cortex (NPAC) but not in Heschl's Gyrus that responded significantly more to music than to each of the other three stimulus types (but not significantly more strongly than to background scanner noise). In contrast, our planned analyses did not reveal voxels in NPAC that responded more to speech than to model- matched speech, although other unplanned analyses did. These preliminary findings suggest that music selectivity arises within the first month of life.
Examples of the stimuli used in the experiment are available here: heatherkosakowski.wordpress.com/stimuli
All stimuli, code, and data are available for download here: osf.io/8ty34
Dr. Heather Kosakowski and Prof. Nancy Kanwisher describe their latest journal article being published in Developmental Science regarding a study of selective responses to music in infant brains.
onlinelibrary.wiley.com/journal/14677687
Research Highlights
• Responses to music, speech, and control sounds matched for the spectrotemporal modulation-statistics of each sound were measured from 2- to 11-week-old sleeping infants using fMRI.
• Auditory cortex was significantly activated by these stimuli in 19 out of 36 sleeping infants.
• Selective responses to music compared to the three other stimulus classes were found in non-primary auditory cortex but not in nearby Heschl's Gyrus.
• Selective responses to speech were not observed in planned analyses but were observed in
unplanned, exploratory analyses.
Abstract
Prior studies have observed selective neural responses in adult human auditory cortex to music and speech that cannot be explained by the differing lower-level acoustic properties of these stimuli. Does infant cortex exhibit similarly selective responses to music and speech shortly after birth? To answer this question, we attempted to collect functional magnetic resonance imaging (fMRI) data from 45 sleeping infants (2.0- to 11.9-weeks-old) while they listened to monophonic instrumental lullabies and infant-directed speech produced by a mother. To match acoustic variation between music and speech sounds we (1) recorded music from instruments that had a similar spectral range as female infant-directed speech, (2) used a novel excitation-matching algorithm to match the cochleagrams of music and speech stimuli, and (3) synthesized “model-matched” stimuli that were matched in spectrotemporal modulation statistics to (yet perceptually distinct from) music or speech. Of the 36 infants we collected usable data from, 19 had significant activations to sounds overall compared to scanner noise. From these infants, we observed a set of voxels in non-primary auditory cortex (NPAC) but not in Heschl's Gyrus that responded significantly more to music than to each of the other three stimulus types (but not significantly more strongly than to background scanner noise). In contrast, our planned analyses did not reveal voxels in NPAC that responded more to speech than to model- matched speech, although other unplanned analyses did. These preliminary findings suggest that music selectivity arises within the first month of life.
Examples of the stimuli used in the experiment are available here: heatherkosakowski.wordpress.com/stimuli
All stimuli, code, and data are available for download here: osf.io/8ty34








![Continuous-time deconvolutional regression: A method for studying continuous dynamics in naturali...
[full title] Continuous-time deconvolutional regression: A method for studying continuous dynamics in naturalistic data
Cory Shain, MIT
Abstract: Naturalistic experiments are of growing interest to neuroscientists and cognitive scientists. Naturalistic data can be hard to analyze because critical events can occur at irregular intervals, and measured responses to those events can overlap and interact in complex ways. For example, words come quickly enough during naturalistic reading and listening that the brain responses to words likely overlap in time, and inherent variability in word durations can make these responses difficult to identify from data. In this tutorial, I will present continuous-time deconvolutional regression (CDR), a new approach to analyzing naturalistic time series. CDR uses machine learning to estimate impulse response functions from data, but, unlike established methods like finite impulse response modeling, these functions are defined in continuous time. CDR can therefore directly estimate event-related responses in a range of naturalistic experiment types, including fMRI, EEG/MEG, and behavioral measures. The tutorial will demonstrate how to define, fit, and evaluate CDR models, how to test hypotheses in the CDR framework, how to visualize patterns with CDR, and how CDR can be used to relax a range of assumptions about time series data. These steps can be run from the command line using an open-source Python library, with no need for users to write any code.
The sample data+models for the CDR tutorial available here: https://www.dropbox.com/sh/yz4l5745nxz8h7w/AACWgDwfKjhkXlK0L5eM-d25a?dl=0. The files data.zip and models.zip need to be downloaded and extracted.
CDR can be installed with either Anaconda or python+pip. To install with pip, run: pip install https://github.com/coryshain/cdr/archive/refs/tags/v0.5.3.tar.gz
Conda installation instructions are available in the readme at https://github.com/coryshain/cdr. Continuous-time deconvolutional regression: A method for studying continuous dynamics in naturali...](https://i.ytimg.com/vi/fNktSKmckBE/mqdefault.jpg)

