From the Visible to the Invisible: On the Phenomenal Gradient of Appearance.

Human visual perception organizes object attributes along a 'phenomenal gradient'—a syntactic, hierarchical ordering by perceptual salience—whereas artificial intelligence responses, though geometrically precise, lack capabilities such as shape prioritization, causal inference, amodal completion, and perception of visible invisibles. Comparing human and AI responses to modified square images reveals that human visual processing involves complex mechanisms that model-generated descriptions fail to capture. This work introduces the phenomenal gradient as a descriptive framework and provides an initial comparative analysis that motivates testable hypotheses for future studies, rather than making direct claims about improving AI. The findings suggest a more integrated approach to studying visual consciousness, bridging phenomenology, information theory, and cognitive science.

Study at a glance

Abstract

Background: By exploring the principles of Gestalt psychology, the neural mechanisms of perception, and computational models, scientists aim to unravel the complex processes that enable us to perceive a coherent and organized world. This multidisciplinary approach continues to advance our understanding of how the brain constructs a perceptual world from sensory inputs. Objectives and Methods: This study investigates the nature of visual perception through an experimental paradigm and method based on a comparative analysis of human and artificial intelligence (AI) responses to a series of modified square images. We introduce the concept of a "phenomenal gradient" in human visual perception, where different attributes of an object are organized syntactically and hierarchically in terms of their perceptual salience. Results: Our findings reveal that human visual processing involves complex mechanisms including shape prioritization, causal inference, amodal completion, and the perception of visible invisibles. In contrast, AI responses, while geometrically precise, lack these sophisticated interpretative capabilities. These differences highlight the richness of human visual cognition and the current limitations of model-generated descriptions in capturing causal, completion-based, and context-dependent inferences. The present work introduces the notion of a 'phenomenal gradient' as a descriptive framework and provides an initial comparative analysis that motivates testable hypotheses for future behavioral and computational studies, rather than direct claims about improving AI systems. Conclusions: By bridging phenomenology, information theory, and cognitive science, this research challenges existing paradigms and suggests a more integrated approach to studying visual consciousness.