Classification of Multiple Psychological Dimensions in Computer Game Players Using Physiology, Performance, and Personality Characteristics
Classification of Multiple Psychological Dimensions in Computer Game Players Using Physiology, Performance, and Personality Characteristics
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DOI：
10.3389/fnins.2019.01278
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发表时间：
2019-11-26
影响因子：
4.3
通讯作者：
Novak, Domen
中科院分区：
文献类型：
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作者：
Darzi, Ali;Wondra, Trent;Novak, Domen
人类的心理(认知和情感)维度可以使用几种方法进行评估，例如生理或表现测量。然而，到目前为止，很少有研究比较不同数据模式对不同心理维度进行准确分类的能力。因此，本研究比较了电脑游戏难度的四个心理维度和两个主观偏好的分类准确率，使用了三种数据形式：生理、表现和人格特征。30名参与者在九种难度配置下玩电脑游戏，这些配置是通过两个难度参数实现的。在每个配置中，记录了七个生理测量和两个性能变量。填写了一份简短的问卷，以评估感知的难度、幸福感、效价、唤醒，以及参与者希望修改这两个难度参数的方式。此外，还使用四份问卷对参与者的人格特征进行了评估。使用四种分类器类型：线性判别分析、支持向量机、集成决策树和多元线性回归，使用三种数据形式(生理、表现和个性)的所有组合将简短问卷的六个维度分类为两个、三个或多个类别。不同心理维度的分类准确率差异较大，两类和三类分类的最高准确率分别为97.6%和84.1%。归一化生理测量是最具信息量的数据形式，尽管当前的游戏难度、个性和表现也有助于分类的准确性；在正文中提出并讨论了最佳选择的特征。支持向量机和多元线性回归是最准确的分类器，回归对归一化生理数据更有效。未来，我们将通过检测用户的心理状态和实时适应游戏难度来进一步检验不同分类方法对用户体验的影响。这将使我们能够以离线(分类准确性)和实时(对用户体验的影响)的方式获得影响感知系统的性能的完整画面。
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.