Analysing the relevance of experience partitions to the prediction of players’ self-reports of affect

A common practice in modeling affect from physiological signals consists of reducing the signals to a set of statistical features that feed predictors of self-reported emotions. This paper analyses the impact of various time-windows, used for the extraction of physiological features, to the accuracy of affective models of players in a simple 3D game. Results show that the signals recorded in the central part of a short gaming experience contain more relevant information to the prediction of positive affective states than the starting and ending parts while the relevant information to predict anxiety and frustration appear not to be localized in a specific time interval but rather dependent on particular game stimuli.peer-reviewe

Learning deep physiological models of affect

Feature extraction and feature selection are crucial phases in the process of affective modeling. Both, however, incorporate substantial limitations that hinder the development of reliable and accurate models of affect. For the purpose of modeling affect manifested through physiology, this paper builds on recent advances in machine learning with deep learning (DL) approaches. The efficiency of DL algorithms that train artificial neural network models is tested and compared against standard feature extraction and selection approaches followed in the literature. Results on a game data corpus — containing players’ physiological signals (i.e. skin conductance and blood volume pulse) and subjective self-reports of affect — reveal that DL outperforms manual ad-hoc feature extraction as it yields significantly more accurate affective models. Moreover, it appears that DL meets and even outperforms affective models that are boosted by automatic feature selection, for several of the scenarios examined. As the DL method is generic and applicable to any affective modeling task, the key findings of the paper suggest that ad-hoc feature extraction and selection — to a lesser degree — could be bypassed.The authors would like to thank Tobias Mahlmann for his work on the development and administration of the cluster used to run the experiments. Special thanks for proofreading goes to Yana Knight. Thanks also go to the Theano development team, to all participants in our experiments, and to Ubisoft, NSERC and Canada Research Chairs for funding. This work is funded, in part, by the ILearnRW (project no: 318803) and the C2Learn (project no. 318480) FP7 ICT EU projects.peer-reviewe

Change blindness: eradication of gestalt strategies

Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task

Multimodal assessment of emotional responses by physiological monitoring: novel auditory and visual elicitation strategies in traditional and virtual reality environments

This doctoral thesis explores novel strategies to quantify emotions and listening effort through monitoring of physiological signals. Emotions are a complex aspect of the human experience, playing a crucial role in our survival and adaptation to the environment. The study of emotions fosters important applications, such as Human-Computer and Human-Robot interaction or clinical assessment and treatment of mental health conditions such as depression, anxiety, stress, chronic anger, and mood disorders. Listening effort is also an important area of study, as it provides insight into the listeners’ challenges that are usually not identified by traditional audiometric measures. The research is divided into three lines of work, each with a unique emphasis on the methods of emotion elicitation and the stimuli that are most effective in producing emotional responses, with a specific focus on auditory stimuli. The research fostered the creation of three experimental protocols, as well as the use of an available online protocol for studying emotional responses including monitoring of both peripheral and central physiological signals, such as skin conductance, respiration, pupil dilation, electrocardiogram, blood volume pulse, and electroencephalography. An emotional protocol was created for the study of listening effort using a speech-in-noise test designed to be short and not induce fatigue. The results revealed that the listening effort is a complex problem that cannot be studied with a univariate approach, thus necessitating the use of multiple physiological markers to study different physiological dimensions. Specifically, the findings demonstrate a strong association between the level of auditory exertion, the amount of attention and involvement directed towards stimuli that are readily comprehensible compared to those that demand greater exertion. Continuing with the auditory domain, peripheral physiological signals were studied in order to discriminate four emotions elicited in a subject who listened to music for 21 days, using a previously designed and publicly available protocol. Surprisingly, the processed physiological signals were able to clearly separate the four emotions at the physiological level, demonstrating that music, which is not typically studied extensively in the literature, can be an effective stimulus for eliciting emotions. Following these results, a flat-screen protocol was created to compare physiological responses to purely visual, purely auditory, and combined audiovisual emotional stimuli. The results show that auditory stimuli are more effective in separating emotions at the physiological level. The subjects were found to be much more attentive during the audio-only phase. In order to overcome the limitations of emotional protocols carried out in a laboratory environment, which may elicit fewer emotions due to being an unnatural setting for the subjects under study, a final emotional elicitation protocol was created using virtual reality. Scenes similar to reality were created to elicit four distinct emotions. At the physiological level, it was noted that this environment is more effective in eliciting emotions. To our knowledge, this is the first protocol specifically designed for virtual reality that elicits diverse emotions. Furthermore, even in terms of classification, the use of virtual reality has been shown to be superior to traditional flat-screen protocols, opening the doors to virtual reality for the study of conditions related to emotional control

Investigating Simulation-Based Pattern Recognition Training For Behavior Cue Detection

