Seminar
Statistical learning in vision and beyond
Practical information
09 December 2022
2pm-3pm
Place
ENS, room L357 (Physics Department, 3rd floor), 24 rue Lhomond, 75005 Paris
LSP
Abstract:
In my talk, I will present a framework in which Perceptual Learning, Statistical Learning and Rule/Abstract learning are not different types of learning but only differently specialized versions of the fundamental learning process. I will argue that this learning process must be captured in its entirety to successfully integrate learning into complex visual processes. First, I will demonstrate how recent behavioral and neural results in the literature reveal a convergence across perceptual, statistical and rule/abstract learning supporting this framework. Next, I will show why the generalized version of statistical learning can provide the appropriate setup for such a unified treatment of learning in vision and present a computational approach that best accommodates this kind of statistical learning. I will follow up by discussing what plausible neural scheme could feasibly implement this framework and how this scheme can help alleviate the present disconnect between neural measures and their interpretation from the standpoint of learning. I will conclude with a case study, “roving” in visual learning, and by listing directions in the field where statistical learning needs to take steps to approach the level of sophistication required for being the method of choice for advancing our understanding of vision and other cognitive processes in their completeness.
In my talk, I will present a framework in which Perceptual Learning, Statistical Learning and Rule/Abstract learning are not different types of learning but only differently specialized versions of the fundamental learning process. I will argue that this learning process must be captured in its entirety to successfully integrate learning into complex visual processes. First, I will demonstrate how recent behavioral and neural results in the literature reveal a convergence across perceptual, statistical and rule/abstract learning supporting this framework. Next, I will show why the generalized version of statistical learning can provide the appropriate setup for such a unified treatment of learning in vision and present a computational approach that best accommodates this kind of statistical learning. I will follow up by discussing what plausible neural scheme could feasibly implement this framework and how this scheme can help alleviate the present disconnect between neural measures and their interpretation from the standpoint of learning. I will conclude with a case study, “roving” in visual learning, and by listing directions in the field where statistical learning needs to take steps to approach the level of sophistication required for being the method of choice for advancing our understanding of vision and other cognitive processes in their completeness.