Overview: This course overview presents a rigorous, interdisciplinary approach to consciousness research that bridges philosophy, neuroscience, information theory, and computational modeling. It emphasizes operational clarity: how to express subjective experience with formal c-descriptions and link those to empirical p-descriptions of physical systems. The material is designed to move the study of consciousness from qualitative debate toward measurable, testable hypotheses that can guide experiments, model-building, and responsible engineering of cognitive systems. What you’ll learn Formal language for experience: How c-descriptions and p-descriptions create a shared vocabulary for mapping subjective reports to neural and computational architectures. Scientific evaluation of theories: How to assess physical c-theories using criteria such as predictive power, falsifiability, and derivation of empirical tests. Complex brain–mind mappings: Why many-to-one and high-dimensional relationships matter, and methods to identify nonlinear mappings between neural dynamics and conscious content. Machine consciousness and ethics: Conceptual distinctions among levels of machine awareness, practical feasibility, and ethical implications for responsibility, rights, and safety. Research methods and tools: Measurement techniques, experimental paradigms, and computational modeling workflows for probing consciousness in biological and artificial systems.
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