title: Decoding Reading Goals from Eye Movements
summary: Readers can have different goals with respect to the text they are reading.
  Can these goals be decoded from the pattern of their eye movements over the
  text? In this work, we examine for the first time whether it is possible to
  decode two types of reading goals that are common in daily life: information
  seeking and ordinary reading. Using large scale eye-tracking data, we apply to
  this task a wide range of state-of-the-art models for eye movements and text
  that cover different architectural and data representation strategies, and
  further introduce a new model ensemble. We systematically evaluate these models
  at three levels of generalization: new textual item, new participant, and the
  combination of both. We find that eye movements contain highly valuable signals
  for this task. We further perform an error analysis which builds on prior
  empirical findings on differences between ordinary reading and information
  seeking and leverages rich textual annotations. This analysis reveals key
  properties of textual items and participant eye movements that contribute to
  the difficulty of the task.
paper_authors._id: 672287ddb915ce8c01cf78d7
paper_authors.name: Omer Shubi
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-31T09:54:50.442Z
paper_authors._id: 60ef001bed64a34082bfa0dd
paper_authors.avatarUrl: /avatars/78e4daeac169edbf4dc42fbed9b50d59.svg
paper_authors.fullname: Omer Shubi
paper_authors.type: user
paper_authors.user: scaperex
paper_authors._id: 672287ddb915ce8c01cf78d8
paper_authors.name: Cfir Avraham Hadar
paper_authors._id: 672287ddb915ce8c01cf78d9
paper_authors.name: Yevgeni Berzak
paper_title: Decoding Reading Goals from Eye Movements
paper_summary: Readers can have different goals with respect to the text they are reading.
  Can these goals be decoded from the pattern of their eye movements over the
  text? In this work, we examine for the first time whether it is possible to
  decode two types of reading goals that are common in daily life: information
  seeking and ordinary reading. Using large scale eye-tracking data, we apply to
  this task a wide range of state-of-the-art models for eye movements and text
  that cover different architectural and data representation strategies, and
  further introduce a new model ensemble. We systematically evaluate these models
  at three levels of generalization: new textual item, new participant, and the
  combination of both. We find that eye movements contain highly valuable signals
  for this task. We further perform an error analysis which builds on prior
  empirical findings on differences between ordinary reading and information
  seeking and leverages rich textual annotations. This analysis reveals key
  properties of textual items and participant eye movements that contribute to
  the difficulty of the task.
paper_ai_summary: Eye movement patterns can be used to differentiate between information-seeking and ordinary reading using various models and data representations.
paper_ai_keywords: eye-tracking data model ensemble generalization textual annotations error analysis