Decoding Reading Goals from Eye Movements
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.
672287ddb915ce8c01cf78d7
Omer Shubi
claimed_verified
2024-10-31T09:54:50.442Z
60ef001bed64a34082bfa0dd
/avatars/78e4daeac169edbf4dc42fbed9b50d59.svg
Omer Shubi
user
scaperex
672287ddb915ce8c01cf78d8
Cfir Avraham Hadar
672287ddb915ce8c01cf78d9
Yevgeni Berzak
Decoding Reading Goals from Eye Movements
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.
Eye movement patterns can be used to differentiate between information-seeking and ordinary reading using various models and data representations.
eye-tracking data model ensemble generalization textual annotations error analysis