Classification of Different Cognitive and Affective States in Computer Game Players Using Physiology, Performance and Intrinsic Factors

Abstract

Intelligent systems infer human psychological states using three types of data: physiology, performance, and intrinsic factors. To date, few studies have compared the performance of the data types in classification of psychological states. This study compares the accuracy of three data types in classification of four psychological states and two game difficulty-related parameters. Thirty subjects played nine scenarios (different difficulty levels) of a computer game, during which seven physiological measurements and two performance variables were recorded. Then, a short questionnaire was filled out to assess the perceived difficulty, enjoyment, valence and arousal, and the way the participant would like to change two game parameters. Furthermore, participants’ intrinsic factors were assessed using four questionnaires. All combinations of the three datasets were used to classify six aspects of the short questionnaire into either two or three classes using three types of classifiers. The highest accuracies for two-class and three-class classification were 98.4% and 81.5%, respectively.