Using subjective emotion, facial expression, and gaze direction to evaluate user affective experience and predict preference when playing single-player games

Abstract

The affective experience generated when users play computer games can influence their attitude and preference towards the game. Existing evaluation means mainly depend on subjective scales and physiological signals. However, some limitations should not be ignored (e.g. subjective scales are not objective, and physiological signals are complicated). In this paper, we 1) propose a novel method to assess user affective experience when playing single-player games based on pleasure-arousal-dominance (PAD) emotions, facial expressions, and gaze directions, and 2) build an artificial intelligence model to identify user preference. Fifty-four subjects participated in a basketball experiment with three difficulty levels. Their expressions, gaze directions, and subjective PAD emotions were collected and analysed. Experimental results showed that the expression intensities of angry, sad, and neutral, yaw angle degrees of gaze direction, and PAD emotions varied significantly under different difficulties. Besides, the proposed model achieved better performance than other machine-learning algorithms on the collected dataset.

PRACTITIONER SUMMARY

This paper considers the limitations of existing methods for assessing user affective experience when playing computer games. It demonstrates a novel approach using subjective emotion and objective facial cues to identify user affective experience and user preference for the game.

Keywords:

• User affective experience
• subjective emotion
• facial expression
• gaze direction
• preference classification

Disclosure statement

No potential competing interest was reported by the authors.

Additional information

Funding

This work was supported in part by the Key R and D Program of Hunan (2022SK2104), Leading plan for scientific and technological innovation of high-tech industries of Hunan (2022GK4010), National Key R and D Program of China (2021YFF0900600), China Scholarship Council (CSC, No. 202306130012 and 202306130013), and National Natural Science Foundation of China (61672222).