Why does a wine expert experience layers of complexity that escape the novice drinking the same wine? Why does red feel warm and comforting to one person but alarming and threatening to another? These two questions—why expertise deepens experience, and why identical stimuli produce different phenomenal qualities across individuals—have traditionally been treated as separate problems in philosophy of mind and cognitive science. This paper proposes a unified explanation grounded in a single cognitive mechanism: structured memory matching.We introduce the concept of phenomenal structure: the hierarchically organized system of categorical distinctions and reinforcement-shaped value associations that each individual builds through learning. Perception, on this account, is the process by which sensory input is matched against this structure. Two dimensions of phenomenal structure are distinguished. Representational granularity—the fineness of categorical distinctions within a domain—determines the layering, differentiation, and depth of phenomenal experience. Associative content—the reinforcement-shaped connections linking each categorical node to value, emotion, memory, and conceptual networks—determines the qualitative character of experience. Together, these dimensions generate a complete account of phenomenal variation across expertise levels, individuals, cultures, and developmental stages.The framework is compatible with, but not dependent upon, predictive processing accounts. It generates ten falsifiable predictions spanning expertise training, cross-cultural phenomenology, developmental trajectories, and clinical populations. It engages directly with classic philosophical puzzles—the inverted spectrum, Mary’s room, philosophical zombies, and the hard problem—offering principled resolutions grounded in cognitive architecture rather than metaphysical stipulation.
Heng Liu (Sat,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: