From Pattern Recognition to Abstract Representation: Visual Search and Neural Mechanisms of Structured Pattern Processing in Skilled Basketball Players

Objective: This study employed eye-tracking and ERP to investigate structured pattern processing in basketball players of different skill levels. Methods: A skill-level comparison (skilled vs. less-skilled) was adopted. Participants viewed static point-light displays depicting basketball half-court configurations, with structured (typical tactical formations) and unstructured (random positions) conditions randomly presented. Participants predicted offensive actions while behavioral, eye-tracking, and ERP data were simultaneously recorded. Results: Skilled players showed higher decision accuracy than less-skilled players under structured conditions but not under unstructured conditions, whereas they responded significantly faster under both conditions, with a larger group difference under structured conditions. For pupil diameter, skilled players showed smaller pupil diameters than less-skilled players under both conditions, while within the skilled group pupil diameter was larger under structured than unstructured conditions. Eye-tracking revealed fewer total fixations and a distinct gaze distribution in skilled players—more fixations on opponents and the ball, fewer on open space—along with larger pupil diameters under structured conditions, suggesting enhanced cognitive engagement. ERP data showed that skilled players exhibited larger P300 and LNC amplitudes under structured conditions, reflecting enhanced semantic mapping and schema-based retrieval. Conclusions: These findings are consistent with a theoretical perspective in which expertise may involve a shift from “pattern recognition” toward “abstract representation,” rather than a reduction in neural activity. However, abstract representation, neural information density, and semantic encoding were not directly measured in the present study. The Transformer architecture is therefore offered as a conceptual analogy—its multi-head self-attention corresponds to skilled players’ hierarchical visual sampling, its encoder to semantic mapping (P300 enhancement), and its memory integration to schema-based retrieval (LNC enhancement). These findings provide a basis for structured situational training in sport decision-making.