Periodic Reporting for period 3 - SLANG (How the brain learns to see language)
Período documentado: 2023-02-01 hasta 2024-07-31
What is the problem/issue being addressed?
Written information permeates our digital society. Yet, surprisingly little is known about how individual brains allow us to learn how to read, that is, to see language.
What are the overall objectives?
Our overall objectives are to uncover (1) how literacy creates a visual interface with the speech sound system and (2) the mental lexicon and (3) how literacy changes the way we process speech sounds. Meeting these objectives, we will pave the way for a neurobiologically grounded model of literacy learning.
Why is it important for society?
A translation of these insights into educational practice could help the EU and policymakers worldwide to lay the foundation for next-generation literacy instruction programs that are tailored to the specific resources of each individual learner.
Written information permeates our digital society. Yet, surprisingly little is known about how individual brains allow us to learn how to read, that is, to see language.
What are the overall objectives?
Our overall objectives are to uncover (1) how literacy creates a visual interface with the speech sound system and (2) the mental lexicon and (3) how literacy changes the way we process speech sounds. Meeting these objectives, we will pave the way for a neurobiologically grounded model of literacy learning.
Why is it important for society?
A translation of these insights into educational practice could help the EU and policymakers worldwide to lay the foundation for next-generation literacy instruction programs that are tailored to the specific resources of each individual learner.
Work performed
1. We developed magnetic resonance imaging sequences together with a local team of MR physicists.
2. We developed a training protocol to prepare children for magnetic resonance imaging.
3. We developed a behavioral assessment routine together with a local team of research assistants.
4. We developed a data transfer pipeline and set up a secure storage system.
5. We established a logistic workflow for bringing participants to the laboratories.
6. We recruited all participants.
7. We acquired baseline and first follow-up data of all participants (magnetic resonance imaging, behavioral assessment, screening).
8. We are currently analyzing data for our first publication.
Main results achieved
1. We developed a probabilistic statistical framework for a priori power calculation for longitudinal fMRI studies based on Bayesian inference of population prevalence.
2. We developed an integrated approach for preprocessing longitudinal developmental magnetic resonance imaging data in accordance with international open source standards.
3. We generated a software repository on GitHub that is continuously expanded in the course of our methodological and analytical work. We see the material available from this repository after publication as an important scientific source for the Developmental Cognitive Neuroscience community.
1. We developed magnetic resonance imaging sequences together with a local team of MR physicists.
2. We developed a training protocol to prepare children for magnetic resonance imaging.
3. We developed a behavioral assessment routine together with a local team of research assistants.
4. We developed a data transfer pipeline and set up a secure storage system.
5. We established a logistic workflow for bringing participants to the laboratories.
6. We recruited all participants.
7. We acquired baseline and first follow-up data of all participants (magnetic resonance imaging, behavioral assessment, screening).
8. We are currently analyzing data for our first publication.
Main results achieved
1. We developed a probabilistic statistical framework for a priori power calculation for longitudinal fMRI studies based on Bayesian inference of population prevalence.
2. We developed an integrated approach for preprocessing longitudinal developmental magnetic resonance imaging data in accordance with international open source standards.
3. We generated a software repository on GitHub that is continuously expanded in the course of our methodological and analytical work. We see the material available from this repository after publication as an important scientific source for the Developmental Cognitive Neuroscience community.
Progress beyond the state of the art
We consider our power calculation framework and our preprocessing pipeline as significant advances beyond the state of the art in developmental cognitive neuroscience. Specifically, we are working on a probabilistic statistical framework for a priori power calculation for longitudinal fMRI studies based on Bayesian inference of population prevalence. Moreover, we are working on an integrated approach for preprocessing longitudinal developmental magnetic resonance imaging data in accordance with international open source standards.
Expected results
Our expectations are guided by a behavioral model of learning to read distinguishing between a preliterate, an alphabetic and an orthographic stage of learning to read (Frith, 1986). In this model, the transition from the preliterate to the alphabetic stage requires learning of the links between speech sounds (phonemes) and letters and the orthographic stage is then entered when letter strings are stored in memory as whole words to instantly access their meanings.
Following this behavioral model we expect that the alphabetic stage of literacy is reached when phoneme-sensitive nerve cell populations within the left posterior superior temporal sulcus start responding to letters (first hypothesis). Moreover, we expect that the orthographic stage of literacy is reached as soon as the left middle fusiform cortex responds to auditorily presented words and visually presented words with increasing similarity (second hypothesis). Finally, we expect that learning to read alters phonological representations such that a set of uncommitted anterior parts of the left posterior superior temporal gyrus, broadly responding to both phonemic and non-phonemic auditory stimuli at a preliterate age, selectively attune to phonemes in the course of literacy instruction (third hypothesis).
We consider our power calculation framework and our preprocessing pipeline as significant advances beyond the state of the art in developmental cognitive neuroscience. Specifically, we are working on a probabilistic statistical framework for a priori power calculation for longitudinal fMRI studies based on Bayesian inference of population prevalence. Moreover, we are working on an integrated approach for preprocessing longitudinal developmental magnetic resonance imaging data in accordance with international open source standards.
Expected results
Our expectations are guided by a behavioral model of learning to read distinguishing between a preliterate, an alphabetic and an orthographic stage of learning to read (Frith, 1986). In this model, the transition from the preliterate to the alphabetic stage requires learning of the links between speech sounds (phonemes) and letters and the orthographic stage is then entered when letter strings are stored in memory as whole words to instantly access their meanings.
Following this behavioral model we expect that the alphabetic stage of literacy is reached when phoneme-sensitive nerve cell populations within the left posterior superior temporal sulcus start responding to letters (first hypothesis). Moreover, we expect that the orthographic stage of literacy is reached as soon as the left middle fusiform cortex responds to auditorily presented words and visually presented words with increasing similarity (second hypothesis). Finally, we expect that learning to read alters phonological representations such that a set of uncommitted anterior parts of the left posterior superior temporal gyrus, broadly responding to both phonemic and non-phonemic auditory stimuli at a preliterate age, selectively attune to phonemes in the course of literacy instruction (third hypothesis).