Séminaire ayant lieu dans le cadre du Doctorat en informatique cognitive, en alliance avec l'ISC et le CRIA

Titre : The challenge of modeling the acquisition of mathematical concepts

Alberto TESTOLIN

Jeudi le 1er avril 2021 à 10h30

Lien zoom (pirère de bien identifier votre nom et prénom, une salle d'attente sera active): https://uqam.zoom.us/j/84473395235 

Résumé

Mathematics is one of the most impressive achievements of human cultural evolution. Despite we perceive it as being overly abstract, it is widely believed that mathematical skills are rooted into a phylogenetically ancient “number sense”, which allows us to approximately represent quantities. However, the relationship between number sense and the subsequent acquisition of symbolic mathematical concepts remains controversial. In this seminar I will discuss how recent advances in AI and deep learning research might allow to investigate how the acquisition of numerical concepts could be grounded into sensorimotor experiences. Success in this challenging enterprise would have immediate implications for cognitive science, but also far-reaching impact for educational practice and for the creation of the next generation of intelligent machines.

References:

1) Zorzi, M., & Testolin, A. (2018). An emergentist perspective on the origin of number sense. Philosophical Transactions of the Royal Society B: Biological Sciences, 373(1740), 20170043. https://royalsocietypublishing.org/doi/full/10.1098/rstb.2017.0043

2) Overmann, K. A. (2018). Constructing a concept of number. Journal of Numerical Cognition. 4, 464–493.https://jnc.psychopen.eu/article/view/161/html 

Biographie 

Dr. Alberto Testolin received the M.Sc. degree in Computer Science and the Ph.D. degree in Psychological Sciences from the University of Padova, Italy, in 2011 and 2015, respectively. In 2019 he was Visiting Scholar at the Department of Psychology at Stanford University. He is currently Assistant Professor at the University of Padova, with a joint appointment at the Department of Information Engineering and the Department of General Psychology. He is broadly interested in artificial intelligence, machine learning and cognitive neuroscience. His main research interests are statistical learning theory, predictive coding, sensory perception, cognitive modeling and applications of deep learning to signal processing and optimization. He is an active member of the IEEE Task Force on Deep Learning.