Benjamin Cowley

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

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, and then attempted to apply the method to games of the well-known first/third-person shooter genres, exemplified by ‘Gears of War’, (Microsoft). The participants found 139 Behavlet concepts mapping from behavioural preferences of the temperament types, to design patterns of the shooter genre games. We conclude that the Behavlet approach has significant promise, is complementary to existing methods and can improve theoretical validity of player models.

Cognitive Collaboration Found in Cardiac Physiology: Study in Classroom Environment

It is known that periods of intense social interaction result in shared patterns in collaborators’ physiological signals. However, applied quantitative research on collaboration is hindered due to scarcity of objective metrics of teamwork effectiveness. Indeed, especially in the domain of productive, ecologically-valid activity such as programming, there is a lack of evidence for the most effective, affordable and reliable measures of collaboration quality. In this study we investigate synchrony in physiological signals between collaborating computer science students performing pair-programming exercises in a class room environment. We recorded electrocardiography over the course of a 60 minute programming session, using lightweight physiological sensors. We employ correlation of heart-rate variability features to study social psychophysiological compliance of the collaborating students. We found evident physiological compliance in collaborating dyads’ heart-rate variability signals. Furthermore, dyads’ self-reported workload was associated with the physiological compliance. Our results show viability of a novel approach to field measurement using lightweight devices in an uncontrolled environment, and suggest that self-reported collaboration quality can be assessed via physiological signals.

Learning in balance: Using oscillatory EEG biomarkers of attention, motivation and vigilance to interpret game-based learning

Abstract Motivated by the link between play and learning, proposed in literature to have a neurobiological basis, we study the electroencephalogram and associated psychophysiology of “learning game” players. Forty-five players were tested for topic comprehension by a questionnaire administered before and after solo playing of the game Peacemaker (Impact Games 2007), during which electroencephalography and other physiological signals were measured. Play lasted for one hour, with a break at half time. We used the Bloom taxonomy to distinguish levels of difficulty in demonstrated learning—with the first five levels assigned to fixed questions—and “gain” scores to measure actual value of demonstrated learning. We present the analysis of the physiological signals recorded during game play and their relationship to learning scores. Main effects related to biomarkers of vigilance and motivation—including decreased delta power and relatively balanced fronto-hemispheric alpha power—predicted learning at the analysed Bloom levels. Results suggest multiple physiological dispositions that support on-task learning styles, and highlight the utility of the psychophysiological method for interpreting game-based learning evaluations.

Computer Enabled Neuroplasticity Treatment: A Clinical Trial of a Novel Design for Neurofeedback Therapy in Adult ADHD.

Background: We report a randomized controlled clinical trial of neurofeedback therapy intervention for ADHD/ADD in adults. We focus on internal mechanics of neurofeedback learning, to elucidate the primary role of cortical self-regulation in neurofeedback. We report initial results; more extensive analysis will follow. Methods: Trial has two phases: intervention and follow-up. The intervention consisted of neurofeedback treatment, including intake and outtake measurements, using a waiting-list control group. Treatment involved ~40 h-long sessions 2–5 times per week. Training involved either theta/beta or sensorimotor-rhythm regimes, adapted by adding a novel “inverse-training” condition to promote self-regulation. Follow-up (ongoing) will consist of self-report and executive function tests. Setting: Intake and outtake measurements were conducted at University of Helsinki. Treatment was administered at partner clinic Mental Capital Care, Helsinki. Randomization: We randomly allocated half the sample then adaptively allocated the remainder to minimize baseline differences in prognostic variables. Blinding: Waiting-list control design meant trial was not blinded. Participants: Fifty-four adult Finnish participants (mean age 36 years; 29 females) were recruited after screening by psychiatric review. Forty-four had ADHD diagnoses, 10 had ADD. Measurements: Symptoms were assessed by computerized attention test (T.O.V.A.) and self-report scales, at intake and outtake. Performance during neurofeedback trials was recorded. Results: Participants were recruited and completed intake measurements during summer 2012, before assignment to treatment and control, September 2012. Outtake measurements ran April-August 2013. After dropouts, 23 treatment and 21 waiting-list participants remained for analysis. Initial analysis showed that, compared to waiting-list control, neurofeedback promoted improvement of self-reported ADHD symptoms, but did not show transfer of learning to T.O.V.A. Comprehensive analysis will be reported elsewhere. Trial Registration: “Computer Enabled Neuroplasticity Treatment (CENT),” ISRCTN13915109.

Experience Assessment and Design in the Analysis of Gameplay

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.

Feasibility of an electrodermal activity ring prototype as a research tool

Electrodermal activity is an indicator of sympathetic activation and a useful tool for investigating psychological and physiological arousal. Novel wearable skin conductivity sensors offer portable low-cost solutions for long-term monitoring. In this article we compare the similarity of signals between a prototype of the wearable Moodmetric EDA Ring and a laboratory-grade skin conductance sensor in a psychophysiological experiment. The similarity of the signals was estimated by calculating the cosine distance between phasic features extracted from decomposed signals. The similarity was on average 83.3% ± 16.4%. The compound error of the decomposition process was also investigated and no systematic bias was observed towards either device. We conclude that the prototype ring is a promising device for ecologically valid field studies.

