Connecting the Stars : Mapping Players Preferences and Motivation to In-Game Behaviour

This study investigates how self-perceived intrinsic player motivation and challenge type preferences predict in-game behaviors in String Theory, an action-puzzle top-down 2D space game where the player has to maneuver a spaceship that traverses the sky constellations. Using existing, validated questionnaires (the Intrinsic Motivation to Gameplay inventory, IMG; and Challenge Type Inventory, CHA), we surveyed participants and analyzed gameplay data across six constellation-themed levels. Three key behaviors were examined: aim accuracy when detaching from stars, energy levels maintained during detachment, and time spent orbiting stars. Results revealed significant positive correlations between Competence factor derived from the IMG inventory and all three behaviors, while Physical factor derived from the CHA inventory correlated specifically with aim score. Per-level analysis showed contextual variations, with Competence factor correlating with aim score in four of six levels, while Physical factor two out of six levels showed correlation. Heatmap analysis demonstrated that players with the higher Analytical factor chose alternative pathways around obstacles, whereas lower-scoring players favored direct routes. These findings bridge player preference research with practical game design applications, providing designers with empirical methods to predict player behavior and tailor game mechanics to target audiences, ultimately narrowing the gap between academic research and industry practice.