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.
Shannon Locke donnera une version de sa soutenance de thèse: "Metacognition for Sensory Motor Tracking"
Thomas va nous présenter Zeljko, Kritikos, et Grove (2019). "Lightness/pitch and elevation/pitch crossmodal correspondences are low-level sensory effects."
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
Numerous studies have demonstrated that the power of ongoing alpha oscillations in the EEG is inversely related to neural excitability, as reflected in spike-firing rate, multi-unit activity, or the hemodynamic fMRI signal. Furthermore, alpha oscillations also affect behavioral performance in perceptual tasks. However, it is surprisingly unclear which latent perceptual or cognitive mechanisms mediate this effect. For example, an open question is whether neuronal excitability fluctuations induced by alpha oscillations affect an observer’s acuity or perceptual bias.
Giovanni présentera Mégevand et al (2019), "Phase resetting in human auditory cortex to visual speech".
In the mammalian brain, sensory representations emerge from the activity of interconnected neurons in large cortical networks. Learning novel representations requires modifications in the
