Topic: classical conditioning

A fundamental and distinguishing feature of human intelligence is that we transfer and accumulate knowledge as a society and across generations. We suggest that for communication and accumulation, explicit (but not implicit) intelligence is likely encapsulated by a network of attractors representing abstractions such as objects or actions and direct connections between them, rather than one that utilizes hidden layers of representation. We propose a network model for explicit human intelligence that is composed of attractors and learned connections as elemental units. As attractors are added a

Recent success in training artificial agents and robots derives from a combination of direct learning of behavioral policies and indirect learning via value functions. Policy learning and value learning employ distinct algorithms that depend upon evaluation of errors in performance and reward prediction errors, respectively. In mammals, behavioral learning and the role of mesolimbic dopamine signaling have been extensively evaluated with respect to reward prediction errors; but there has been little consideration of how direct policy learning might inform our understanding. I’ll discuss our re