Some of my past and current research looks at "decisions from  experience,” i.e., decisions based on the personally experienced outcomes of past choices, along the lines of reinforcement learning models and how such learning and updating is related to and differs from the way in which people and other intelligent agents use other sources of information, e.g., vicarious feedback (anecdotal/social and/or in the form of statistical distributions of outcomes) or science- or model-based outcome predictions to make “decisions from description.”  What happens when these different sources of foreca

The thesis explores the relationship between social and phonological perception, relying on case studies from Modern Hebrew (MH). The social setting is the ethnically-based dichotomy between "Mizrahi" (Middle Eastern and North-African background) and "Ashkenazi" (European background) Jewish-Israelis.

Un programme à grande échelle pour enrichir les pratiques pédagogiques des enseignants

Would you like to complete your internship in a prestigious institution - Ecole Normale Supérieure Ulm - and a dynamic and innovative environment? Come and join our LAAC (Language Acquisition Across Cultures) team.

Would you like to complete your internship in a prestigious institution - Ecole Normale Supérieure Ulm - and a dynamic and innovative environment? Come and join our LAAC (Language Acquisition Across Cultures) team.

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.

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.