Inferring relevance from eye movements with wrong models

Statistical inference forms the backbone of modern science. It is often viewed as giving an objective validation for hypotheses or models. Perhaps for this reason the theory of statistical inference is often derived with the assumption that the "truth" is within the model family. However, in many real-world applications the applied statistical models are incorrect. A more appropriate probabilistic model may be computationally too complex, or the problem to be modelled may be so new that there is little prior information to be incorporated. However, in statistical theory the theoretical and practical implications of the incorrectness of the model family are to a large extent unexplored. This thesis focusses on conditional statistical inference, that is, modeling of classes of future observations given observed data, under the assumption that the model is incorrect. Conditional inference or prediction is one of the main application areas of statistical models which is still lacking a conclusive theoretical justification of Bayesian inference. The main result of the thesis is an axiomatic derivation where, given an incorrect model and assuming that the utility is conditional likelihood, a discriminative posterior yields a distribution on model parameters which best agrees with the utility. The devised discriminative posterior outperforms the classical Bayesian joint likelihood-based approach in conditional inference. Additionally, a theoretically justified expectation maximization-type algorithm is presented for obtaining conditional maximum likelihood point estimates for conditional inference tasks. The convergence of the algorithm is shown to be more stable than in earlier partly heuristic variants. The practical application field of the thesis is inference of relevance from eye movement signals in an information retrieval setup. It is shown that relevance can be predicted to some extent, and that this information can be exploited in a new kind of task, proactive information retrieval. Besides making it possible to design new kinds of engineering applications, statistical modeling of eye tracking data can also be applied in basic psychological research to make hypotheses of cognitive processes affecting eye movements, which is the second application area of the thesis

The Role of Word-Eye-Fixations for Query Term Prediction

Throughout the search process, the user's gaze on inspected SERPs and websites can reveal his or her search interests. Gaze behavior can be captured with eye tracking and described with word-eye-fixations. Word-eye-fixations contain the user's accumulated gaze fixation duration on each individual word of a web page. In this work, we analyze the role of word-eye-fixations for predicting query terms. We investigate the relationship between a range of in-session features, in particular, gaze data, with the query terms and train models for predicting query terms. We use a dataset of 50 search sessions obtained through a lab study in the social sciences domain. Using established machine learning models, we can predict query terms with comparably high accuracy, even with only little training data. Feature analysis shows that the categories Fixation, Query Relevance and Session Topic contain the most effective features for our task

Mutual dependency-based modeling of relevance in co-occurrence data

In the analysis of large data sets it is increasingly important to distinguish the relevant information from the irrelevant. This thesis outlines how to find what is relevant in so-called co-occurrence data, where there are two or more representations for each data sample. The modeling task sets the limits to what we are interested in, and in its part defines the relevance. In this work, the problem of finding what is relevant in data is formalized via dependence, that is, the variation that is found in both (or all) co-occurring data sets was deemed to be more relevant than variation that is present in only one (or some) of the data sets. In other words, relevance is defined through dependencies between the data sets. The method development contributions of this thesis are related to latent topic models and methods of dependency exploration. The dependency-seeking models were extended to nonparametric models, and computational algorithms were developed for the models. The methods are applicable to mutual dependency modeling and co-occurrence data in general, without restriction to the applications presented in the publications of this work. The application areas of the publications included modeling of user interest, relevance prediction of text based on eye movements, analysis of brain imaging with fMRI and modeling of gene regulation in bioinformatics. Additionally, frameworks for different application areas were suggested. Until recently it has been a prevalent convention to assume the data to be normally distributed when modeling dependencies between different data sets. Here, a distribution-free nonparametric extension of Canonical Correlation Analysis (CCA) was suggested, together with a computationally more efficient semi-parametric variant. Furthermore, an alternative view to CCA was derived which allows a new kind of interpretation of the results and using CCA in feature selection that regards dependency as the criterion of relevance. Traditionally, latent topic models are one-way clustering models, that is, one of the variables is clustered by the latent variable. We proposed a latent topic model that generalizes in two ways and showed that when only a small amount of data has been gathered, two-way generalization becomes necessary. In the field of brain imaging, natural stimuli in fMRI studies imitate real-life situations and challenge the analysis methods used. A novel two-step framework was proposed for analyzing brain imaging measurements from fMRI. This framework seems promising for the analysis of brain signal data measured under natural stimulation, once such measurements are more widely available

