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

## TL;DR

This paper explores how humans perceive visual objects differently from AI, introducing a new concept called the 'phenomenal gradient' to describe human visual cognition.

## Contribution

The study introduces the novel concept of a 'phenomenal gradient' to describe hierarchical perceptual salience in human visual processing.

## Key findings

- Human visual processing includes shape prioritization, causal inference, and amodal completion.
- AI systems lack the ability to perform context-dependent and interpretative visual inferences.
- The research proposes a framework for future studies on visual consciousness and perception.

## 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.