Periodic Reporting for period 4 - Babylearn (Neural mechanisms of learning in the infant brain : from Statistics to Rules and Symbols)
Reporting period: 2021-03-01 to 2022-02-28
Infant is the most powerful learner: He learns in a few months to master language, complex social interactions, etc. Powerful statistical algorithms, simultaneously acting at the different levels of functional hierarchies have been proposed to explain learning. I propose here that two other elements are crucial. The first is the particular human cerebral architecture that constrains statistical computations. The second is the ability to access a rich symbolic system. In 6 work packages using the complementary information offered by non-invasive brain-imaging techniques, I have studied the neural bases of infant statistical computations and symbolic competence during the first months. The goal was to clarify the specificities of a neural functional architecture that is critical for human learning from the onset of cortical circuits.
Through the project, we were able to show that many functional features characteristics of the human neural architecture described in adults are seen from start revealing a strong biological constraint on the neural networks that are nevertheless modulated by the environment. For example, the superior temporal sulcus, which hosts the verbal and non-verbal human communication system, is deeper on the right side in almost all humans, but its depth is modulated by the term at birth, that is probably by the auditory environment. We also found that phonemes, number and musical pitch are part of the initial representations automatically computed (i.e. even during sleep) by the infant brain. These representations are abstract, in the sense that they are not dependent on a single sensory feature but consist in a multi-level integration, even across the auditory and visual modalities in the case of number.
These representations are robust to local variations and allow conditional statistical computations, that is the computation of the probability of an event given the previous event (i.e. transitional probability). We showed that the computation of transitional probabilities between auditory events is already present at 6 months of gestation in preterm neonates. This computation allows the chunking of artificial stream in triplets in sleeping neonates as in attentive adults. Interestingly, the same operation on quadruplets is not possible at both ages revealing a hard limit of 4 items in the verbal short-term memory which appears to be constant across ages, attention and linguistic expertise.
Finally, infants are not limited to automatic processes but can recover explicit representations, that can even be symbolic allowing the understanding of negation (A and nonA), abstract rules and logic already in the first semester at a preverbal age. As adults, the access to a conscious space is limited by a bottleneck whose duration is longer in infants, around 1 second vs ~300 ms.
To summarize, thanks to the use of state of the art brain imaging techniques, our work has revealed the complex operations human infants are already computing during the first semester to structure their environment and the robust similarities in terms of operations between infants and adults. Thus the difference between ages is not in the impossibility of complex operation by infants but more in the slowness of these operations in infants. These similarities between infants and adults, even for very abstract operations involving the use of symbols and logical reasoning, suggest strong genetic bases structuring a specific human brain architecture underlying these high-level functions.
Through the project, we were able to show that many functional features characteristics of the human neural architecture described in adults are seen from start revealing a strong biological constraint on the neural networks that are nevertheless modulated by the environment. For example, the superior temporal sulcus, which hosts the verbal and non-verbal human communication system, is deeper on the right side in almost all humans, but its depth is modulated by the term at birth, that is probably by the auditory environment. We also found that phonemes, number and musical pitch are part of the initial representations automatically computed (i.e. even during sleep) by the infant brain. These representations are abstract, in the sense that they are not dependent on a single sensory feature but consist in a multi-level integration, even across the auditory and visual modalities in the case of number.
These representations are robust to local variations and allow conditional statistical computations, that is the computation of the probability of an event given the previous event (i.e. transitional probability). We showed that the computation of transitional probabilities between auditory events is already present at 6 months of gestation in preterm neonates. This computation allows the chunking of artificial stream in triplets in sleeping neonates as in attentive adults. Interestingly, the same operation on quadruplets is not possible at both ages revealing a hard limit of 4 items in the verbal short-term memory which appears to be constant across ages, attention and linguistic expertise.
Finally, infants are not limited to automatic processes but can recover explicit representations, that can even be symbolic allowing the understanding of negation (A and nonA), abstract rules and logic already in the first semester at a preverbal age. As adults, the access to a conscious space is limited by a bottleneck whose duration is longer in infants, around 1 second vs ~300 ms.
To summarize, thanks to the use of state of the art brain imaging techniques, our work has revealed the complex operations human infants are already computing during the first semester to structure their environment and the robust similarities in terms of operations between infants and adults. Thus the difference between ages is not in the impossibility of complex operation by infants but more in the slowness of these operations in infants. These similarities between infants and adults, even for very abstract operations involving the use of symbols and logical reasoning, suggest strong genetic bases structuring a specific human brain architecture underlying these high-level functions.
Through the project, we have tested more than 800 infants. The results have been submitted to publications (14 accepted papers in international journals comprising 3 in PNAS and 2 published chapters). They have been presented in 28 international conferences despite COVID, that has stopped many conferences. I presented the results in the media (radio and magazines). Early cognition has interested the public agencies from the Ministries of Education and Health to change the approach around the first 1000 days
Through extended experimental sessions and cutting-edge technology (256-channel net), we overcame the usual limitations of neuroimaging in young participants, such as preverbal infants and exploit immature anatomy (thin skull, hairless scalp) to gain direct access to the content of the infant brain for the first time. We were able to decode phonetic, musical and numerical representations early in life revealing the core systems that newborns rely on to begin structuring their environment.
We have developed new experimental paradigms to demonstrate access to symbolic representations in non-verbal participants. These paradigms have been tested in animals, allowing us to compare the abilities, and their neural bases, of human adults and infants with those of macaques and to show the remarkable specificities of these representations in humans.
We were able to correlate EEG responses and microstructural changes measured in MRI in the same infants. These correlations help to understand how brain maturation influences cognitive development.
Finally, we developed a new concept of event-related variability. It is generally assumed that the brain signal must be averaged over multiple trials to eliminate unwanted variability. In contrast to this approach, we quantified the signal variability after a stimulus and revealed that it was not random activity but corresponded to a structured landscape, which evolves with maturation, in which neuronal trajectories following a stimulus are constrained. This approach opens up new possibilities for sorting responses to events according to their trajectories, even in non-responders, such as infants or comatose patients, and for studying developmental cognitive disorders.
All these results obtained thanks to advanced technologies improving brain imaging methods in young infants (from 6 months of gestation to the end of the first semester after term) have allowed us to better understand the development of the brain networks that underlie the remarkable learning abilities of the human species.
We have developed new experimental paradigms to demonstrate access to symbolic representations in non-verbal participants. These paradigms have been tested in animals, allowing us to compare the abilities, and their neural bases, of human adults and infants with those of macaques and to show the remarkable specificities of these representations in humans.
We were able to correlate EEG responses and microstructural changes measured in MRI in the same infants. These correlations help to understand how brain maturation influences cognitive development.
Finally, we developed a new concept of event-related variability. It is generally assumed that the brain signal must be averaged over multiple trials to eliminate unwanted variability. In contrast to this approach, we quantified the signal variability after a stimulus and revealed that it was not random activity but corresponded to a structured landscape, which evolves with maturation, in which neuronal trajectories following a stimulus are constrained. This approach opens up new possibilities for sorting responses to events according to their trajectories, even in non-responders, such as infants or comatose patients, and for studying developmental cognitive disorders.
All these results obtained thanks to advanced technologies improving brain imaging methods in young infants (from 6 months of gestation to the end of the first semester after term) have allowed us to better understand the development of the brain networks that underlie the remarkable learning abilities of the human species.