Classification of Multiple Psychological Dimensions in Computer Game Players Using Physiology, Performance, and Personality Characteristics

Breaking presence in Immersive Virtual Reality toward behavioral and emotional engagement

BACKGROUND AND OBJECTIVE

Many recent studies in virtual reality (VR) have managed the sense of Presence to assess the suitability of their designs, mainly when focused on learning goals that require high user engagement, such as in serious games for psychomotor training. However, the place and plausibility illusions needed to promote Presence are achieved by combining different VR-based design cues, and their individual contribution to preserving the Presence's engagement/involvement component is still unclear. This article explored the single effect of breaking the sense of Presence per VR factor, i.e., removing VR cues related to Social Presence (human interactions), Self-Presence (embodiment), and Physical Presence (Scenario realism).

METHODS

Thirty-three participants were asked to play an immersive VR simulation of an arcade game three times by experiencing a stepped Break of Illusion in one of the VR factors, i.e., while two factors were kept high, the remaining one was reduced to a low and null (hypothetical) level. The game difficulty was fixed after assessing each person's skills.

RESULTS

Results showed that psychophysiology indicators (heart rate and skin conductance) were not affected by the level of illusion, whereas exercise intensity was significantly higher with low body and social presence-based conditions. Moreover, skin conductance was lower in the Social-presence group, which suggests that perspiration is only affected by breaks in realism (scenario and body representations). Based on the obtained evidence, we proposed some guidelines for adapting the design of immersive virtual environments through Breaks in Presence, mainly by changing the realism of the scenario and body representation depending on the skin conductance or the interaction with virtual humans depending on exercise intensity.

Automatic Decision-Making Style Recognition Method Using Kinect Technology

In recent years, somatosensory interaction technology, represented by Microsoft's Kinect hardware platform, has been widely used in various fields, such as entertainment, education, and medicine. Kinect technology can easily capture and record behavioral data, which provides new opportunities for behavioral and psychological correlation analysis research. In this paper, an automatic decision-style recognition method is proposed. Experiments involving 240 subjects were conducted to obtain face data and individual decision-making style score. The face data was obtained using the Kinect camera, and the decision-style score were obtained via a questionnaire. To realize automatic recognition of an individual decision-making style, machine learning was employed to establish the mapping relationship between the face data and a scaled evaluation of the decision-making style score. This study adopts a variety of classical machine learning algorithms, including Linear regression, Support vector machine regression, Ridge regression, and Bayesian ridge regression. The experimental results show that the linear regression model returns the best results. The correlation coefficient between the linear regression model evaluation results and the scale evaluation results was 0.6, which represents a medium and higher correlation. The results verify the feasibility of automatic decision-making style recognition method based on facial analysis.

Automatic Estimation of Interpersonal Engagement During Naturalistic Conversation Using Dyadic Physiological Measurements

Physiological responses of two interacting individuals contain a wealth of information about the dyad: for example, the degree of engagement or trust. However, nearly all studies on dyadic physiological responses have targeted group-level analysis: e.g., correlating physiology and engagement in a large sample. Conversely, this paper presents a study where physiological measurements are combined with machine learning algorithms to dynamically estimate the engagement of individual dyads. Sixteen dyads completed 15-min naturalistic conversations and self-reported their engagement on a visual analog scale every 60 s. Four physiological signals (electrocardiography, skin conductance, respiration, skin temperature) were recorded, and both individual physiological features (e.g., each participant's heart rate) and synchrony features (indicating degree of physiological similarity between two participants) were extracted. Multiple regression algorithms were used to estimate self-reported engagement based on physiological features using either leave-interval-out crossvalidation (training on 14 60-s intervals from a dyad and testing on the 15th interval from the same dyad) or leave-dyad-out crossvalidation (training on 15 dyads and testing on the 16th). In leave-interval-out crossvalidation, the regression algorithms achieved accuracy similar to a 'baseline' estimator that simply took the median engagement of the other 14 intervals. In leave-dyad-out crossvalidation, machine learning achieved a slightly higher accuracy than the baseline estimator and higher accuracy than an independent human observer. Secondary analyses showed that removing synchrony features and personality characteristics from the input dataset negatively impacted estimation accuracy and that engagement estimation error was correlated with personality traits. Results demonstrate the feasibility of dynamically estimating interpersonal engagement during naturalistic conversation using physiological measurements, which has potential applications in both conversation monitoring and conversation enhancement. However, as many of our estimation errors are difficult to contextualize, further work is needed to determine acceptable estimation accuracies.

