Periodic Reporting for period 2 - BabyBayes (Bayesian Learning in the Infant Brain)
Período documentado: 2020-11-01 hasta 2021-10-31
The unique cognitive abilities observed in humans do not suddenly emerge in adulthood, but are rather deeply rooted in early infancy. Over the past decades, research in developmental psychology has repeatedly evidenced infants’ amazing learning abilities, dispelling the original vision of a neurologically insufficient and cognitively confused infant. However, developmental psychology still lacks an algorithmic understanding of early learning mechanisms, integrating cognitive computations with brain processes. Crucially, this knowledge gap has been holding the field back from having the resources to design theory-driven screening procedures with young infants in order to identify early markers of atypical development, before these deficits lead to profound delays. The development of non-invasive brain imaging techniques has recently opened the black-box of the infant brain, and, we are now at a turning point where we can move from the description of what infants can learn to the exploration of how the typically developing brain implements its learning strategies.
The present project aims at bringing together developmental psychology, neuroscience and computational modelling to unravel the brain mechanisms underlying typically developing infants’ learning skills. The specific objectives were (1) to learn fNIRS, a neuroimaging technique compatible with infant research, and (2) to explore predictive coding in the infant brain, a neural coding strategy.
During the implementation of this project, I learnt fNIRS during the outgoing phase, under the supervision of Richard Aslin. Due to important delays in the recruitment of infant participants, I primarily implemented this technique with adult participants, exploring the neural signature of speech and face processing. I was able to evidence clear neural responses to both stimuli from adult participants. The manuscript is under preparation. Due to the COVID crisis, I was unable to use this technique with infant participants, and I was forced to put the project on hold during that time. Instead, I wrote a review paper on steady-state evoked potentials, an EEG analysis technique particularly relevant with infant populations. This paper was published in 2022. During the return phase of the project, I implemented the EEG experimental part of the project, exploring predictive processes in the infant brain. I tested a total of 58 4-month-old infants, and I was able to include 36 of these infants in the study. Infants were presented with audio-visual events, with some auditory cues predicting the appearance of an image 1 sec after on the screen. The analysis of the recoded EEG activity indicate that infants quickly learn these audio-visual associations. Besides, the analyses revealed enhanced visual activity in response to unexpected vs expected visual omissions. Overall, these results indicate that predictive coding is a neural coding strategy available to the infant brain very early on.
The present project aims at bringing together developmental psychology, neuroscience and computational modelling to unravel the brain mechanisms underlying typically developing infants’ learning skills. The specific objectives were (1) to learn fNIRS, a neuroimaging technique compatible with infant research, and (2) to explore predictive coding in the infant brain, a neural coding strategy.
During the implementation of this project, I learnt fNIRS during the outgoing phase, under the supervision of Richard Aslin. Due to important delays in the recruitment of infant participants, I primarily implemented this technique with adult participants, exploring the neural signature of speech and face processing. I was able to evidence clear neural responses to both stimuli from adult participants. The manuscript is under preparation. Due to the COVID crisis, I was unable to use this technique with infant participants, and I was forced to put the project on hold during that time. Instead, I wrote a review paper on steady-state evoked potentials, an EEG analysis technique particularly relevant with infant populations. This paper was published in 2022. During the return phase of the project, I implemented the EEG experimental part of the project, exploring predictive processes in the infant brain. I tested a total of 58 4-month-old infants, and I was able to include 36 of these infants in the study. Infants were presented with audio-visual events, with some auditory cues predicting the appearance of an image 1 sec after on the screen. The analysis of the recoded EEG activity indicate that infants quickly learn these audio-visual associations. Besides, the analyses revealed enhanced visual activity in response to unexpected vs expected visual omissions. Overall, these results indicate that predictive coding is a neural coding strategy available to the infant brain very early on.
The objective of this project was to acquire transferable knowledge with a novel and emerging neuroimaging technique compatible with infant research: functional near-infrared spectroscopy (fNIRS), and use this technique to investigate the presence of anticipatory predictive signals in the infant brain as a viable neural learning strategy. The project also involved the adaptation of this experimental protocol to a more classic brain imaging technique: EEG, in order to investigate the dynamics of prediction signals in the infant brain.
Over the course of this project, I was able to conceptualize and design a novel experimental paradigm to investigate predictive processing in the infant brain using fNIRS. I successfully implemented this experimental paradigm to test infant participants.
I participated in setting up a fully functional environment to welcome families and test infant participants. Unfortunately, I had to face unforeseen hardware issues, and infant recruitment issues preventing us from testing participants until December 2019. In the meantime, I was able to get further hands-on training with this technique, by running an alternative fNIRS experiment with adult participants. A manuscript is currently under preparation.
