Consciousness research has produced many competing theories, ranging from computational to quantum approaches, more than in other natural sciences. This abundance may stem from a lack of clear criteria for how empirical data should constrain such theories. The authors argue consciousness is empirically well-defined and propose a checklist of criteria that empirical theories of consciousness must address. They review 13 influential theories against these criteria, revealing their relative strengths and weaknesses from a strictly empirical perspective.
Theories that identify consciousness with specific causal structures in the brain, such as those requiring feedback loops, are either false or unscientific. Using theorems from computation theory, the authors demonstrate that causal structure theories—including Information Integration Theory (IIT) and Recurrent Processing Theory (RPT)—cannot be empirically tested because any causal structure can be realized by systems that lack consciousness. This undermines the claim that feedforward systems are never conscious and feedback systems always are. The argument suggests that consciousness research should focus instead on functional explanations, such as global workspace or higher-order theories, which are compatible with diverse neural implementations.