Tutorial M2: Seth and Kanai

Friday July 12 09:00-12:00

M2: Integrated information, predictive coding, and qualia

Anil. K. Seth & Ryota Kanai (Sackler Centre for Consciousness Science and Dep’t of Informatics, University of Sussex, Brighton, U.K.)

Current research in consciousness science must better integrate theory and experiment in developing our understanding of qualia [1]. Two classes of brain theory are now emerging as leading candidates. Integrated information theory (IIT, [2]) proposes that consciousness has to do with the amount of information generated by a neural system as a whole, compared to the sum of its parts. Integrated information (‘phi’) can be operational- ized as a variant of dynamical complexity and compared with similar measures [3,4]. IIT thus highlights informa- tion theory and complexity as key tools for naturalizing consciousness and qualia. Predictive coding (PC) proposes that perception emerges via Bayesian inference: Perceptual content is determined top-down predictive signals arising from generative models of external causes, which are continually modified by bottom-up prediction-error signals [5]. PC thus highlights re-entrant processing and probabilistic inference as key concepts. While both frameworks are powerfully explanatory, IIT is underconstrained by current cognitive neuroscience and difficult to test, while for PC the relationship between conscious and unconscious perception is poorly specified. In this tutorial, we will first provide basic introduction to IIT and PC with special emphasis on their relationship to understanding qualia. To facilitate interdisciplinary discussion, the tutorial does not assume any mathematical background and we will focus on conceptual understanding of the theories rather than math- ematical details. In a later part of the tutorial, we will discuss how these different frameworks might be synthesized into a coherent computational framework.