GUX-Analyzer: A Deep Multi-modal Analyzer Via Motivational Flow For Game User Experience

TL;DR

GUX-Analyzer introduces a multi-modal deep learning approach that extends the Flow model to include motivation, enabling quantitative analysis of game user experience through physiological and game process data.

Contribution

It presents the MovFlow model and GUXAS system, pioneering a computational method to analyze GUX by integrating physiological, affective, and game data.

Findings

MovFlow effectively distinguishes user experience states.

The system predicts in-game experience using only game process data.

Multi-modal analysis enhances understanding of player motivation and affect.

Abstract

Quantitative analysis of Game User eXperience (GUX) is important to the game industry. Different from the typical questionnaire analysis, this paper focuses on the computational analysis of GUX. We aim to analyze the relationship between game and players using the multi-modal data including physiological data and game process data. We theoretically extend the Flow model from the classic skill-and-challenge plane by expanding new dimension on motivation, which is the result of the multi-modal data analysis on affect, and physiological data. We call this 3D Flow as Motivational Flow, MovFlow. Meanwhile, we implement a quantitative GUX Analysis System (GUXAS), which can predict the player's in-game experience state by only using game process data. It analyzes the correlation among not only in-game state, but the player's psychological-and-physiological reaction in the entire interactive game-play process. The experiments demonstrated our MovFlow model efficiently distinguished the users' in-game experience states from the perspective of GUX.