Epileptic Electroencephalography Profile Associates with Attention Problems in Children with Fragile X Syndrome: Review and Case Series.

Fragile X syndrome (FXS) is the most common cause of inherited intellectual disability and a variant of autism spectrum disorder (ASD). The FXS population is quite heterogeneous with respect to comorbidities, which implies the need for a personalized medicine approach, relying on biomarkers or endophenotypes to guide treatment. There is evidence that quantitative electroencephalography (EEG) endophenotype-guided treatments can support increased clinical benefit by considering the patients neurophysiological profile. We describe a case series of 11 children diagnosed with FXS, aged one to 14 years, mean 4.6 years. Case data are based on longitudinal clinically-observed reports by attending physicians for comorbid symptoms including awake and asleep EEG profiles. We tabulate the comorbid EEG symptoms in this case series, and relate them to the literature on EEG endophenotypes and associated treatment options. The two most common endophenotypes in the data were diffuse slow oscillations and epileptiform EEG, which have been associated with attention and epilepsy respectively. This observation agrees with reported prevalence of comorbid behavioral symptoms for FXS. In this sample of FXS children, attention problems were found in 37% (4 of 11), and epileptic seizures in 45% (5 of 11). Attention problems were found to associate with the epilepsy endophenotype. From the synthesis of this case series and literature review, we argue that the evidence-based personalized treatment approach, exemplified by neurofeedback, could benefit FXS children by focusing on observable, specific characteristics of comorbid disease symptoms.

Biosignals reflect pair-dynamics in collaborative work: EDA and ECG study of pair-programming in a classroom environment

Collaboration is a complex phenomenon, where intersubjective dynamics can greatly affect the productive outcome. Evaluation of collaboration is thus of great interest, and can potentially help achieve better outcomes and performance. However, quantitative measurement of collaboration is difficult, because much of the interaction occurs in the intersubjective space between collaborators. Manual observation and/or self-reports are subjective, laborious, and have a poor temporal resolution. The problem is compounded in natural settings where task-activity and response-compliance cannot be controlled. Physiological signals provide an objective mean to quantify intersubjective rapport (as synchrony), but require novel methods to support broad deployment outside the lab. We studied 28 student dyads during a self-directed classroom pair-programming exercise. Sympathetic and parasympathetic nervous system activation was measured during task performance using electrodermal activity and electrocardiography. Results suggest that (a) we can isolate cognitive processes (mental workload) from confounding environmental effects, and (b) electrodermal signals show role-specific but correlated affective response profiles. We demonstrate the potential for social physiological compliance to quantify pair-work in natural settings, with no experimental manipulation of participants required. Our objective approach has a high temporal resolution, is scalable, non-intrusive, and robust.

The QUARTIC Process Model for Developing Serious Games: ‘Green My Place’ Case Study

Software engineering for pedagogy and game design for entertainment produce very different requirements and generate unique kinds of practical difficulties. The design and development of serious games relies heavily on the experience of practitioners to overcome the pitfalls inherent in joining these two distinct processes into one, but experience in tackling these problems is not widespread. This creates a requirement for a process model to guide any development of integrated game-like and education-like elements, helping to manage risk in areas of hidden difficulty such as tightly integrating the mechanics of play with the formal pedagogy. This paper presents a process model for developing contextualized educational games. Parallel streams of pedagogy and game development are married to streamline the process of deriving appropriate educational games from client requirements. The process model is illustrated in action using the case of Green My Place, a serious game developed as part of the SAVE ENERGY EU project to teach energy efficient knowledge and behaviour to users of public buildings around Europe. Our evaluation highlights the positive outcome of the project and the functioning of the serious game; this evidence also suggests a positive benefit from using the model.

Computational Testing for Automated Preprocessing 2: Practical Demonstration of a System for Scientific Data-Processing Workflow Management for High-Volume EEG

Existing tools for the preprocessing of EEG data provide a large choice of methods to suitably prepare and analyse a given dataset. Yet it remains a challenge for the average user to integrate methods for batch processing of the increasingly large datasets of modern research, and compare methods to choose an optimal approach across the many possible parameter configurations. Additionally, many tools still require a high degree of manual decision making for, e.g., the classification of artifacts in channels, epochs or segments. This introduces extra subjectivity, is slow, and is not reproducible. Batching and well-designed automation can help to regularize EEG preprocessing, and thus reduce human effort, subjectivity, and consequent error. The Computational Testing for Automated Preprocessing (CTAP) toolbox facilitates: (i) batch processing that is easy for experts and novices alike; (ii) testing and comparison of preprocessing methods. Here we demonstrate the application of CTAP to high-resolution EEG data in three modes of use. First, a linear processing pipeline with mostly default parameters illustrates ease-of-use for naive users. Second, a branching pipeline illustrates CTAPs support for comparison of competing methods. Third, a pipeline with built-in parameter-sweeping illustrates CTAPs capability to support data-driven method parameterization. CTAP extends the existing functions and data structure from the well-known EEGLAB toolbox, based on Matlab, and produces extensive quality control outputs. CTAP is available under MIT open-source licence from https://github.com/bwrc/ctap.