title: Humans have idiosyncratic and task-specific scanpaths for judging faces.
abstract: Since Yarbus's seminal work, vision scientists have argued that our eye movement patterns differ depending upon our task. This has recently motivated the creation of multi-fixation pattern analysis algorithms that try to infer a person's task (or mental state) from their eye movements alone. Here, we introduce new algorithms for multi-fixation pattern analysis, and we use them to argue that people have scanpath routines for judging faces. We tested our methods on the eye movements of subjects as they made six distinct judgments about faces. We found that our algorithms could detect whether a participant is trying to distinguish angriness, happiness, trustworthiness, tiredness, attractiveness, or age. However, our algorithms were more accurate at inferring a subject's task when only trained on data from that subject than when trained on data gathered from other subjects, and we were able to infer the identity of our subjects using the same algorithms. These results suggest that (1) individuals have scanpath routines for judging faces, and that (2) these are diagnostic of that subject, but that (3) at least for the tasks we used, subjects do not converge on the same "ideal" scanpath pattern. Whether universal scanpath patterns exist for a task, we suggest, depends on the task's constraints and the level of expertise of the subject.
authors: Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA. Electronic address: ckanan@caltech.edu.
authors: Christopher
authors: C
authors: Kanan
authors: Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA, USA. Electronic address: dbseiso@ucsd.edu.
authors: Dina N F
authors: DN
authors: Bseiso
authors: Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA, USA. Electronic address: niray@ucsd.edu.
authors: Nicholas A
authors: NA
authors: Ray
authors: Department of Psychology, University of Hong Kong, Hong Kong. Electronic address: jhsiao@hku.hk.
authors: Janet H
authors: JH
authors: Hsiao
authors: Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA, USA. Electronic address: gary@ucsd.edu.
authors: Garrison W
authors: GW
authors: Cottrell
mesh_terms: D000293:Adolescent; D000328:Adult; D000465:Algorithms; D001288:Attention / Q000502:physiology*; D005133:Eye Movements / Q000502:physiology*; D005145:Face; D005149:Facial Expression; D000066499:Facial Recognition / Q000502:physiology*; D005260:Female; D005403:Fixation, Ocular / Q000502:physiology; D006801:Humans; D007600:Judgment / Q000502:physiology; D008297:Male; D010364:Pattern Recognition, Visual / Q000502:physiology; D010775:Photic Stimulation / Q000379:methods; D011930:Reaction Time; D021641:Recognition, Psychology / Q000502:physiology; D055815:Young Adult
keywords: Eye movements; Face perception; Machine learning; Scanpath routines