Measuring Cognition Load Using Eye-Tracking Parameters Based on Algorithm Description Tools

Writing a computer program is a complex cognitive task, especially for a new person in the field. In this research an eye-tracking system was developed and applied, which allows the observation of eye movement parameters during programming as a complex, cognitive process, and the conclusions can be drawn from the results. The aim of the paper is to examine whether the flowchart or Nassi–Shneiderman diagram is a more efficient algorithm descripting tool for describing cognitive load by recording and evaluating eye movement parameters. The results show that the case of the interpreting flowchart has significantly longer fixation duration, more number of fixations, and larger pupil diameter than the case of the Nassi–Shneiderman diagram interpreting. Based on the results of the study, it is clear how important it is to choose the right programming tools for efficient and lower cost application development

Evaluation of 2D combination of eye-tracking metrics for task distinction

Eye-tracking techniques enable researchers to observe human behaviors by using eye tracking metrics. Machine learning is one of the techniques used in task inference. However, in our research in order to decrease the effort to analyze the task inference, we consider two combinations of different metrics on a two-dimensional scatter plot. Also, we analyze the data with K-Means clustering and correlation analysis to determine the task inference. Two-dimensional scatter plot let the analyst interact with the data in a better manner. In this thesis, we reduced the metrics dimensions, for example, calculating the mean value of the fixation durations that gave us a single value. We examined a few metrics such as crossings of saccades, first fixation duration after the onset of a stimulus, fixation duration mean, and fixation duration median. Furthermore, we created some custom metrics specifically for this research to analyze the tasks for the participants better. Next, we developed a simple game. In the game, there were three game modes for building distinctive gaze behavior. Those game modes include changes in the color tint information, size changes of the stimulus, and as a control mode, a text-only representation which does not contain any color or size differences. Finally, we made a study with six participants. They played our game to give us a dataset which we can work in the analysis with K-means clustering. Nevertheless, the results were promising and helpful in distinguishing human behavior on different tasks. However, this research is not enough for task inference, and there are further improvements which could achieve a better result than the current state

Exploiting physiological changes during the flow experience for assessing virtual-reality game design.

Immersive experiences are considered the principal attraction of video games. Achieving a healthy balance between the game's demands and the user's skills is a particularly challenging goal. However, it is a coveted outcome, as it gives rise to the flow experience – a mental state of deep concentration and game engagement. When this balance fractures, the player may experience considerable disinclination to continue playing, which may be a product of anxiety or boredom. Thus, being able to predict manifestations of these psychological states in video game players is essential for understanding player motivation and designing better games. To this end, we build on earlier work to evaluate flow dynamics from a physiological perspective using a custom video game. Although advancements in this area are growing, there has been little consideration given to the interpersonal characteristics that may influence the expression of the flow experience. In this thesis, two angles are introduced that remain poorly understood. First, the investigation is contextualized in the virtual reality domain, a technology that putatively amplifies affective experiences, yet is still insufficiently addressed in the flow literature. Second, a novel analysis setup is proposed, whereby the recorded physiological responses and psychometric self-ratings are combined to assess the effectiveness of our game's design in a series of experiments. The analysis workflow employed heart rate and eye blink variability, and electroencephalography (EEG) as objective assessment measures of the game's impact, and self-reports as subjective assessment measures. These inputs were submitted to a clustering method, cross-referencing the membership of the observations with self-report ratings of the players they originated from. Next, this information was used to effectively inform specialized decoders of the flow state from the physiological responses. This approach successfully enabled classifiers to operate at high accuracy rates in all our studies. Furthermore, we addressed the compression of medium-resolution EEG sensors to a minimal set required to decode flow. Overall, our findings suggest that the approaches employed in this thesis have wide applicability and potential for improving game designing practices