Linking search tasks with low-level eye movement patterns

  • Authors:

  • Michael J. Cole;Jacek Gwizdka;Ralf Bierig;Nicholas J. Belkin;Jingjing Liu;Chang Liu;Xiangmin Zhang

  • Affiliations:

  • The State University of New Jersey, Huntington Street, New Brunswick, NJ;The State University of New Jersey, Huntington Street, New Brunswick, NJ;The State University of New Jersey, Huntington Street, New Brunswick, NJ;The State University of New Jersey, Huntington Street, New Brunswick, NJ;The State University of New Jersey, Huntington Street, New Brunswick, NJ;The State University of New Jersey, Huntington Street, New Brunswick, NJ;The State University of New Jersey, Huntington Street, New Brunswick, NJ

  • Venue:
  • Proceedings of the 28th Annual European Conference on Cognitive Ergonomics
  • Year:
  • 2010

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Abstract

Motivation -- On-the-task detection of the task type and task attributes can benefit personalization and adaptation of information systems. Research approach -- A web-based information search experiment was conducted with 32 participants using a multi-stream logging system. The realistic tasks were related directly to the backgrounds of the participants and were of distinct task types. Findings/Design -- We report on a relationship between task and individual reading behaviour. Specifically we show that transitions between scanning and reading behaviour in eye movement patterns are an implicit indicator of the current task. Research limitations/Implications -- This work suggests it is plausible to infer the type of information task from eye movement patterns. One limitation is a lack of knowledge about the general reading model differences across different types of tasks in the population. Although this is an experimental study we argue it can be generalized to real world text-oriented information search tasks. Originality/Value -- This research presents a new methodology to model user information search task behaviour. It suggests promise for detection of information task type based on patterns of eye movements. Take away message -- With increasingly complex computer interaction, knowledge about the type of information task can be valuable for system personalization. Modelling the reading/scanning patterns of eye movements can allow inference about the task type and task attributes.