Automated affect classification and task difficulty adaptation in a competitive scenario based on physiological linkage: An exploratory study

In competitive and cooperative scenarios, task difficulty should be dynamically adapted to suit people with different abilities. State-of-the-art difficulty adaptation methods for such scenarios are based on task performance, which conveys little information about user-specific factors such as workload. Thus, we present an exploratory study of automated affect recognition and task difficulty adaptation in a competitive scenario based on physiological linkage (covariation of participants' physiological responses). Classification algorithms were developed in an open-loop study where 16 pairs played a competitive game while 5 physiological responses were measured: respiration, skin conductance, electrocardiogram, and 2 facial electromyograms. Physiological and performance data were used to classify four self-reported variables (enjoyment, valence, arousal, perceived difficulty) into two or three classes. The highest classification accuracies were obtained for perceived difficulty: 84.3% for two-class and 60.5% for three-class classification. As a proof of concept, the developed classifiers were used in a small closed-loop study to dynamically adapt game difficulty. While this closed-loop study found no clear advantages of physiology-based adaptation, it demonstrated the technical feasibility of such real-time adaptation. In the long term, physiology-based task adaptation could enhance competition and cooperation in many multi-user settings (e.g., education, manufacturing, exercise).

User Experience With Dynamic Difficulty Adjustment Methods for an Affective Exergame: Comparative Laboratory-Based Study

BACKGROUND

In affective exergames, game difficulty is dynamically adjusted to match the user's physical and psychological state. Such an adjustment is commonly made based on a combination of performance measures (eg, in-game scores) and physiological measurements, which provide insight into the player's psychological state. However, although many prototypes of affective games have been presented and many studies have shown that physiological measurements allow more accurate classification of the player's psychological state than performance measures, few studies have examined whether dynamic difficulty adjustment (DDA) based on physiological measurements (which requires additional sensors) results in a better user experience than performance-based DDA or manual difficulty adjustment.

OBJECTIVE

This study aims to compare five DDA methods in an affective exergame: manual (player-controlled), random, performance-based, personality-performance-based, and physiology-personality-performance-based (all-data).

METHODS

A total of 50 participants (N=50) were divided into five groups, corresponding to the five DDA methods. They played an exergame version of Pong for 18 minutes, starting at a medium difficulty; every 2 minutes, two game difficulty parameters (ball speed and paddle size) were adjusted using the participant's assigned DDA method. The DDA rules for the performance-based, personality-performance-based, and all-data groups were developed based on data from a previous open-loop study. Seven physiological responses were recorded throughout the sessions, and participants self-reported their preferred changes to difficulty every 2 minutes. After playing the game, participants reported their in-game experience using two questionnaires: the Intrinsic Motivation Inventory and the Flow Experience Measure.

RESULTS

Although the all-data method resulted in the most accurate changes to ball speed and paddle size (defined as the percentage match between DDA choice and participants' preference), no significant differences between DDA methods were found on the Intrinsic Motivation Inventory and Flow Experience Measure. When the data from all four automated DDA methods were pooled together, the accuracy of changes in ball speed was significantly correlated with players' enjoyment (r=0.38) and pressure (r=0.43).

CONCLUSIONS

Although our study is limited by the use of a between-subjects design and may not generalize to other exergame designs, the results do not currently support the inclusion of physiological measurements in affective exergames, as they did not result in an improved user experience. As the accuracy of difficulty changes is correlated with user experience, the results support the development of more effective DDA methods. However, they show that the inclusion of physiological measurements does not guarantee a better user experience even if it yields promising results in offline cross-validation.