I started piloting the fNIRS study in late December 2019, and I started collecting the actual experimental data in February 2020. I collected data from 15 infants up to mid-March 2020. In parallel, I started piloting the EEG experiment with infants at the beginning of March 2020. I was able to collect pilot data from three babies up to mid-March 2020. Unfortunately, due to COVID restrictions, the lab stopped infant testing in the middle of March 2020.
During that time, I wrote a review article on steady-state evoked potentials, an EEG analysis technique particularly relevant for infant research.
When I returned back to my European institution, I implemented the EEG version of the project, and as soon as the sanitary situation allowed, I started collecting data. I was able to test 58 4-month-old infants, and I included 36 of them in the analyses. I tested the predictive coding hypothesis, and evidenced predictive signals in a learning task. Some details of the data analysis are still undergoing, but I had the opportunity to disseminate this work at an international conference and in various lab seminars in Paris, Brussels, and Marseille. The manuscript is currently being prepared for further dissemination.
Over the course of this project, I was able to conceptualize and design a novel experimental paradigm to investigate predictive processing in the infant brain using fNIRS. I successfully implemented this experimental paradigm to test infant participants.
I participated in setting up a fully functional environment to welcome families and test infant participants. Unfortunately, I had to face unforeseen hardware issues, and infant recruitment issues preventing us from testing participants until December 2019. In the meantime, I was able to get further hands-on training with this technique, by running an alternative fNIRS experiment with adult participants. A manuscript is currently under preparation.
I started piloting the fNIRS study in late December 2019, and I started collecting the actual experimental data in February 2020. I collected data from 15 infants up to mid-March 2020. In parallel, I started piloting the EEG experiment with infants at the beginning of March 2020. I was able to collect pilot data from three babies up to mid-March 2020. Unfortunately, due to COVID restrictions, the lab stopped infant testing in the middle of March 2020.
During that time, I wrote a review article on steady-state evoked potentials, an EEG analysis technique particularly relevant for infant research.
When I returned back to my European institution, I implemented the EEG version of the project, and as soon as the sanitary situation allowed, I started collecting data. I was able to test 58 4-month-old infants, and I included 36 of them in the analyses. I tested the predictive coding hypothesis, and evidenced predictive signals in a learning task. Some details of the data analysis are still undergoing, but I had the opportunity to disseminate this work at an international conference and in various lab seminars in Paris, Brussels, and Marseille. The manuscript is currently being prepared for further dissemination.
Overall, I was able to develop an innovative experimental paradigm to investigate predictive processing in the infant brain using fNIRS and EEG. The unforeseen COVID crisis nonetheless severely impacted the progress of this project, and I was able to implement only the EEG version of the study. This experiment opens up a unique window onto the neural dynamics of predictive signaling in the infant brain, and provides crucial insights in our understanding of infant learning abilities. Preliminary results indicate that predictive processing in already functional in 4-month-old infants. Importantly, these results suggest that top-down mechanisms support early cognition very early on, and might support early learning mechanisms in typically developing infants. This work represents a first step towards characterizing typical brain function during development, and opens up exciting perspectives for the understanding of atypical development. Further research should explore how predictive processes are impacted in various developmental disorders.
Besides, the adult dataset acquired instead of the planned infant study aimed at investigating the brain networks involved in face and speech processing using fNIRS, a relatively novel neuroimaging technique. Importantly our results show that speech and face specific responses can be recorded using fNIRS. The technique and/or the proposed experimental design however did not allow us to pinpoint interactions between the two processing streams. These results document audiovisual processing using fNIRS, demonstrating the potential and limits of the technique to study adult cognition.
Finally, I wrote a review paper on steady-state evoked potentials, an EEG analysis method particularly relevant for infant population. I hope that this publication will encourage more developmental scientists to use and develop innovative neuroimaging techniques adapted to the constraints of infant research.
Besides, the adult dataset acquired instead of the planned infant study aimed at investigating the brain networks involved in face and speech processing using fNIRS, a relatively novel neuroimaging technique. Importantly our results show that speech and face specific responses can be recorded using fNIRS. The technique and/or the proposed experimental design however did not allow us to pinpoint interactions between the two processing streams. These results document audiovisual processing using fNIRS, demonstrating the potential and limits of the technique to study adult cognition.
Finally, I wrote a review paper on steady-state evoked potentials, an EEG analysis method particularly relevant for infant population. I hope that this publication will encourage more developmental scientists to use and develop innovative neuroimaging techniques adapted to the constraints of infant research.