Modeling the acquisition of mathematical concepts
Alberto TESTOLIN (U. Padua)
10:30 am (Montreal time) Thursday April 1
zoom: https://uqam.zoom.us/j/84473395235<https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fuqam.zoom…>
Abstract : Mathematics is one of the most impressive achievements of human cultural evolution. Despite the fact that we perceive it as being overly abstract, it is widely believed that mathematical skills are rooted in 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 us to investigate how the acquisition of numerical concepts could be grounded in sensorimotor experiences. Success in this challenging enterprise would have immediate implications for cognitive science, but also far-reaching impact on educational practice and the creation of the next-generation 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<https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Froyalsoci…>
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<https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fjnc.psych…>
Alberto Testolin. M.Sc. in Computer Science and Ph.D. in Psychological Sciences. University of Padova, Italy Formerly Visiting Scholar in the Department of Psychology at Stanford University. He is now Assistant Professor at the University of Padova, with a joint appointment in 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.
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