In order to understand both consciousness and the Freudian unconscious we need to understand the notion of innerness that we apply to the mind. We can partly do so via the use of the theory of conceptual metaphor, and this casts light on a number of related topics.

This work addresses the autonomous organization of biological systems. It does so by considering the boundaries of biological systems, from individual cells to Home sapiens, in terms of the presence of Markov blankets under the active inference scheme—a corollary of the free energy principle. A Markov blanket defines the boundaries of a system in a statistical sense. Here we consider how a collective of Markov blankets can self-assemble into a global system that itself has a Markov blanket; thereby providing an (...) illustration of how autonomous systems can be understood as having layers of nested and self-sustaining boundaries. This allows us to show that: (i) any living system is a Markov blanketed system and (ii) the boundaries of such systems need not be co-extensive with the biophysical boundaries of a living organism. In other words, autonomous systems are hierarchically composed of Markov blankets of Markov blankets—all the way down to individual cells, all the way up to you and me, and all the way out to include elements of the local environment. (shrink)

__Winner of the 2013 Sigourney Award!__ Psychoanalysis seen through Bion's eyes is a radical departure from all conceptualizations which preceded him. In this major contribution to the series _Makers of Modern Psychotherapy_, Joan and Neville Symington concentrate on understanding Bion's concepts in relation to clinical practice, but their book is also accessible to the educated reader who wishes to understand the main contours of Bion's thinking. Rather than following the chronological development of Bion's ideas, each chapter looks in depth at (...) an important theme in his thinking and describes how this contributes to his revolutionary model of the mind. (shrink)

Bayes' Theorem is a simple mathematical formula used for calculating conditional probabilities. It figures prominently in subjectivist or Bayesian approaches to epistemology, statistics, and inductive logic. Subjectivists, who maintain that rational belief is governed by the laws of probability, lean heavily on conditional probabilities in their theories of evidence and their models of empirical learning. Bayes' Theorem is central to these enterprises both because it simplifies the calculation of conditional probabilities and because it clarifies significant features of subjectivist position. Indeed, (...) the Theorem's central insight — that a hypothesis is confirmed by any body of data that its truth renders probable — is the cornerstone of all subjectivist methodology. (shrink)