The first comprehensive treatment of active inference, an integrative perspective on brain, cognition, and behavior used across multiple disciplines. Active inference is a way of understanding sentient behavior—a theory that characterizes perception, planning, and action in terms of probabilistic inference. Developed by theoretical neuroscientist Karl Friston over years of groundbreaking research, active inference provides an integrated perspective on brain, cognition, and behavior that is increasingly used across multiple disciplines including neuroscience, psychology, and philosophy. Active inference puts the action into perception. This book offers the first comprehensive treatment of active inference, covering theory, applications, and cognitive domains. Active inference is a “first principles” approach to understanding behavior and the brain, framed in terms of a single imperative to minimize free energy. The book emphasizes the implications of the free energy principle for understanding how the brain works. It first introduces active inference both conceptually and formally, contextualizing it within current theories of cognition. It then provides specific examples of computational models that use active inference to explain such cognitive phenomena as perception, attention, memory, and planning.

The first comprehensive treatment of active inference, an integrative perspective on brain, cognition, and behavior used across multiple disciplines. Active inference is a way of understanding sentient behavior—a theory that characterizes perception, planning, and action in terms of probabilistic inference. Developed by theoretical neuroscientist Karl Friston over years of groundbreaking research, active inference provides an integrated perspective on brain, cognition, and behavior that is increasingly used across multiple disciplines including neuroscience, psychology, and philosophy. Active inference puts the action into perception. This book offers the first comprehensive treatment of active inference, covering theory, applications, and cognitive domains. Active inference is a “first principles” approach to understanding behavior and the brain, framed in terms of a single imperative to minimize free energy. The book emphasizes the implications of the free energy principle for understanding how the brain works. It first introduces active inference both conceptually and formally, contextualizing it within current theories of cognition. It then provides specific examples of computational models that use active inference to explain such cognitive phenomena as perception, attention, memory, and planning.

"A much-needed synthesis of active inference, a theory of mind that addresses cognition, behavior, intelligence, & mental disorders and which can be extended to explain behavior in all living systems"--

This volume constitutes the papers of the 3rd International Workshop on Active Inference, IWAI 2022, held in Grenoble, France, in conjunction with ECML/PKDD, on September 19, 2022. The 25 revised full papers presented in this book were carefully reviewed and selected from 31 submissions.

This book constitutes the refereed proceedings of the First International Workshop on Active Inference, IWAI 2020, co-located with ECML/PKDD 2020, held in Ghent, Belgium, in September 2020. The 13 full papers along with 6 short papers were thoroughly reviewed and selected from 25 submissions. They are organized in the topical sections on ​active inference and continuous control; active inference and machine learning; active inference: theory and biology.

Exciting new theories in neuroscience, psychology, and artificial intelligence are revealing minds like ours as predictive minds, forever trying to guess the incoming streams of sensory stimulation before they arrive. In this up-to-the-minute treatment, philosopher and cognitive scientist Andy Clark explores new ways of thinking about perception, action, and the embodied mind.

This book constitutes the refereed proceedings of the First International Workshop on Active Inference, IWAI 2020, co-located with ECML/PKDD 2020, held in Ghent, Belgium, in September 2020. The 13 full papers along with 6 short papers were thoroughly reviewed and selected from 25 submissions. They are organized in the topical sections on ​active inference and continuous control; active inference and machine learning; active inference: theory and biology.

A bold, expert, and actionable map for the re-invention of America’s broken mental health care system. “Healing is truly one of the best books ever written about mental illness, and I think I’ve read them all." —Pete Earley, author of Crazy As director of the National Institute of Mental Health, Dr. Thomas Insel was giving a presentation when the father of a boy with schizophrenia yelled from the back of the room, “Our house is on fire and you’re telling me about the chemistry of the paint! What are you doing to put out the fire?” Dr. Insel knew in his heart that the answer was not nearly enough. The gargantuan American mental health industry was not healing millions who were desperately in need. He left his position atop the mental health research world to investigate all that was broken—and what a better path to mental health might look like. In the United States, we have treatments that work, but our system fails at every stage to deliver care well. Even before COVID, mental illness was claiming a life every eleven minutes by suicide. Quality of care varies widely, and much of the field lacks accountability. We focus on drug therapies for symptom reduction rather than on plans for long-term recovery. Care is often unaffordable and unavailable, particularly for those who need it most and are homeless or incarcerated. Where was the justice for the millions of Americans suffering from mental illness? Who was helping their families? But Dr. Insel also found that we do have approaches that work, both in the U.S. and globally. Mental illnesses are medical problems, but he discovers that the cures for the crisis are not just medical, but social. This path to healing, built upon what he calls the three Ps (people, place, and purpose), is more straightforward than we might imagine. Dr. Insel offers a comprehensive plan for our failing system and for families trying to discern the way forward. The fruit of a lifetime of expertise and a global quest for answers, Healing is a hopeful, actionable account and achievable vision for us all in this time of mental health crisis.

Is there a right way to study how the brain works? Following the empiricist's tradition, the most common approach involves the study of neural reactions to stimuli presented by an experimenter. This 'outside-in' method fueled a generation of brain research and now must confront hidden assumptions about causation and concepts that may not hold neatly for systems that act and react. György Buzsáki's The Brain from Inside Out examines why the outside-in framework for understanding brain function has become stagnant and points to new directions for understanding neural function. Building upon the success of 2011's Rhythms of the Brain, Professor Buzsáki presents the brain as a foretelling device that interacts with its environment through action and the examination of action's consequence. Consider that our brains are initially filled with nonsense patterns, all of which are gibberish until grounded by action-based interactions. By matching these nonsense "words" to the outcomes of action, they acquire meaning. Once its circuits are "calibrated" by action and experience, the brain can disengage from its sensors and actuators, and examine "what happens if" scenarios by peeking into its own computation, a process that we refer to as cognition. The Brain from Inside Out explains why our brain is not an information-absorbing coding device, as it is often portrayed, but a venture-seeking explorer constantly controlling the body to test hypotheses. Our brain does not process information: it creates it.

A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning. The mathematization of causality is a relatively recent development, and has become increasingly important in data science and machine learning. This book offers a self-contained and concise introduction to causal models and how to learn them from data. After explaining the need for causal models and discussing some of the principles underlying causal inference, the book teaches readers how to use causal models: how to compute intervention distributions, how to infer causal models from observational and interventional data, and how causal ideas could be exploited for classical machine learning problems. All of these topics are discussed first in terms of two variables and then in the more general multivariate case. The bivariate case turns out to be a particularly hard problem for causal learning because there are no conditional independences as used by classical methods for solving multivariate cases. The authors consider analyzing statistical asymmetries between cause and effect to be highly instructive, and they report on their decade of intensive research into this problem. The book is accessible to readers with a background in machine learning or statistics, and can be used in graduate courses or as a reference for researchers. The text includes code snippets that can be copied and pasted, exercises, and an appendix with a summary of the most important technical concepts.