John M. Henderson
Published November 19, 2012
Reference - 264 Pages
ISBN 9781848727700 - CAT# Y140431
Series: Special Issues of Visual Cognition
What we see and understand about the visual world is tightly tied to where we direct our eyes. High-resolution visual information is acquired from only a very limited region of the scene surrounding the fixation point, with the quality of visual input falling off precipitously from central vision into a low-resolution visual surround. This special issue of Visual Cognition brings together cutting-edge research from eight research groups around the world whose work is focused on these important topics. The goal of this special issue is to facilitate a constructive convergence of behavioral data and computational modeling to explore the fundamental nature of attention control, and particularly eye movement control, in viewing complex visual input.
1. Introduction: Computational Approaches to Reading and Scene Perception John M. Henderson 2. Eye movements in reading versus non-reading tasks: Using E-Z Reader to understand the role of word/stimulus familiarity Erik D. Reichle, Keith Rayner and Alexander Pollatsek 3. The zoom lens of attention: Simulating shuffled versus normal text reading using the SWIFT model Daniel J. Schad and Ralf Engbert 4. The utility of modelling word identification from visual input within models of eye movements in reading Klinton Bicknell and Roger Levy 5. Using CRISP to model global characteristics of fixation durations in scene viewing and reading with a common mechanism Antje Nuthmann and John M. Henderson 6. Eye movement prediction and variability on natural video data sets Michael Dorr, Eleonora Vig and Erhardt Barth 7. TAM: Explaining off-object fixations and central fixation tendencies as effects of population averaging during search Gregory J. Zelinsky 8. Modelling the influence of central and peripheral information on saccade biases in gaze-contingent scene viewing Tom Foulsham and Alan Kingstone 9. Influence of the amount of context learned for improving object classification when simultaneously learning object and contextual cues Sophie Marat and Laurent Itti