# Expertise-dependent mental representation in chess: evaluation and comparisons based on structural dimensional analysis-motoric

## TL;DR

The study explores how chess players of different skill levels mentally represent chess strategies, finding similarities between intermediate and expert players.

## Contribution

It introduces Structural Dimensional Analysis-Motoric as a novel method to evaluate mental representations in chess based on sorting preferences.

## Key findings

- Experts and intermediate players showed significant similarity in mental representation of chess motifs.
- No significant differences were found between experts/intermediates and novices.
- The method highlights potential for training systems and virtual players in chess.

## Abstract

Research findings underline that human behavior and decisive action significantly depend on knowledge accessibility in long-term memory (LTM). For this purpose, various methods have been conducted and applied to help researchers gain insights into LTM functioning. These methods are based on traditional low-cost instruments (e.g., think-aloud protocols, memory protocols, questionnaires, a. o.) as well as modern high-cost technologies.

Furthermore, an emerging method that evolves traditional research techniques in a digitalized environment is Structural Dimensional Analysis-Motoric. This analysis is based on participants’ preferences regarding the closeness of given task-related concepts (TRCs) during a sorting process. From this perspective, chess is a highly cognitive domain involving an immense amount of specific knowledge—a reason it became a prominent field in cognitive research. The present study aims to examine how strategy-related patterns (meaningful and interconnected standardized chess motifs) are incorporated into the LTM of chess players, depending on their expertise (novices, intermediates, and high-level players).

The analysis shows a significant similarity between experts and intermediate chess players but no significant results for the comparisons between experts and intermediates to novices.

Researchers should make efforts to expand mental representation research in chess, for example, by manipulating a variety of strategy-related patterns (e.g., critical openings, middle games, and endgame situations) and/or enhancing the difficulty of the TRCs. The results can be applied to the further development of augmented feedback (e.g., assistive training systems) and virtual players (platforms).