Experience Assessment and Design in the Analysis of Gameplay

Please download to get full document.

View again

of 29
All materials on our website are shared by users. If you have any questions about copyright issues, please report us to resolve them. We are always happy to assist you.
Document Description
We report research on player modeling using psychophysiology and machine learning, conducted through interdisciplinary collaboration between researchers of computer science, psychology, and game design at Aalto University, Helsinki. First, we propose
Document Share
Document Transcript
  Simulation & Gaming2014, Vol. 45(1) 41  –69© 2013 SAGE PublicationsReprints and permissions:sagepub.com/journalsPermissions.nav DOI: 10.1177/1046878113513936sag.sagepub.com Experience Assessment and Design in the Analysis of Gameplay Benjamin Cowley 1,2 , Ilkka Kosunen 1,3 , Petri Lankoski 2,4 ,  J. Matias Kivikangas 2 , Simo Järvelä 2 , Inger Ekman 2,5 ,  Jaakko Kemppainen 2 , and Niklas Ravaja 1,2,3 Abstract We report research on player modeling using psychophysiology and machine learning, conducted through interdisciplinary collaboration between researchers of computer science, psychology, and game design at Aalto University, Helsinki. First, we propose the Play Patterns And eXperience (PPAX) framework to connect three levels of game experience that previously had remained largely unconnected: game design patterns, the interplay of game context with player personality or tendencies, and state-of-the-art measures of experience (both subjective and non-subjective). Second, we describe our methodology for using machine learning to categorize game events to reveal corresponding patterns, culminating in an example experiment. We discuss the relation between automatically detected event clusters and game design patterns, and provide indications on how to incorporate personality profiles of players in the analysis. This novel interdisciplinary collaboration combines basic psychophysiology research with game design patterns and machine learning, and generates new knowledge about the interplay between game experience and design. Keywords behavior patterns, computer science, event clusters, experiment, game design, game experience, gameplay patterns, interdisciplinarity, machine learning, personality profiles, PPAX framework, psychophysiology 1 University of Helsinki, Finland 2 Aalto University, Helsinki, Finland 3 Helsinki Institute for Information Technology, Finland 4 Södertörn University, Sweden 5 University of Tampere, FinlandThis article is a part of the symposium: Development of a Finnish Community of Game Scholars Corresponding Author: Benjamin Cowley, Cognitive Science Unit, Institute of Behavioral Sciences, University of Helsinki, Siltavuorenpenger 1B, P.O. Box 9, Helsinki 00014, Finland. Email: ben.cowley@helsinki.fi SAG   XX   X   10.1177/1046878113513936Simulation & Gaming Cowley etal. research-article   2013  42  Simulation & Gaming 45(1) Psychophysiological data offer valuable information for assessing, quantifying, and testing the player experience 1  in support of game design (Ambinder, 2011; Yannakakis & Hallam, 2008). However, the information gained from physiological data is deter-mined by the analytical framework being used to describe, annotate, and group the activities of play. Currently, the practical application of psychophysiological research remains limited because the analytical framework is not explicitly linked to the the-matic areas of interest within game design.From a design perspective, all events in a game exist within a larger framework. In shaping the game experience, the designer works from the assumption that the out-come of one event depends on a history of prior events and encounters. Thus, part of shaping the experience is to design  patterns of events  with a desired cumulative expe-riential effect. Likewise, the experience of gaming is not only a series of individual emotional reactions, but also of patterns of cognitions and emotions (Lazzaro, 2008), all of which are reflected in the player’s real-time physiological reactions. Psychophysiological research usually deals with much simpler stimuli than games; with complex stimuli, it is very difficult to differentiate between what is a new effect, what is a remnant of a previous effect, and what is merely a reflection of the person’s characteristic physiology or current metabolic demand. In order to facilitate meaning-ful analysis, the temporal behavior (e.g., response onset latency) and resolution of different physiological responses must be considered in relation to the effect being examined. Likewise, the examined responses are almost without exception relative, with effects assessed as a change in the physiological status during a given time frame.For the purposes of design, this frame of analysis is not adequate. As we are forced to consider well-differentiated, predetermined temporal instances, our analysis is con-fined to isolated events and presentation effects that carry only a superficial link to game design. Even when this type of analysis can mitigate the interdependent nature of events (a difficult task in itself, see, for example, Järvelä, Kivikangas, Ekman, & Ravaja, 2013) and produce accurate feature-specific data, the focus on narrow features limits the scope of design phenomena that can be addressed. For example, no mean-ingful way exists to utilize physiology for assessing the merits of one design compared with another, unless the design differences involve similar sets of low-level parame-ters that may