2016 Series: Prediction Machines

Professor Andy Clark (Edinburgh)

Andy Clark was appointed to the Chair in Logic and Metaphysics at University of Edinburgh in 2004. Prior to that he had taught at the University of Glasgow, the University of Sussex, Washington University in St Louis, and Indiana University, Bloomington. He was Director of the Philosophy/Neuroscience/Psychology Program at Washington University in St Louis, and Director of the Cognitive Science Program at Indiana University. His research interests include philosophy of mind and artificial intelligence, including robotics, artificial life, embodied cognition, and mind, technology and culture.

 

Lecture 1: Prediction Machines

Biological brains are increasingly cast as ‘prediction machines’: evolved organs forever trying to predict their own streams of incoming sensory stimulation. Rich, world-revealing perception only occurs, these stories suggest, when cascading neuronal activity is able to match the incoming sensory signal with a multi-level stream of apt ‘top-down’ predictions. This blurs the lines between perception, thought, and imagination, revealing them as inextricably tied together. In this talk, I first introduce this general explanatory schema, and then discuss these (and other) implications. I end by asking what all this suggests concerning the fundamental nature of our perceptual contact with the world.

Lecture 2: Busting Out – Two Takes on the Predictive Brain

In this talk, I contrast two ways of understanding the emerging vision of the predictive brain. One way (Conservative Predictive Processing) depicts the predictive brain as an insulated inner arena populated by richly reconstructive representations. The other (Radical Predictive Processing) stresses processes of circular causal influence linking brain, body, and world. Such processes deliver fast and frugal, action-involving solutions of the kind already highlighted by work in robotics and embodied cognition. I present some arguments that seem to favour the more radical reading. This raises questions concerning exactly how best to understand the core notions of prediction and prediction-error minimization themselves.

 

Lecture Three: The Future of Prediction

The ‘predictive processing’ framework shows great promise as a means of both understanding and integrating many of the core information processing strategies underlying perception, thought, and action. But this leaves many questions unanswered. What is the true scope of this story – can it really be a theory of ‘everything cognitive’? Is it falsifiable? Can a story that posits prediction error minimization as cognitive bedrock accommodate the undoubted attractions of novelty and exploration? What can it tell us about specifically human forms of thought and reason? And what, if anything, does it have to say about the nature and possibility of conscious experience itself?