Graphic and verbal communication are typically thought to work in very different ways. While speech uses a conventionalized vocabulary that is acquired from children’s environments, drawing is assumed to reflect the articulation of how people see and think, with learning based on “artistic talent.” Yet, research from linguistics and cognitive science upends these assumptions, suggesting that these domains are actually not so distinctive.
Learning and decision making have hitherto been investigated almost exclusively in multicellular neural organisms. Yet, evidence for learning and decision making have been described in single celled organisms : ciliates and slime molds for instance. In this conference, in the first part of my talk, I will focus on decision making in slime molds and explore various frameworks: nutritional geometry, speed versus accuracy trade-off, Weber's law and social influence.
Neural responses and perception of visual inputs strongly depend on the spatial context, i.e., what surrounds a given object or feature. I will discuss our work on developing a visual cortical model based on the hypothesis that neurons represent inputs in a coordinate system that is matched to the statistical structure of images in the natural environment. The model generalizes a nonlinear computation known as normalization, that is ubiquitous in neural processing, and can capture some spatial context effects in cortical neurons.
This is an exciting time for scientists who are interested in cognitive development: there is now a wealth of easily-accessible data that can be used to ask interesting questions about how psychological, neural, and genetic factors affect changes in cognitive functions across the lifespan - and how they differ between individuals. In this talk, I'll describe several studies that apply individual-differences methods to large-scale, sometimes longitudinal datasets that include cognitive and biological information.
De l’étude du traitement du signal à l’étude du développement et l’acquisition du langage
The world is structured in countless ways. When cognitive and machine models respect these structures, by factorizing their modules and parameters, they can achieve remarkable accuracy and generalization. In this talk, I will discuss our work investigating the factorizations of objects, relations, and physics in both humans and machines.
