Full bibliography

Butlin et al. (2023) propose a rubric for evaluating AI systems based on indicator properties derived from existing theories of consciousness, suggesting that while current AIs do not possess consciousness, these indicators are pivotal for future developments towards artificial consciousness. The current paper critiques the approach by Butlin et al., arguing that the complexity of consciousness, characterized by subjective experience poses significant challenges for its operationalization and measurement, thus complicating the replication in AI. The commentary further explores the limitations of current methodologies in artificial consciousness research, pointing to the necessity of out-of-the-box thinking and the integration of individual differences research in cognitive psychology, particularly in the areas of attention, cognitive control, autobiographical memory, and Theory of Mind (ToM), to advance the understanding and development of artificial consciousness.