Decision Prediction Using Visual Patterns

Lack of understanding of users' underlying decision making process results in the bottleneck of EB-HCI eye movement-based human-computer interaction systems. Meanwhile, considerable findings on visual features of decision making have been derived from cognitive researches over past few years. A promising method of decision prediction in EB-HCI systems is presented in this article, which is inspired by the looking behavior when a user makes a decision. As two features of visual decision making, gaze bias and pupil dilation are considered into judging intensions. This method combines the history of eye movements to a given interface and the visual traits of users. Hence, it improves the prediction performance in a more natural and objective way. We apply the method to an either-or choice making task on the commercial Web pages to test its effectiveness. Although the result shows a good performance only of gaze bias but not of pupil dilation to predict a decision, it proves that hiring the visual traits of users is an effective approach to improve the performance of automatic triggering in EB-HCI systems.

[1]  Toni Granollers,et al.  COMPUTER VISION INTERACTION FOR PEOPLE WITH SEVERE MOVEMENT RESTRICTIONS , 2006 .

[2]  Christof Koch,et al.  Fully Formatted Pdf and Full Text (html) Versions Will Be Made Available Soon. Pupil Dilation Betrays the Timing of Decisions , 2022 .

[3]  H. Nittono,et al.  Gaze Shifts Do Not Affect Preference Judgments of Graphic Patterns , 2009, Perceptual and motor skills.

[4]  Elizabeth R Schotter,et al.  Gaze bias: Selective encoding and liking effects , 2010 .

[5]  R. Pieters,et al.  Visual attention during brand choice : The impact of time pressure and task motivation , 1999 .

[6]  J. Bradshaw,et al.  Pupil Size as a Measure of Arousal during Information Processing , 1967, Nature.

[7]  Gwo-Dong Chen,et al.  AN EYE TRACKING SYSTEM AND ITS APPLICATION IN AIDS FOR PEOPLE WITH SEVERE DISABILITIES , 2006 .

[8]  S. Shimojo,et al.  Early interactions between orienting, visual sampling and decision making in facial preference , 2006, Vision Research.

[9]  Eyal M. Reingold,et al.  Eye Movement Monitoring as a Process Tracing Methodology in Decision Making Research , 2011 .

[10]  Robert J. K. Jacob,et al.  What you look at is what you get: eye movement-based interaction techniques , 1990, CHI '90.

[11]  S. Iliffe,et al.  Bmc Medical Research Methodology Open Access the Hawthorne Effect: a Randomised, Controlled Trial , 2007 .

[12]  Mark S. Gilzenrat,et al.  Pupil diameter tracks changes in control state predicted by the adaptive gain theory of locus coeruleus function , 2010, Cognitive, affective & behavioral neuroscience.

[13]  Eyal M. Reingold,et al.  The time course of gaze bias in visual decision tasks , 2009 .

[14]  A. L. Yarbus.  Eye Movements During Perception of Complex Objects , 1967 .

[15]  Andrew T Duchowski,et al.  A breadth-first survey of eye-tracking applications , 2002, Behavior research methods, instruments, & computers : a journal of the Psychonomic Society, Inc.

[16]  Eyal M. Reingold,et al.  Predicting preference from fixations , 2009, PsychNology J..

[17]  Thorsten O. Zander,et al.  Combining Eye Gaze Input With a Brain–Computer Interface for Touchless Human–Computer Interaction , 2010, Int. J. Hum. Comput. Interact..

[18]  H M Simpson,et al.  Pupillary Changes during a Decision-Making Task , 1969, Perceptual and motor skills.

[19]  S. Shimojo,et al.  Gaze bias both reflects and influences preference , 2003, Nature Neuroscience.

[20]  Shinsuke Shimojo,et al.  Interrupting the cascade: Orienting contributes to decision making even in the absence of visual stimulation , 2007, Perception & psychophysics.

[21]  Minoru Ohyama,et al.  An Eye-Gaze Input System Using Information on Eye Movement History , 2007, HCI.

[22]  Eyal M Reingold,et al.  Evidence for top-down control of eye movements during visual decision making. , 2010, Journal of vision.

[23]  C. Koch,et al.  Computational modelling of visual attention , 2001, Nature Reviews Neuroscience.

[24]  Richard A. Bolt.  Eyes at the interface , 1982, CHI '82.

[25]  Jonathan D. Cohen,et al.  An integrative theory of locus coeruleus-norepinephrine function: adaptive gain and optimal performance. , 2005, Annual review of neuroscience.