As the amount of information captured about users increased over the last decade, interest in personalized user interfaces has surged in the HCI and IS communities. Personalization is an effective means for accommodating for differences between individuals. The fundamental idea behind personalization rests on the notion that if a system can gather useful information about the user, generate a relevant user model and apply it appropriately, it would be possible to adapt the behavior of a system and its interface to the user at the individual level. Personal-ization of a user interface features can enhance usability. With recent technological advances, personalization can be achieved automatically and unobtrusively. A user interface can deploy a NeuroIS technology such as eye-tracking that learns from the user's visual behavior to provide users an experience most unique to them. The advantage of eye-tracking technology is that subjects cannot consciously manipulate their responses since they are not readily subject to manipulation. The objective of this dissertation is to develop a theoretical framework for user personalization during reading comprehension tasks based on two machine learning (ML) models. The proposed ML-based profiling process consists of user's age characterization and user's cognitive load detection, while the user reads text. To this end, detection of cognitive load through eye-movement features was investigated during different cognitive tasks (see Chapters 3, 4 and 6) with different task conditions. Furthermore, in separate studies (see Chapters 5 and 6) the relationship between user's eye-movements and their age population (e.g., younger and older generations) were carried out during a reading comprehension task. A Tobii X300 eye tracking device was used to record the eye movement data for all studies. Eye-movement data was acquired via Tobii eye tracking software, and then preprocessed and analyzed in R for the aforementioned studies. Machine learning techniques were used to build predictive models. The aggregated results of the studies indicate that machine learning accompanied with a NeuroIS tool like eye-tracking, can be used to model user characteristics like age and user mental states like cognitive load, automatically and implicitly with accuracy above chance (range of 70-92%). The results of this dissertation can be used in a more general framework to adaptively modify content to better serve the users mental and age needs. Text simplification and modification techniques might be developed to be used in various scenarios.

An early detection of hearing loss in children is important for the child's speech and language development. For children between 3-6 months, a reliable method to measure hearing and determine hearing thresholds is missing. A hearing test based on the pupillary response to auditory signal detection as measured by eye tracking is based on an automatic physiological response. This hearing test could be used instead of the objective hearing tests used today. The presence of pupillary response has been shown in response to speech, but it is unstudied in response to sinus tones. The objective of this thesis was to study whether there is a consistent pupillary response to different sinus tone frequencies commonly used in hearing tests and if yes, to determine reliably the time window of this response. Four different tests were done. The adult pupillary response in regard to sinus tone stimuli with four frequency levels (500 Hz, 1000 Hz, 2000 Hz and 4000 Hz), and four loudness levels (silence, 30 dB, 50 dB and 70 dB) was tested (N=20, 15 females, 5 males). Different brightness levels and distractions on the eye tracking screen were investigated in three substudies (N=5, 4 females, 1 male). Differences between silence and loudness levels within frequency levels were tested for statistical significance. A pupillary response in regard to sinus tones occurred consistently between 300 ms and 2000 ms with individual variation, i.e. earlier than for speech sounds. Differences between silence and loudness levels were only statistically significant for 4000 Hz. No statistical difference was shown between different brightness levels or if there were distractions present on the eye tracker screen. The conclusion is that pupillary response to pure sinus tones in adults is a possible measure of hearing threshold for at least 4000 Hz. Larger studies are needed to confirm this, and also to more thoroughly investigate the other frequencies. / En tidig upptäckt av hörselnedsättning hos barn är viktig för barnets tal- och språkutveckling. För barn mellan 3-6 månader saknas det en tillförlitlig metod för att mäta hörsel och bestämma hörtrösklar. Ett hörseltest baserad på pupillreaktion på ljud som mäts med en eye tracker bygger på en automatisk fysiologisk reaktion och skulle kunna användas istället för de objektiva test som används idag. Hitintills har pupillreaktion på tal påvisats, men det saknas studier som studerat eventuella reaktioner på sinustoner. Syftet med denna uppsats var att undersöka om det finns en enhetlig pupillreaktion på de olika frekvenserna av sinustoner som vanligen används i hörseltest. Vidare var studiens syfte att fastställa ett tillförlitligt tidsfönster för pupillreaktion. Fyra olika typer av tester utfördes. Pupillreaktionen mot sinustoner med fyra olika frekvensnivåer (500 Hz, 1000 Hz, 2000 Hz och 4000 Hz), och fyra olika ljudnivåer (tystnad, 30 dB, 50 dB och 70 dB) undersöktes i ett test på vuxna deltagare (N=20, 15 kvinnor, 5 män). Olika ljusnivåer och distraktioner på eye tracker-skärmen undersöktes i tre test (N=5, 4 kvinnor, 1 man). Skillnaderna mellan ljudnivåer och frekvensnivåer testades med statistiska tester. Resultaten visade att pupillreaktion på sinustoner inträffade konsekvent mellan 300 ms och 2000 ms med individuella variationer. Denna reaktionstid inträffar tidigare än för taljud. En statistisk signifikant skillnad mellan tystnad och olika ljudnivåer kunde endast ses för frekvensnivån 4000 Hz. Ingen statistisk skillnad uppmättes mellan olika ljudnivåer eller om det fanns distraktioner på eye tracker-skärmen. De i studien framkomna resultaten tyder på att pupillreaktioner mot rena sinustoner hos vuxna är en möjlig metod för att identifiera hörseltrösklar för åtminstone 4000 Hz. Större studier behöver göras för att fastställa detta och en noggrannare undersökning behöver genomföras för de andra frekvenserna.