Overview
Everyone lives within a Perception Box (PB). What we see and hear—how we interpret it and what it means to us—depends on our brain and learning history. Recognizing that perception is subjective, and understanding why, are key steps to open our minds, counteract bias, and reduce suffering. This project establishes a rigorous theoretical and empirical foundation for the science of the PB. A key requirement is understanding what makes a perception feel the way it does, and why it may feel differently in different people. Integrated Information Theory (IIT) is unique in providing a principled, testable account of the quality of experience. According to IIT, a percept corresponds to a cause–effect structure specified by the substrate of consciousness in its current state. Although triggered by an external stimulus, a percept's feeling/meaning is always intrinsic—consistent with the PB notion.
Aim 1 trains simple model systems (animats) in environments that differ only in stimulus statistics. Using IIT's mathematical framework, we will "unfold" the cause–effect structures (percepts) triggered by the same visual stimulus in different animats, showing that the same stimulus triggers different intrinsic meanings and is perceived as different objects ('segment' vs. 'centered odd').
Aim 2 employs implicit perceptual learning for a firsthand demonstration of the PB. Two groups of subjects will be exposed to streams of visual stimuli embedding distinct statistical regularities unbeknownst to them. We will show how this transforms the percept from a "jumble" of random strokes to a "unitized" configuration (perceptual unitization) and then to a "categorized" object (perceptual categorization), and that the two groups will perceive different objects within the same stimulus (令 vs. 瓜).
Throughout, we emphasize phenomenology rather than behavior or function: what changes is perception itself, not judgements about perception. Together, this will transform the PB from a vivid metaphor into a scientific construct.