The brain and cognitive sciences are hard at work on a great scientific quest — to reverse engineer the human mind and its intelligent behavior. Yet these field are still in their infancy. Not surprisingly, forward engineering approaches that aim to emulate human intelligence (HI) in artificial systems (AI) are also still in their infancy.
Western history of thought abounds with claims that knowledge is valued and sought. Yet people often choose not to know. We call the conscious choice not to seek or use knowledge (or information) deliberate ignorance. Using examples from a wide range of domains, we demonstrate that deliberate ignorance has important functions. We systematize types of deliberate ignorance, describe their functions, discuss their normative desirability, and consider how they can be modeled. We conclude that the desire not to know is no anomaly.
Modern theories of reinforcement learning posit two systems competing for control of behavior: a "model-free" or "habitual" system that learns cached state-action values, and a "model-based" or "goal-directed" system that learns a world model which is then used to plan actions. I will argue that humans can adaptively invoke model-based computation when its benefits outweigh its costs. A simple meta-control learning rule can capture the dynamics of this cost-benefit analysis. Neuroimaging evidence points to the role of cognitive control regions in this computation.
Organisée par Yves Laszlo et Nicolas Baumard.
Cette journée de rencontre vise à promouvoir la collaboration scientifique entre départements et à créer de nouveaux projets interdisciplinaires.
9:30 INTRODUCTION - Yves Laszlo
9:45 COMPUTER SCIENCE
With presentations of collaborations with Biology and Cognitive Sciences
9:45 ‘Recent advances in machine learning’ Francis Bach
Des chercheurs de plusieurs laboratoires spécialisés dans le développement cognitif ont mis en évidence des capacités insoupçonnées chez tous les enfants en bonne santé de 0 à 6 ans : linguistes hors pair, super matheux, athlètes précoces, explorateurs de relations sociales.
Many important questions in education require analysing large and complex datasets including measures of environmental factors, cognitive skills, and educational achievement.
Pour rencontrer Tamar Makin, merci de contacter Frederique de Vignemont, fvignemont@gmail.com
Pour la première fois, quatre grandes écoles ont décidé de faire Portes Ouvertes communes. Situées au coeur de Paris, l’École normale supérieure, l’ESPCI Paris, MINES ParisTech et Chimie ParisTech, toutes membres de l’Université PSL (Paris Sciences & Lettres), ouvriront leurs établissements le samedi 16 février de 13h à 18h. Leur volonté ? Donner aux étudiants l’envie d’intégrer leurs formations, qui couvrent un très large panel de disciplines, et qui offrent de nombreuses passerelles entre elles.
We have access to data from several cohorts that have been following children from birth to later ages, and that have collected large amounts of data on health, cognition, psychopathology and educational achievement. In particular, the Eden cohort has been following 2000 children since birth until 11-12 years of age. The Elfe Cohort is currently following about 11000 children since birth. Other international cohorts are also available, including some with brain imaging and genetic data.
Data analysis can bear on any of the following questions:
Functional neuroimaging has given rise to tens of thousands of publications, but we remain far from a comprehensive understanding the neural basis of mental functions. I will argue that this arises from two fundamental flaws in the way that neuroimaging research has been done.
