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ABSTRACT
This study investigates dynamic information acquisition strategies during decision making. The authors conduct an eye-tracking experiment to trace consumers‘ moment-to-moment decision process on comparison websites. A new hierarchical Hidden Markov Model is developed to analyze the eye-movement data. It consists of three connected hierarchical layers: a lower layer that describes the eye-movements, a middle layer that captures product-based and attribute-based information acquisition strategies, and an upper layer that enables us to analyze the time course of switching between these information acquisition strategies. In the experiment on the effects of presentation formats of comparison websites for laptop computers, the authors quantify the usage of information acquisition strategies, identify switching patterns, and investigate the impact that strategy switching has on evaluation of the choice process. Consumers switch frequently between information acquisition strategies: around 50 to 60 times for the average decision. The contiguity of presented information and the row-column presentation format influence information strategy usage and product choice. These findings support our recommendations for the rapidly growing comparison website industry.

USA 2010 HCI Usability Tobii eye tracking 1750 dynamic information acquisition strategies comparison websites Switching Hidden Markov model

in list: HCI & Usability

ABSTRACT
In this thesis we present an evaluation of machine learning methods for real-time classification of reading in eye movements recorded by an eye tracker. The classification uses the relative positions of fixations in the gaze data. The methods evaluated are Hidden Markov models and Artificial Neural Networks. We conclude that real-time classification indeed is possible and that Hidden Markov models provide better predictability in terms of performance and better actual performance. The Hidden Markov Models also are more flexible as the number of fixations used as input can be adjusted at runtime to make a tradeoff between speed and classification performance.

Sweden 2010 technology Tobii eye tracking evaluation machine learning classification reading movement Hidden Markov model

in list: Eye Tracking Technology

Abstract
We study how processing states alternate during information search tasks. Inference is carried out with a discriminative hidden Markov model (dHMM) learned from eye movement data, measured in an experiment consisting of three task types: (i) simple word search, (ii) finding a sentence that answers a question and (iii) choosing a subjectively most interesting title from a list of ten titles. The results show that eye movements contain necessary information for determining the task type. After training, the dHMM predicted the task for test data with 60.2% accuracy (pure chance 33.3%). Word search and subjective interest conditions were easier to predict than the question-answer condition. The dHMM that best fitted our data segmented each task type into three hidden states. The three processing states were identified by comparing the parameters of the dHMM states to literature on eye movement research. A scanning type of eye behavior was observed in the beginning of the tasks. Next, participants tended to shift to states reflecting reading type of eye movements, and finally they ended the tasks in states which we termed as the decision states.

Eye movements Computational models Hidden Markov model Information search Scanning Reading Decision process eye tracking Finland 2008 Tobii

in list: Cognitive & Behavioural Psychology