Michael H. Herzog is a professor for Psychophysics at the Brain Mind Institute of the Swiss Federal Technology Institute of Lausanne. He previously studied Mathematics, Biology, and Philosophy at the Universities of Erlangen, Tübingen, and the Massachusetts Institute of Technology. Michael is co-author of the Open Access book Understanding Statistics and Experimental Design (2019). Aaron Schurger is Assistant Professor of Computational Cognitive Neuroscience at Chapman University. He studied computer science at Indiana University, Bloomington and completed his Ph.D. in Psychology and Neuroscience at Princeton University. Throughout his career, Aaron has conducted research on conscious versus non-conscious neural information processing, with a focus on the psychophysics of near-threshold sensory perception. Adrien Doerig is a professor in Cognitive Computational Neuroscience at Freie Universität Berlin. He studied neuroscience and physics at the Swiss Federal Technology Institute of Lausanne, where he completed his Ph.D. Adrien's research focuses on applying artificial neural network models to deepen our understanding human cognition, and using psychophysics, computational modelling, and philosophical approaches to study consciousness scientifically.

• Part I. The Science of Consciousness: History, Methods, and Characteristics: 1. Introduction; 1.1. A historical perspective on consciousness; 1.2. Philosophical perspectives on consciousness; 1.3. What this book has to offer; 1.4. How to read this book; 2. Experimental approaches to consciousness: 2.1 Experimental approaches to states of consciousness; 2.2 Experimental approaches to contents of consciousness; 2.3 Problems, pitfalls, and methodological considerations; 3. Characteristics of consciousness: 3.1 Where in the brain?: The neural correlates of consciousness (NCC); 3.2 When do contents 'reach' consciousness?: The temporal aspects of consciousness; 3.3 Access versus phenomenal consciousness: is there 'overflow'?; 3.4 Is consciousness discrete or graded?; 3.5 Is consciousness unified?; 3.6 Attention and neglect; 3.7 Phylogenetic and ontogenetic emergence of consciousness: Evolution, animals, mirrors, and babies; 3.8 The role of consciousness; 3.9 The quest for generalisability; Part II. Portrayals and Evaluations of Empirical ToCs: 4. What is a theory of consciousness?: 4.1. The self-organizing metarepresentational account (SOMA) Axel Cleeremans; 4.2. Challenge I: the non-conscious alternative; 5. Process theories: 5.1. Predictive processing theory and consciousness (PPT) Anil Seth; 5.2. The global neuronal workspace theory of consciousness (GNWT) Claire Sergent and Daphné Rimsky-Robert; 5.3. Higher order theories (HOTs) and the perceptual reality monitoring theory (PRM) Matthias Michel; 5.4. Challenge II: underconstraining consciousness: the small network argument; 6. Causal structure theories: 6.1. Recurrent processing theory (RPT) Victor A. F. Lamme; 6.2. Integrated information theory (IIT) William Marshall; 6.3 Challenge III: the unfolding argument; 7. Biological theories: 7.1. Dendritic integration theory (DIT) Jaan Aru, Talis Bachmann, Mototaka Suzuki and Matthew E. Larkum; 7.2. The 'felt uncertainty' theory of consciousness (FUT) Mark Solms; 7.3. Challenge IV: the other systems argument; 8. Quantum theories: 8.1. The orchestrated objective reduction theory of consciousness (Orch-OR) Stuart Hameroff; 8.2. Challenges for quantum theories of consciousness; 9. Illusionism: 9.1. The attention schema theory (AST) Michael S. A. Graziano; 9.2. The sensorimotor theory of phenomenal consciousness (SMT) J. Kevin O'Regan; 9.3. Challenges for illusionist theories; Part III. The Science of Consciousness: 10. The science of consciousness: an opinionated perspective: 10.1 Scientific theories of consciousness; 10.2 At the core of current ToCs; 10.3 How to escape the core problems of today's ToCs?; 10.4 Summary; Appendix; References.