Abstract
Human psychological (cognitive and affective) dimensions can be assessed using several methods, such as physiological or performance measurements. To date, however, few studies have compared different data modalities with regard to their ability to enable accurate classification of different psychological dimensions. This study thus compares classification accuracies for four psychological dimensions and two subjective preferences about computer game difficulty using three data modalities: physiology, performance, and personality characteristics. Thirty participants played a computer game at nine difficulty configurations that were implemented via two difficulty parameters. In each configuration, seven physiological measurements and two performance variables were recorded. A short questionnaire was filled out to assess the perceived difficulty, enjoyment, valence, arousal, and the way the participant would like to modify the two difficulty parameters. Furthermore, participants’ personality characteristics were assessed using four questionnaires. All combinations of the three data modalities (physiology, performance, and personality) were used to classify six dimensions of the short questionnaire into either two, three or many classes using four classifier types: linear discriminant analysis, support vector machine (SVM), ensemble decision tree, and multiple linear regression. The classification accuracy varied widely between the different psychological dimensions; the highest accuracies for two-class and three-class classification were 97.6 and 84.1%, respectively. Normalized physiological measurements were the most informative data modality, though current game difficulty, personality and performance also contributed to classification accuracy; the best selected features are presented and discussed in the text. The SVM and multiple linear regression were the most accurate classifiers, with regression being more effective for normalized physiological data. In the future, we will further examine the effect of different classification approaches on user experience by detecting the user’s psychological state and adapting game difficulty in real-time. This will allow us to obtain a complete picture of the performance of affect-aware systems in both an offline (classification accuracy) and real-time (effect on user experience) fashion.
Highlights
Affective games are an emerging type of videogame in which the player’s psychological state is automatically detected and used as a basis for intelligent game adaptation (Liu et al, 2009; Chanel et al, 2011)
The goal of our study is to compare the effectiveness of different machine learning (ML) methods in recognizing different psychological dimensions of affective game players based on different data modalities
The classifiers used different combinations of three input data modalities, and classification accuracies were compared between data modalities
Summary
Affective games are an emerging type of videogame in which the player’s psychological (cognitive and affective) state is automatically detected and used as a basis for intelligent game adaptation (Liu et al, 2009; Chanel et al, 2011). Affective games have the potential to achieve more effective adaptation than “classic” games (Ng et al, 2012) and result in higher user engagement, immersion and enjoyment (Nagle et al, 2015; McCrea et al, 2016; Denisova and Cairns, 2018). Such improvements would be useful for entertainment, and for serious game applications such as education (Ip et al, 2016), motor rehabilitation (Koenig et al, 2011; Rodriguez-Guerrero et al, 2017), and autism intervention (Zhang et al, 2017b). There is only limited knowledge about how to choose the psychological dimension, data modalities, and ML approach in order to optimize psychological state estimation and game adaptation
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