title: Modeling task control of eye movements.
abstract: In natural behavior, visual information is actively sampled from the environment by a sequence of gaze changes. The timing and choice of gaze targets, and the accompanying attentional shifts, are intimately linked with ongoing behavior. Nonetheless, modeling of the deployment of these fixations has been very difficult because they depend on characterizing the underlying task structure. Recently, advances in eye tracking during natural vision, together with the development of probabilistic modeling techniques, have provided insight into how the cognitive agenda might be included in the specification of fixations. These techniques take advantage of the decomposition of complex behaviors into modular components. A particular subset of these models casts the role of fixation as that of providing task-relevant information that is rewarding to the agent, with fixation being selected on the basis of expected reward and uncertainty about environmental state. We review this work here and describe how specific examples can reveal general principles in gaze control.
authors.affiliation: Department of Psychology, 108 E. Dean Keeton, Stop A8000, The University of Texas at Austin, Austin, TX 78712, USA. Electronic address: mary@mail.cps.utexas.edu.
authors.forename: Mary
authors.initials: M
authors.lastname: Hayhoe
authors.affiliation: Department of Computer Science, 2317 Speedway, Stop D9500, The University of Texas at Austin, Austin, TX 78712, USA. Electronic address: dana@cs.utexas.edu.
authors.forename: Dana
authors.initials: D
authors.lastname: Ballard
mesh_terms: D001499:Bayes Theorem; D005133:Eye Movements / Q000502:physiology*; D005403:Fixation, Ocular / Q000502:physiology*; D006801:Humans; D008954:Models, Biological; D011597:Psychomotor Performance / Q000502:physiology*; D012201:Reward; D035501:Uncertainty