Literature DB >> 16469349

The long and the short of it: spatial statistics at fixation vary with saccade amplitude and task.

Benjamin W Tatler1, Roland J Baddeley, Benjamin T Vincent.   

Abstract

We recorded over 90,000 saccades while observers viewed a diverse collection of natural images and measured low level visual features at fixation. The features that discriminated between where observers fixated and where they did not varied considerably with task, and the length of the preceding saccade. Short saccades (<8 degrees) are image feature dependent, long are less so. For free viewing, short saccades target high frequency information, long saccades are scale-invariant. When searching for luminance targets, saccades of all lengths are scale-invariant. We argue that models of saccade behaviour must account not only for task but also for saccade length and that long and short saccades are targeted differently.

Mesh:

Year:  2006        PMID: 16469349     DOI: 10.1016/j.visres.2005.12.005

Source DB:  PubMed          Journal:  Vision Res        ISSN: 0042-6989            Impact factor:   1.886


  28 in total

1.  Eye movements during scene viewing: evidence for mixed control of fixation durations.

Authors:  John M Henderson; Pierce L Graham
Journal:  Psychon Bull Rev       Date:  2008-06

2.  Gist in time: Scene semantics and structure enhance recall of searched objects.

Authors:  Emilie L Josephs; Dejan Draschkow; Jeremy M Wolfe; Melissa L-H Võ
Journal:  Acta Psychol (Amst)       Date:  2016-06-03

Review 3.  Eye guidance in natural vision: reinterpreting salience.

Authors:  Benjamin W Tatler; Mary M Hayhoe; Michael F Land; Dana H Ballard
Journal:  J Vis       Date:  2011-05-27       Impact factor: 2.240

4.  TAM: Explaining off-object fixations and central fixation tendencies as effects of population averaging during search.

Authors:  Gregory J Zelinsky
Journal:  Vis cogn       Date:  2012-05-23

Review 5.  Making Sense of Real-World Scenes.

Authors:  George L Malcolm; Iris I A Groen; Chris I Baker
Journal:  Trends Cogn Sci       Date:  2016-10-18       Impact factor: 20.229

6.  There's Waldo! A Normalization Model of Visual Search Predicts Single-Trial Human Fixations in an Object Search Task.

Authors:  Thomas Miconi; Laura Groomes; Gabriel Kreiman
Journal:  Cereb Cortex       Date:  2015-06-19       Impact factor: 5.357

7.  Modeling Search for People in 900 Scenes: A combined source model of eye guidance.

Authors:  Krista A Ehinger; Barbara Hidalgo-Sotelo; Antonio Torralba; Aude Oliva
Journal:  Vis cogn       Date:  2009-08-01

8.  Eye movement prediction and variability on natural video data sets.

Authors:  Michael Dorr; Eleonora Vig; Erhardt Barth
Journal:  Vis cogn       Date:  2012-03-26

9.  Influence of low-level stimulus features, task dependent factors, and spatial biases on overt visual attention.

Authors:  Sepp Kollmorgen; Nora Nortmann; Sylvia Schröder; Peter König
Journal:  PLoS Comput Biol       Date:  2010-05-20       Impact factor: 4.475

10.  Learning where to look for a hidden target.

Authors:  Leanne Chukoskie; Joseph Snider; Michael C Mozer; Richard J Krauzlis; Terrence J Sejnowski
Journal:  Proc Natl Acad Sci U S A       Date:  2013-06-10       Impact factor: 11.205

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