Associate team MetaBrain – Metacognition and Error-Tracking Algorithms in Bio-Realistic Artificial Intelligence Networks
Principal investigators
Frédéric Alexandre, MNEMOSYNE research team, Inria
Sander Bohte, Machine Learning Department, CWI (The Netherlands)
Abstract
Metacognition is the process by which, instead of just learning to associate a response or a behavior to a situation, animals (and mainly primates) monitor the functioning (and particulary errors) of these simple cognitive processes and learn to inhibit automatic responses and to promote instead contextually appropriate behavioral rules. The main learning algorithms classically used in Artificial Intelligence (supervised learning and reinforcement learning) correspond to simple cognitive processes. A large amount of work (including ours) have shown a general structural equivalence between classical and bio-inspired Machine Learning on these topics. Nevertheless, the divergence between algorithms in Artificial Intelligence and Computational Neuroscience is much more important, when we consider metacognition. This motivates the need for preparing future bio-inspired models of metacognition for Artificial Intelligence, in addition to their intrinsic interest for brain sciences, as we propose in this Associate Team.
Keywords: Artificial Intelligence, computational neuroscience, metacognition, Machine Learning