Daniel Montoya

Can computation alone generate consciousness, or can it merely simulate its outward appearance? This presentation proposes a three-constraint framework for evaluating claims of artificial consciousness based on semantic grounding, phenomenal binding, and physical realization. First, we examine whether subjective experience can be reduced to computation, arguing that consciousness may depend upon psychophysical principles not captured by classical digital architectures. I.e. Consciousness may depend upon biological and embodied processes that are inseparable from the dynamics of living tissue and therefore not fully reproducible through abstract computation. Second, we revisit the problem of semantic understanding in contemporary Artificial Intelligence (AI). Although large language models increasingly exhibit behaviors associated with emotional intelligence, self-reflection, and theory of mind, such performances may reflect sophisticated statistical inference rather than intrinsic understanding, extending concerns raised by Searle's Chinese Room argument (Searle, 1980). In humans, pattern recognition is one of many unconscious processes contributing to conscious thought. In large language models, by contrast, statistical pattern matching is the principal mechanism generating outputs that may appear thoughtful despite the absence of subjective experience. Third, we argue that the phenomenal binding problem remains unresolved for current computational systems. Human consciousness presents itself as a unified experiential field, whereas digital processes are implemented through distributed and discrete operations whose relationship to phenomenal unity remains unclear. Taken together, these three constraints suggest that functional equivalence and behavioral sophistication may be insufficient criteria for attributing consciousness, perhaps rendering the Turing test inconsequential. The framework developed here distinguishes simulation from subjective awareness and identifies specific explanatory gaps that future theories of machine consciousness must address. The emergence of bioelectronic and organoid-based intelligences may provide a critical testing ground for competing theories of consciousness, occupying a conceptual space between conventional digital computation and biological nervous systems. As these technologies advance, they may force a more precise articulation of the mechanisms, substrates, and conditions necessary for conscious experience. Rather than asking whether AI behaves as if it were conscious, we propose a more fundamental question: what conditions would have to be satisfied for consciousness to arise at all?

Daniel Montoya, Ph.D., is Associate Professor of Psychology at Fayetteville State University. Trained in biological psychology and neuroscience, he received his doctorate from the National University of Córdoba and completed postdoctoral research at Duke University, Wake Forest University, and Bowling Green State University. His research examines the intersection of neuroscience, artificial intelligence, and consciousness studies, with a particular focus on the neural correlates of consciousness, machine consciousness, organoid intelligence, and the philosophical foundations of subjective experience.