Perception and cognition are core processes that transform external sensory signals into internal representations for knowledge construction and understanding, and in visually impaired individuals, this transformation is reorganized through auditory and tactile feedback. To explain how perceptual information evolves into stable cognitive representations under limited sensory bandwidth, this study proposes Phase Transition Hypothesis of Perception and Cognition. The proposed hypothesis models the perceptual–cognitive process as a dynamic phase transition, in which sensory information evolves from fragmented perception into organized cognition. To counteract perceptual bias induced by information collapse, the Perceptual Dynamic Optimization Mechanism adaptively regulates sensory deviations to stabilize the perceptual–cognitive transition, whereas the Cognitive Potential Model, derived from the Free-Energy Principle, elucidates how stable and self-organizing cognition emerges from this dynamic process. A cognitive simulation system and a blind writing navigation experiment are conducted to validate the hypothesis. Experiments demonstrate the proposed adaptive correction of perceptual bias and the phase transition mechanism from perception to cognition.

The study adopted a human-centered perspective to research the financial markets, focusing on identifying variations in eye movement patterns between professional and non-professional traders as they analyze a series of stock charts. Eye movement data was selected as the analysis target based on the hypothesis that it represents a behavioral phenotype indicative of stock analysts' cognitive processes during market analysis. Disparities were identified by conducting variance analysis and the Wilcoxon signed-rank test on statistical metrics derived from eye fixations and saccades. Psychological and behavioral economic interpretations were provided to understand the underlying reasons for these observed patterns. To showcase the practical application potential of the human-centered perspective, eye movement data and human visual characteristics were used to construct visual saliency prediction models of professional stock analysts. Leveraging this human-centered model, we developed two practical application demonstrations specifically designed to support and instruct novice traders. Based on the above demonstrations, a training program was designed that demonstrates how, with ongoing training, the non-professional traders' ability to observe stock charts improves progressively.