Exploring the applicability of implicit relevance measures in varying reading speed for adaptive I.R. systems

Projecte realitzat en el marc d’un programa de mobilitat amb la University of Helsinki. Faculty of Science. Department of Computer ScienceThis thesis goes further in the study of implicit indicators used to infer interest in documents for information retrieval tasks. We study the behavior of two different categories of implicit indicators: fixation-derived features and physiology (pupil size, electrodermal activity). Based on the limited number of participants at our disposal we study how these measures react when addressing documents at three different reading rates. Most of the fixation-derived features are reported to differ significantly when reading at different speeds. Furthermore, the ability of pupil size and electrodermal activity to indicate perceived relevance is found intrinsically dependent on speed of reading. That is, when users read at comfortable reading speed, these measures are found to be able to correctly discriminate relevance judgments, but fail when increasing the addressed speed of reading. Therefore, the outcomes of this thesis strongly suggest to take into account reading speed when designing highly adaptive information retrieval systems

Exploring the applicability of implicit relevance measures in varying reading speed for adaptive I.R. systems

This thesis goes further in the study of implicit indicators used to infer interest in documents for information retrieval tasks. We study the behavior of two different categories of implicit indicators: fixation-derived features (number of fixations, average time of fixations, regression ratio, length of forward saccades), and physiology (pupil dilation, electrodermal activity). Based on the limited number of participants at our disposal we study how these measures react when addressing documents at three different reading rates. Most of the fixation-derived features are reported to differ significantly when reading at different speeds. Furthermore, the ability of pupil size and electrodermal activity to indicate perceived relevance is found intrinsically dependent on speed of reading. That is, when users read at comfortable reading speed, these measures are found to be able to correctly discriminate relevance judgments, but fail when increasing the addressed speed of reading. Therefore, the outcomes of this thesis strongly suggest to take into account reading speed when designing highly adaptive information retrieval systems

Just-in-time Information Interfaces: A new Paradigm for Information Discovery and Exploration

We live in a time of increasing information overload. Described as “a byproduct of the lack of maturity of the information age” (Spira & Goldes, 2007), information overload can be painful, and harm our concentration - the resulting choice overload impacts out decision-making abilities. Given the problem of information overload, and the unsatisfying nature of human-information interaction using traditional browsing or keyword-based search, this research investigates how the design of just-in-time information services can improve the user experience of goal-driven interactions with information. This thesis explores the design of just-in-time information services through the iterative development of two strands of high-level prototypes (FMI and KnowDis). I custombuilt both prototype systems for the respective evaluations, which have then been conducted as part of a series of lab-based eye-tracking studies (FMI) as well as two field studies (KnowDis). The lab-based eye-tracking studies were conducted with 100 participants measuring task performance, user satisfaction, and gaze behaviour. The lab studies found that the FMI prototype design did improve the performance aspect of the user experience for all participants and improved the usability aspect of the user experience for novice participants. However, the FMI prototype design seemed to be less effective and usable for expert participants. Two field studies were conducted as part of a two-year research collaboration, which lasted a total of 10 weeks and involved approximately 70 knowledge workers overall from across the globe. As part of those field studies, 46 semi-structured interviews were also conducted. The field studies found that the KnowDis prototype design did improve the user experience for participants overall by making work-related information search more efficient. However, while the KnowDis prototype design was useful for some knowledge workers and even integrated seamlessly into their day-to-day work, it appeared to be less useful and even distracting to others

Proceedings of the 4th Workshop on Interacting with Smart Objects 2015

These are the Proceedings of the 4th IUI Workshop on Interacting with Smart Objects. Objects that we use in our everyday life are expanding their restricted interaction capabilities and provide functionalities that go far beyond their original functionality. They feature computing capabilities and are thus able to capture information, process and store it and interact with their environments, turning them into smart objects