DEEP: A Model of Gaming Preferences Informed by the Hierarchical Nature of Goal-Oriented Cognition

The DEEP model establishes a comprehensive framework for understanding gaming preferences by mapping player agency onto the hierarchical nature of goal-oriented cognition. By categorizing engagement into four distinct dimensions—Discovering, Experimenting, Expanding, and Performing—the model posits that gaming enjoyment is fundamentally rooted in how players navigate the tension between exploratory and exploitative actions. These dimensions are structured as a nested hierarchy, where high-level goals, such as discovering new content or expanding a virtual footprint, rely upon the successful execution of foundational, low-level goals like experimenting with mechanics or performing specific tasks.

Empirical validation of this framework involved a 20-item psychometric scale tested on 622 participants and an AI-assisted analysis of 16,598 video games. The data reveals that 90% of analyzed titles adhere to this nested hierarchical structure, confirming that game design often mirrors the cognitive architecture of human goal-setting. Furthermore, the model demonstrates significant predictive power regarding player behavior; for instance, individuals who prioritize discovering and experimenting tend to engage with a wider variety of titles, whereas those who favor performing often exhibit deeper mastery of fewer, specific games. These preferences also correlate with established psychological traits, such as openness and conscientiousness.

By shifting the focus from general motivational needs to specific, measurable game-based goals, the DEEP model offers a robust alternative to existing theories of player engagement. It provides a standardized methodology for both assessing individual player profiles and analyzing the structural design of games. Ultimately, the framework suggests that player satisfaction is determined by the alignment between a game’s mechanical architecture and the player’s preferred mode of agency, offering a precise tool for developers and researchers to quantify the complex relationship between cognitive style and interactive entertainment.