verge on the trivial. Furthermore, whereas game designers typically con-sider different player types, playing styles, and play preferences (Ambinder, 2011),  player experience research has virtually no tools to include these in the analysis frame-work for physiological data. This leads to a one-size-fits-all mentality in the evalua-tion of player responses. To conclude, although current psychological game research addresses relevant experiential phenomena for understanding gaming, it provides poor tools for actual design practices and in-development evaluation. Likewise, the benefits of combining experience data with player personality and game preference have not  previously been investigated, either for design or for basic research.From this synthesis of the state of the art, we obtain the goal for our study: to con-struct a conceptual-methodological framework for measurement and analysis of play experiences with respect to their designs . This article describes a novel method for integrating player preferences, experiential data, and game design patterns 2  into one single construct: the Play Patterns And eXperience (PPAX) framework. The PPAX  Cowley et al. 43 approach forges a link between self-report and physiological player data and game design. At its core, it uses machine learning to model relationships between game design patterns and high-bandwidth real-time data on the player experience. The approach is an interdisciplinary effort between computer science, psychology, and games research. It ultimately rests on data-driven models that take quantitative and qualitative input and are described below. However, the main value lies in the concep-tual framework, which can guide thinking in the area.We illustrate the applicability of the framework with an example experiment, exploring the causal link between gameplay patterns and physiological reactions, which is one of the core relationships within the framework. Although this article focuses on specific technology, the approach is technology ambivalent; we do not wish to measure every possible facet of experience, but use only what tools are neces-sary and sufficient to facilitate design analysis.The article proceeds as follows: The next section presents the background and state of the art. “PPAX—Play Patterns And eXperience Framework” section presents the PPAX framework. In “Example on Application of the Framework” section, we present an evaluation study demonstrating the PPAX framework in action. We detail, under separate headers, the materials and methods for gathering and categorizing player experience data (“Experimental Procedure” section); the novel data-driven analysis approach (“Method of Analysis” section); the empirical results of clustering game event data using the PPAX framework (“Results” section), and the discussion thereof (“Discussion” section). In “Conclusion” section, we lay out our conclusions and views on future work. Background The construction of an interlocking framework of existing theories of games and expe-rience has already been suggested in, for example, the User-System-Experience (USE) model (Cowley, Charles, Black, & Hickey, 2008). This work was a review, where the concept of gameplay experience patterns was expressed in terms of the theories of  player types, factor-analytic machine learning and flow (Csikszentmihalyi, 1975); synthesized within an information systems paradigm drawing on cognitive science, much as in Bateman and Nacke (2010). USE name-checks theoretical constructs for each domain, such as typologies to describe players (Bateman & Boon, 2005), LeBlanc’s Mechanics/Dynamics/Aesthetics to describe the game (Hunicke, LeBlanc, & Zubek, 2004) and the Immersion-Engagement cycle to describe the experience (Douglas & Hargadon, 2000). PPAX retains the same principal concept of drawing on existing research to broadly describe gameplay, but makes a number of advances.  Namely, the specific theories and instruments of player experience are updated to match the state of the art; PPAX introduces a connection with empiria; and PPAX ultimately aims to create predictions and not just descriptions of play, which is much more useful in practical applications.The PPAX framework operates along the player-game relation, incorporating into one analytical framework various constructs that have previously only been considered separately. PPAX is strongly interdisciplinary in character, and an exhaustive review of  44  Simulation & Gaming 45(1) all affiliated disciplines is beyond the scope of this article. The next sections will dis-cuss the relevant background for the four major areas underlying this contribution, and contextualize the key methods that PPAX draws upon to create its empirical scaffold:1. the research on psychophysiology and emotions,2. the use of game design patterns as a formal description of game structure,3. approaches for player profiling, and4. the combination of machine learning and psychophysiology, which is explored in the example, “Example on Application of the Framework” section. Psychophysiology and Emotions Psychophysiological methods study psychological phenomena, emotions in the pres-ent case, by measuring human autonomous physiological activity, which is real time and relatively objective. Psychophysiological studies often use a model of emotions  positing that emotional experiences can be organized along two dimensions: valence and arousal (Lang, 1995). The valence dimension corresponds to the extent to which an emotional experience is unpleasant or pleasant and the arousal dimension