Founded in the spirit of facilitating the transition from closed scientific enquiry to a more open model we aim to transcending barriers between disciplines, science and the society.
We foster research at crossroads between interdisciplinary life and health sciences, basic understanding of learning processes and novel education technology/methodology testing and implementation, and digital sciences.
PLEASE NOTE THAT THE DATE HAS CHANGED TO THURSDAY JANUARY 23rd
Lecture by Laurence T Maloney,
Laurence T Maloney is professor of Psychology at New York University. He is currently fellow at IEA for a research project entitled “When to decide not to decide: metacognition and uncertainty”. His general interest is in applications of statistical decision theory to decision-making, perceptual judgments and the planning of movement. Game theory, coordination and cooperation, bluffing. He received his PhD in Psychology from Stanford University in 1985, an MS in Mathematical Statistics – also from Stanford – in 1982 and a BA in Mathematics from Yale University in 1973. In 1987, he shared the Troland Award of the National Academy of Sciences and, in 2008, he received the Humboldt Research Prize. In 2014 he was selected as a Fulbright Scholar and during 2015-2016, was appointed a Guggenheim Fellow. He is also a fellow in several societies.
Sciences in Context is a series of public lectures organized in collaboration with the Institut d'études avancées de Paris by Muriel Mambrini and Pascal Kolbe, aimed at bringing new concepts and perspectives from the frontiers of the social sciences to the CRI community and beyond. The topics of the conference is prepared at a public session of the Practical Philosophy Club on the Friday before each conference, in order to encourage discussion with the guest speaker.
SUBJECT OF THE CONFERENCE
In executing any speeded movement, there is uncertainty about the outcome. A large part of the uncertainty is due to perceptual and motor variability. In economic environments where the possible outcomes of any movement carry reward or punishment, your plans and decisions should reflect knowledge of your own uncertainty, a form of metacognition. In this talk, Laurence T Maloney will present a Bayesian (based on probabilities) theoretic decision model of ideal movement planning based on metacognition, and describe recent experiments testing the model
Gert-Jan Both - CRI
As scientific data sets become richer and increasingly complex, machine learning (ML) tools become more useful and widely applied. Discovering a mechanistic model, rather than predicting the outcome is paramount in the scientific endeavor and its lack in present day ML is limiting further integration of ML in quantitative science. In this talk I will present our development of quantitative tools to extract human interpretable models from quantitative biological and physical data sets. The work combines the predictive power of neural networks with the interpretability of symbolic regression to develop a framework of interpretable AI and discover mechanistic models from biological and physical data.