Classification of Multiple Psychological Dimensions in Computer Game Players Using Physiology, Performance, and Personality Characteristics
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 psychol...
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doaj-b2acf2984545432cb72a8af7da02aea72020-11-25T02:33:14ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2019-11-011310.3389/fnins.2019.01278464261Classification of Multiple Psychological Dimensions in Computer Game Players Using Physiology, Performance, and Personality CharacteristicsAli Darzi0Trent Wondra1Sean McCrea2Domen Novak3Department of Electrical and Computer Engineering, University of Wyoming, Laramie, WY, United StatesDepartment of Psychology, University of Wyoming, Laramie, WY, United StatesDepartment of Psychology, University of Wyoming, Laramie, WY, United StatesDepartment of Electrical and Computer Engineering, University of Wyoming, Laramie, WY, United StatesHuman 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.https://www.frontiersin.org/article/10.3389/fnins.2019.01278/fullaffective computingdynamic difficulty adaptationphysiological measurementstask performancepersonality characteristicspsychophysiology |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ali Darzi Trent Wondra Sean McCrea Domen Novak |
spellingShingle |
Ali Darzi Trent Wondra Sean McCrea Domen Novak Classification of Multiple Psychological Dimensions in Computer Game Players Using Physiology, Performance, and Personality Characteristics Frontiers in Neuroscience affective computing dynamic difficulty adaptation physiological measurements task performance personality characteristics psychophysiology |
author_facet |
Ali Darzi Trent Wondra Sean McCrea Domen Novak |
author_sort |
Ali Darzi |
title |
Classification of Multiple Psychological Dimensions in Computer Game Players Using Physiology, Performance, and Personality Characteristics |
title_short |
Classification of Multiple Psychological Dimensions in Computer Game Players Using Physiology, Performance, and Personality Characteristics |
title_full |
Classification of Multiple Psychological Dimensions in Computer Game Players Using Physiology, Performance, and Personality Characteristics |
title_fullStr |
Classification of Multiple Psychological Dimensions in Computer Game Players Using Physiology, Performance, and Personality Characteristics |
title_full_unstemmed |
Classification of Multiple Psychological Dimensions in Computer Game Players Using Physiology, Performance, and Personality Characteristics |
title_sort |
classification of multiple psychological dimensions in computer game players using physiology, performance, and personality characteristics |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Neuroscience |
issn |
1662-453X |
publishDate |
2019-11-01 |
description |
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. |
topic |
affective computing dynamic difficulty adaptation physiological measurements task performance personality characteristics psychophysiology |
url |
https://www.frontiersin.org/article/10.3389/fnins.2019.01278/full |
work_keys_str_mv |
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