Experience Assessment and Design in the Analysis of Gameplay

Cowley, Benjamin Ultan; Kosunen, Ilkka; Lankoski, Petri; Kivikangas, J. Matias; Järvelä, Sanna; Ekman, Inger; Kemppainen, Jaakko; Ravaja, Niklas

doi:10.1177/1046878113513936

Cited by 22 publications

(12 citation statements)

References 52 publications

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“…A significant effect of sound was observed in questionnaire results related to tension and flow and these results correlate with EMG/EDA activity. The EDA, EMG, and ECG data were used to classify two different gaming events with 80% accuracy shows that the psychophysiological signal has the capability to differentiate between different user experience [100].…”

Section: Game Systemsmentioning

confidence: 99%

A Survey on Psycho-Physiological Analysis & Measurement Methods in Multimodal Systems

Baig¹,

Kavakli²

2019

MTI

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Psycho-physiological analysis has gained greater attention in the last few decades in various fields including multimodal systems. Researchers use psychophysiological feedback devices such as skin conductance (SC), Electroencephalography (EEG) and Electrocardiography (ECG) to detect the affective states of the users during task performance. Psycho-physiological feedback has been successful in detection of the cognitive states of users in human-computer interaction (HCI). Recently, in game studies, psycho-physiological feedback has been used to capture the user experience and the effect of interaction on human psychology. This paper reviews several psycho-physiological, cognitive, and affective assessment studies and focuses on the use of psychophysiological signals in estimating the user's cognitive and emotional states in multimodal systems. In this paper, we review the measurement techniques and methods that have been used to record psycho-physiological signals as well as the cognitive and emotional states in a variety of conditions. The aim of this review is to conduct a detailed study to identify, describe and analyze the key psycho-physiological parameters that relate to different mental and emotional states in order to provide an insight into key approaches. Furthermore, the advantages and limitations of these approaches are also highlighted in this paper. The findings state that the classification accuracy of >90% has been achieved in classifying emotions with EEG signals. A strong correlation between self-reported data, HCI experience, and psychophysiological data has been observed in a wide range of domains including games, human-robot interaction, mobile interaction, and simulations. An increase in β and γ-band activity have been observed in high intense games and simulations.

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Section: Game Systemsmentioning

confidence: 99%

A Survey on Psycho-Physiological Analysis & Measurement Methods in Multimodal Systems

Baig¹,

Kavakli²

2019

MTI

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“…In addition, digital assessments have the potential to be designed to be adaptive and dynamic, thereby reducing practice effects [18]. For example, a digital assessment could be designed in the form of a series of game-like tasks that offers different levels and can take different skills and ability levels into consideration [19, 20, 21, 22, 23]. These advantages may enable digital assessments to be used as a tool to measure cognitive state over long periods of time in more natural environments.…”

Section: Introductionmentioning

confidence: 99%

An analysis of a digital variant of the Trail Making Test using machine learning techniques

Dahmen

Cook

Fellows

et al. 2017

THC

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BACKGROUND The goal of this work is to develop a digital version of a standard cognitive assessment, the Trail Making Test (TMT), and assess its utility. OBJECTIVE This paper introduces a novel digital version of the TMT and introduces a machine learning based approach to assess its capabilities. METHODS Using digital Trail Making Test (dTMT) data collected from (N=54) older adult participants as feature sets, we use machine learning techniques to analyze the utility of the dTMT and evaluate the insights provided by the digital features. RESULTS Predicted TMT scores correlate well with clinical digital test scores (r=0.98) and paper time to completion scores (r=0.65). Predicted TICS exhibited a small correlation with clinically-derived TICS scores (r=0.12 Part A, r=0.10 Part B). Predicted FAB scores exhibited a small correlation with clinically-derived FAB scores (r=0.13 Part A, r=0.29 for Part B). Digitally-derived features were also used to predict diagnosis (AUC of 0.65). CONCLUSION Our findings indicate that the dTMT is capable of measuring the same aspects of cognition as the paper-based TMT. Furthermore, the dTMT’s additional data may be able to help monitor other cognitive processes not captured by the paper-based TMT alone.

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“…Behavlets in that work described patterns of real-world energy-use behaviour that could be leveraged by simulation game designers, elaborated further in Cowley (2014). Finally, the third line arose from Cowley et al (2014), in which game design patterns were related to machine-learned clusters of game events. Cowley et al (2014) showed how disparate methods could be integrated, but itself lacked a means to describe the player's actions in psychological terms, showing the need for the current work.…”

Section: Introductionmentioning

confidence: 97%

“…Finally, the third line arose from Cowley et al (2014), in which game design patterns were related to machine-learned clusters of game events. Cowley et al (2014) showed how disparate methods could be integrated, but itself lacked a means to describe the player's actions in psychological terms, showing the need for the current work. The main contribution of this paper is to define comprehensively the Behavlet process for player modelling.…”

Section: Introductionmentioning

confidence: 99%

Behavlets: a method for practical player modelling using psychology-based player traits and domain specific features

Cowley

Charles

2016

User Model User-Adap Inter

Self Cite

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As player demographics broaden it has become important to understand variation in player types. Improved player models can help game designers create games that accommodate a range of playing styles, and may also facilitate the design of systems that detect the currently-expressed player type and adapt dynamically in real-time. Existing approaches can model players, but most focus on tracking and classifying behaviour based on simple functional metrics such as deaths, specific choices, player avatar attributes, and completion times. We describe a novel approach which seeks to leverage expert domain knowledge using a theoretical framework linking behaviour and game design patterns. The aim is to derive features of play from sequences of actions which are intrinsically informative about behaviour-which, because they are directly interpretable with respect to psychological theory of behaviour, we name 'Behavlets'. We present the theoretical underpinning of this approach from research areas including psychology, temperament theory, player modelling, and game composition. The Behavlet creation process is described in detail; illustrated using a clone of the well-known game Pac-Man, with data gathered from 100 participants. A workshop-based evaluation study is also presented, where nine game design expert participants were briefed on the Behavlet concepts and requisite models, Electronic supplementary material The online version of this article (

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Experience Assessment and Design in the Analysis of Gameplay

Cited by 22 publications

References 52 publications

A Survey on Psycho-Physiological Analysis & Measurement Methods in Multimodal Systems

A Survey on Psycho-Physiological Analysis & Measurement Methods in Multimodal Systems

An analysis of a digital variant of the Trail Making Test using machine learning techniques

Behavlets: a method for practical player modelling using psychology-based player traits and domain specific features

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