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Place Campus Saint-Germain-des-Prés, U. Paris Cité
Marie Garin and Dimitri Keriven Serpollet from the Centre Borelli will present their work at the Borelli@Saints-Pères seminar.
Speaker 1: Marie Garin
Titre: Multilevel atlas comparisons reveal divergent evolution of the primate brain
Abstract: The evolution of the primate brain is a long-standing matter of debate. Although prior studies have claimed expansion in the frontal lobe of humans, more recent studies have challenged this assertion. We therefore devised a methodology to evaluate the relative expansion of different brain areas based on available digital atlases that we called prediction interval deviation matrices. We ultimately found disproportionally greater expansion in the frontal and parietal lobes of catarrhini when compared to other mammalian species. Our results suggest that the brain of catarrhini prioritized the development of regions related to higher cognitive functions over auditory or visual areas.
Speaker 2: Dimitri Keriven Serpollet
Title: Understanding pilot’s sensorimotricity: from vestibular perception to mental workload assessment
Abstract: The aeronautical sector is looking for innovations to improve flight safety. In this context, my work focused on two main points. On the one hand, we analyzed the differences in perception between different planes of space in the absence of visual cues, keeping in mind the issue of sensory illusions. We showed that a cohort of professional helicopter pilots were more accurate in the frontal plane than in the sagittal plane (p<0.01). However, we found no significant difference between either left or right roll inclinations (p=0.51) or between forward and backward pitch inclinations (p=0.59). On the other hand, we created a predictive model of mental workload (MWL) under ecological conditions. The proposed model is a data-driven machine learning approach that determines the most accurate predictors of mental workload from a set of measured signals. custom paradigm, our algorithm shows good performance (AUC = 0.804 ± 0.086) and produces an interpretable list of the most relevant features for MWL estimation. These results are a first step in determining the perceptual-motor profile of pilots, which could be of great use for their training and longitudinal follow-up.