Literature DB >> 10615487

A simple saliency model predicts a number of motion popout phenomena.

R Rosenholtz1.   

Abstract

Visual search for a moving target among stationary distractors is more efficient than searching for a stationary target among moving distractors, and searching for a fast target among slow distractors is more efficient than vice versa. This indicates that the ease of search for a target with a particular motion is not determined simply by the difference between target and distractor velocities. We suggest a simple model for predicting ease of search for a unique motion, based upon a quantitative measure of target saliency. Essentially, search will be easier the more the target motion deviates from the general pattern of velocities in the scene. Our model predicts a number of well-known motion search phenomena, and suggests that one control for target saliency as well as target discriminability when drawing conclusions about visual system mechanisms from search experiments.

Mesh:

Year:  1999        PMID: 10615487     DOI: 10.1016/s0042-6989(99)00077-2

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


  22 in total

1.  Shared attentional resources for global and local motion processing.

Authors:  Paul F Bulakowski; David W Bressler; David Whitney
Journal:  J Vis       Date:  2007-07-24       Impact factor: 2.240

2.  A unique visual rhythm does not pop out.

Authors:  Hui Li; Yan Bao; Ernst Pöppel; Yi-Huang Su
Journal:  Cogn Process       Date:  2013-10-11

3.  Do high-functioning people with autism spectrum disorder spontaneously use event knowledge to selectively attend to and remember context-relevant aspects in scenes?

Authors:  Eva Loth; Juan Carlós Gómez; Francesca Happé
Journal:  J Autism Dev Disord       Date:  2011-07

4.  Cube search, revisited.

Authors:  Xuetao Zhang; Jie Huang; Serap Yigit-Elliott; Ruth Rosenholtz
Journal:  J Vis       Date:  2015-03-16       Impact factor: 2.240

5.  Contributions of ensemble perception to outlier representation precision.

Authors:  Burcu Avci; Aysecan Boduroglu
Journal:  Atten Percept Psychophys       Date:  2021-03-16       Impact factor: 2.199

6.  SUN: Top-down saliency using natural statistics.

Authors:  Christopher Kanan; Mathew H Tong; Lingyun Zhang; Garrison W Cottrell
Journal:  Vis cogn       Date:  2009-08-01

7.  An explicit investigation of the roles that feature distributions play in rapid visual categorization.

Authors:  Hee Yeon Im; Natalia A Tiurina; Igor S Utochkin
Journal:  Atten Percept Psychophys       Date:  2021-04       Impact factor: 2.199

8.  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

9.  A summary-statistic representation in peripheral vision explains visual crowding.

Authors:  Benjamin Balas; Lisa Nakano; Ruth Rosenholtz
Journal:  J Vis       Date:  2009-11-19       Impact factor: 2.240

10.  Capture of attention to threatening stimuli without perceptual awareness.

Authors:  Jeffrey Y Lin; Scott O Murray; Geoffrey M Boynton
Journal:  Curr Biol       Date:  2009-06-11       Impact factor: 10.834

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