The U.S. military uses pattern recognition training to observe anomalies in human behavior. An examination of the pattern recognition training literature for Warfighters reveals a gap in training to discern patterns of human behavior in live environments. Additionally, the current state of warfare is evolving and requires operations to change. As a result, pattern recognition training must accommodate new practices to improve performance. A technique used to improve memory for identifying patterns in the environment is Kim\u27s game. Kim\u27s game establishes patterns to identify inanimate objects, of which information retains in memory for later recall. The paper discusses the fundamental principles of Kim\u27s game applied to virtual Simulation-Based Training. The virtual version of Kim\u27s game contains customized scenarios for training behavior cue analysis. Virtual agents display kinesic cues that exhibit aggressive (i.e., slap hands and clench fist) and nervous behaviors including wring hands and check six. This research takes a novel approach by animating the kinesics cues in the virtual version of Kim\u27s game for pattern recognition training. Detection accuracy, response time, and false positive detection serve as the performance data for analysis. Additional survey data collected include engagement, flow, and simulator sickness. All collected data was compared to a control condition to examine its effectiveness of behavior cue detection. A series of one-way between subjects design ANOVA\u27s were conducted to examine the differences between Kim\u27s game and control on post-test performance. Although, the results from this experiment showed no significance in post-test performance, the percent change in post-test performance provide further insight into the results of the Kim\u27s game and control strategies. Specifically, participants in the control condition performed better than the Kim\u27s game group on detection accuracy and response time. However, the Kim\u27s game group outperformed the control group on false positive detection. Further, this experiment explored the differences in Engagement, Flow, and Simulator Sickness after the practice scenario between Kim\u27s game group and the control group. The results found no significant difference in Engagement, partial significance for Flow, and significant difference for Simulator Sickness between the Kim\u27s game and control group after the practice scenario. Next, a series of Spearman\u27s rank correlations were conducted to assess the relationships between Engagement, Flow, Simulator Sickness, and post-test performance, as well as examine the relationship between working memory and training performance; resulting in meaningful correlations to explain the relationships and identifying new concepts to explain unrelated variables. Finally, the role of Engagement, Flow, and Simulator Sickness as a predictor of post-test performance was examined using a series of multiple linear regressions. The results highlighted Simulator Sickness as a significant predictor of post-test performance. Overall, the results from this experiment proposes to expand the body of pattern recognition training literature by identifying strategies that enhance behavior cue detection training. Furthermore, it provides recommendations to training and education communities for improving behavior cue analysis.

Preface

DAMSS-2018 is the jubilee 10th international workshop on data analysis methods for software systems, organized in Druskininkai, Lithuania, at the end of the year. The same place and the same time every year. Ten years passed from the first workshop. History of the workshop starts from 2009 with 16 presentations. The idea of such workshop came up at the Institute of Mathematics and Informatics. Lithuanian Academy of Sciences and the Lithuanian Computer Society supported this idea. This idea got approval both in the Lithuanian research community and abroad. The number of this year presentations is 81. The number of registered participants is 113 from 13 countries. In 2010, the Institute of Mathematics and Informatics became a member of Vilnius University, the largest university of Lithuania. In 2017, the institute changes its name into the Institute of Data Science and Digital Technologies. This name reflects recent activities of the institute. The renewed institute has eight research groups: Cognitive Computing, Image and Signal Analysis, Cyber-Social Systems Engineering, Statistics and Probability, Global Optimization, Intelligent Technologies, Education Systems, Blockchain Technologies. The main goal of the workshop is to introduce the research undertaken at Lithuanian and foreign universities in the fields of data science and software engineering. Annual organization of the workshop allows the fast interchanging of new ideas among the research community. Even 11 companies supported the workshop this year. This means that the topics of the workshop are actual for business, too. Topics of the workshop cover big data, bioinformatics, data science, blockchain technologies, deep learning, digital technologies, high-performance computing, visualization methods for multidimensional data, machine learning, medical informatics, ontological engineering, optimization in data science, business rules, and software engineering. Seeking to facilitate relations between science and business, a special session and panel discussion is organized this year about topical business problems that may be solved together with the research community. This book gives an overview of all presentations of DAMSS-2018.DAMSS-2018 is the jubilee 10th international workshop on data analysis methods for software systems, organized in Druskininkai, Lithuania, at the end of the year. The same place and the same time every year. Ten years passed from the first workshop. History of the workshop starts from 2009 with 16 presentations. The idea of such workshop came up at the Institute of Mathematics and Informatics. Lithuanian Academy of Sciences and the Lithuanian Computer Society supported this idea. This idea got approval both in the Lithuanian research community and abroad. The number of this year presentations is 81. The number of registered participants is 113 from 13 countries. In 2010, the Institute of Mathematics and Informatics became a member of Vilnius University, the largest university of Lithuania. In 2017, the institute changes its name into the Institute of Data Science and Digital Technologies. This name reflects recent activities of the institute. The renewed institute has eight research groups: Cognitive Computing, Image and Signal Analysis, Cyber-Social Systems Engineering, Statistics and Probability, Global Optimization, Intelligent Technologies, Education Systems, Blockchain Technologies. The main goal of the workshop is to introduce the research undertaken at Lithuanian and foreign universities in the fields of data science and software engineering. Annual organization of the workshop allows the fast interchanging of new ideas among the research community. Even 11 companies supported the workshop this year. This means that the topics of the workshop are actual for business, too. Topics of the workshop cover big data, bioinformatics, data science, blockchain technologies, deep learning, digital technologies, high-performance computing, visualization methods for multidimensional data, machine learning, medical informatics, ontological engineering, optimization in data science, business rules, and software engineering. Seeking to facilitate relations between science and business, a special session and panel discussion is organized this year about topical business problems that may be solved together with the research community. This book gives an overview of all presentations of DAMSS-2018