Paper Info
Title
Quiet Eye Affects Action Detection From Gaze More Than Context Length
Abstract
Every purposive interactive action begins with an intention to interact. In the domain of intelligent adaptive systems, behavioral signals linked to the actions are of great importance, and even though humans are good in such predictions, interactive systems are still falling behind. We explored mouse interaction and related eye-movement data from interactive problem solving situations and isolated sequences with high probability of interactive action. To establish whether one can predict the interactive action from gaze, we 1) analyzed gaze data using sliding fixation sequences of increasing length and 2) considered sequences several fixations prior to the action, either containing the last fixation before action (i.e. the quiet eye fixation) or not. Each fixation sequence was characterized by 54 gaze features and evaluated by an SVM-RBF classifier. The results of the systematic evaluation revealed importance of the quiet eye fixation and statistical differences of quiet eye fixation compared to other fixations prior to the action.
Year
DOI
Venue
2015
10.1007/978-3-319-20267-9_23
USER MODELING, ADAPTATION AND PERSONALIZATION
Keywords
Field
DocType
Action, Intentions, Prediction, Eye-tracking, SVM, Mouse interaction, Problem solving
Computer vision,Fixation (psychology),Quiet eye,Gaze,Computer science,Adaptive system,Support vector machine,Eye tracking,Artificial intelligence,Classifier (linguistics),Interactive problem solving
Conference
Volume
ISSN
Citations 
9146
0302-9743
3
PageRank 
References 
Authors
0.39
14
2
Name
Order
Citations
PageRank
Hana Vrzakova16211.41
Roman Bednarik256148.77