Risk and Ambiguity in Information Seeking: Eye Gaze Patterns Reveal Contextual Behavior in Dealing with Uncertainty

Abstract

Information foraging connects optimal foraging theory in ecology with how humans search for information. The theory suggests that, following an information scent, the information seeker must optimize the tradeoff between exploration by repeated steps in the search space vs. exploitation, using the resources encountered. We conjecture that this tradeoff characterizes how a user deals with uncertainty and its two aspects, risk and ambiguity in economic theory. Risk is related to the perceived quality of the actually visited patch of information, and can be reduced by exploiting and understanding the patch to a better extent. Ambiguity, on the other hand, is the opportunity cost of having higher quality patches elsewhere in the search space. The aforementioned tradeoff depends on many attributes, including traits of the user: at the two extreme ends of the spectrum, analytic and wholistic searchers employ entirely different strategies. The former type focuses on exploitation first, interspersed with bouts of exploration, whereas the latter type prefers to explore the search space first and consume later. Our findings from an eye-tracking study of experts' interactions with novel search interfaces in the biomedical domain suggest that user traits of cognitive styles and perceived search task difficulty are significantly correlated with eye gaze and search behavior. We also demonstrate that perceived risk shifts the balance between exploration and exploitation in either type of users, tilting it against vs. in favor of ambiguity minimization. Since the pattern of behavior in information foraging is quintessentially sequential, risk and ambiguity minimization cannot happen simultaneously, leading to a fundamental limit on how good such a tradeoff can be. This in turn connects information seeking with the emergent field of quantum decision theory.

Relevance Prediction from Eye-movements Using Semi-interpretable Convolutional Neural Networks

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Utilising Information Foraging Theory for User Interaction with Image Query Auto-Completion

“…For instance, sections and their associated features in search engine results can be considered patches. From a foraging perspective in image search, the searcher is the predator (or forager [38]), the information patch is any segment or a region within an image (or image itself) in a given information environment. The piece of information a user is looking for is the prey, and the consumed (or gained) information is the information diet.…”

Consumers' Bayesian learning under Knightian uncertainty: An eye‐tracking analysis

“…Decision making under uncertainty is what our brain does most of the time, whereas situations of known risk are relatively rare and found mostly in gambling. Recently, decisions under Knightian uncertainty have received increasingly more attention (Al-Najjar & Weinstein, 2015;Feldman Hall, Glimcher, Baker, & Phelps, 2016;Keskin, 2017;Wittek, Liu, Darányi, Gedeon, & Lim, 2016), motivated by the gap between observed behavior and the predicted outcome of models. Therefore, introducing uncertainty into the basic theory would bring the model closer to the natural state.…”

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