Schema theory provides a foundation for the analysis of game play patterns created by players during their interaction with a game. Schema models derived from the analysis of play provide a rich explanatory framework for the cognitive processes underlying game play, as well as detailed hypotheses for the hierarchical structure of pleasures and rewards motivating players. Game engagement is accounted for as a process of schema selection or development, while immersion is explained in terms of levels of attentional demand in schema execution. However, schemas may not only be used to describe play, but might be used actively as cognitive models within a game engine. Predesigned schema models are knowledge representations constituting anticipated or desired learned cognitive outcomes of play. Automated analysis of player schemas and comparison with predesigned target schemas can provide a foundation for a game engine adapting or tuning game mechanics to achieve specific effects of engagement, immersion, and cognitive skill acquisition by players. Hence, schema models may enhance the play experience as well as provide a foundation for achieving explicitly represented pedagogical or therapeutic functions of games.
This study investigates individuals' cognitive load processing abilities while engaged on a decision-making task in serious games, to explore how a substantial cognitive load dominates over the physiological arousal effect on pupil diameter. A serious game was presented to the participants, which displayed the on-line biofeedback based on physiological measurements of arousal. In such dynamic decision-making environment, the pupil diameter was analyzed in relation to the heart rate, to evaluate if the former could be a useful measure of cognitive abilities of individuals. As pupil might reflect both cognitive activity and physiological arousal, the pupillary response will show an arousal effect only when the cognitive demands of the situation are minimal. Evidence shows that in a situation where a substantial level of cognitive activity is required, only that activity will be observable on the pupil diameter, dominating over the physiological arousal effect indicated by the pupillary response. It is suggested that it might be possible to design serious games tailored to the cognitive abilities of an individual player, using the proposed physiological measurements to observe the moment when such dominance occurs.
Secure and reliable sensing plays the key role for cognitive tracking i.e., activity identification and cognitive monitoring of every individual. Over the last years there has been an increasing interest from both academia and industry in cognitive authentication also known as biometric recognition. These are an effect of individuals’ biological and physiological traits. Among various traditional biometric and physiological features, we include cognitive/brainwaves via electroencephalogram (EEG) which function as a unique performance indicator due to its reliable, flexible, and unique trait resulting in why it is hard for an un-authorized entity(ies) to breach the boundaries by stealing or mimicking them. Conventional security and privacy techniques in the medical domain are not the potential candidates to simultaneously provide both security and energy efficiency. Therefore, state-of-the art biometrics methods (i.e., machine learning, deep learning, etc.) their applications with novel solutions are investigated and recommended. The experimental setup considers EEG data analysis and interpretation of BCI. The key purpose of this setup is to reduce the number of electrodes and hence the computational power of the Random Forest (RF) classifier while testing EEG data. The performance of the random forest classifier was based on EEG datasets for 20 subjects. We found that the total number of occurred events revealed 96.1% precision in terms of chosen events.
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