indicates the level of (bodily) activation associated with the emotional experience. However, when studying responses to complex, interactive, and multimodal activity such as digital game playing, it is important to notice that positive and negative emotions are not mutually exclusive. Indeed theory and experimental evidence both suggest that they are, at least to some extent, separable and independent (see, for example, Cacioppo, Gardner, & Berntson, 1999; Larsen & McGraw, 2011; Park, 2008). Furthermore, the valence and arousal dimensions have been suggested to be the sub- jective components of the two primary brain motivational systems: the behavioral activation system (BAS) that regulates approach/appetitive behavior and the behav-ioral inhibition system (BIS) that regulates withdrawal/aversive behavior, respectively (Watson, Wiese, Vaidya, & Tellegen, 1999).The current iteration of the PPAX framework focuses on those signals that combine  practicality of use with relevance to the valence-arousal model. The signals in this core set are relatively simple to record, functionally cohesive and have strong empirical and theoretical foundations in the game research literature. These are electrodermal activ-ity (EDA), associated with arousal; facial electromyography (EMG), which indexes conscious and non-conscious emotional expression; and electrocardiography (ECG), which measures the activity of the heart and can help to index many processes such as mental workload.Processing pleasant or unpleasant emotions, or stress, is associated with increasing activity over facial muscle areas, which can be measured using EMG (Bradley, 2000; Ekman, Davidson, & Friesen, 1990; Lang, 1995; Tassinary & Cacioppo, 2000). Several encouraging reports on using EMG specifically to index responses to digital game events have already been published (see Kivikangas et al., 2011; Kivikangas & Ravaja, 2013).  Cowley et al. 45 Arousal is most often measured with EDA (or skin conductance level; sometimes inaccurately called galvanic skin response; Bradley, 2000; Lang, 1995). EDA is a reli-able tool when studying gaming experiences (e.g., Mandryk & Atkins, 2007; Schneider, Lang, Shin, & Bradley, 2004; Staude-Müller, Bliesener, & Luthman, 2008), as it is less susceptible to misinterpretations than either ECG or facial EMG.In addition, features of cardiac activity such as heart rate (HR) and certain fre-quency bands of HR variability are among the most widely used physiological signals, despite the fact that interpretation in the game context can be challenging, given that the heart and circulatory system are regulated by many different bodily processes. This difficulty is demonstrated by the fact that, in different studies, cardiac activity has  been interpreted as an index of both valence and arousal, but also of attention, cogni-tive effort (see, for example, Cowley, Ravaja, & Heikura, 2013), stress, and the orien-tation reflex (see, for example, Ravaja, 2004). Still, cardiac indices have been used in many game studies especially for indexing arousal, and often with claims of success (e.g., Ravaja et al., 2006). Formal Game Features and Game Design The PPAX framework seeks to integrate experiential data with a structural under-standing of game events. A major challenge when discussing the evaluation of design is how to classify individual game features in meaningful ways that allow us to link  particular elements or game events to the playing experience, but also adequately con-textualizes elements within the greater plan of the game. The development of formal systems for investigating games has largely been design-driven, addressing practitio-ners’ need for talking about design decisions in terms of gameplay outcomes. However, the conceptual roots are sociological, lying for example in the 1960s work of Caillois (2001), who distinguished four types of games as having different structural and expe-riential qualities. We can extrapolate from Caillois’s work that these different types of games would arise from different types of play preferences; in other words, different forms of play arose from different ways of having fun. Even a complex game is usu-ally built from simpler elements that are based on either competition, chance, role- playing, or other mechanics. The typical way of structuring discussion of these archetypal forms of play is the genre model. Although it is true that genres do not facilitate well-defined taxonomies (see, for example, the review in Arsenault, 2009), Zammitto (2010) has shown it is possible to leverage them for modeling purposes.A number of authors have proposed formal solutions to the problem of describing games (Church, 1999; Costikyan, 2002; Zagal, Mateas, Fernández-Vara, Hochhalter, & Lichti, 2005). Björk and Holopainen (2005) proposed expressing formalized game- play features as  game design patterns , also related to the concept of game grammars (Cook, 2006; Koster, 2005). Other authors have proposed the use of game metrics  to explore the play space (Cousins, 2005; Drachen, Canossa, & Yannakakis, 2009; Yannakakis & Maragoudakis, 2005). The main idea behind all of these is to generalize and describe recurring design problems and their solutions. These approaches allow us
Similar documents
Search Related
We Need Your Support
Thank you for visiting our website and your interest in our free products and services. We are nonprofit website to share and download documents. To the running of this website, we need your help to support us.

Thanks to everyone for your continued support.

No, Thanks