Literature DB >> 23064405

Learning to break camouflage by learning the background.

Xin Chen1, Jay Hegdé.   

Abstract

How does the visual system recognize a camouflaged object? Obviously, the brain cannot afford to learn all possible camouflaged scenes or target objects. However, it may learn the general statistical properties of backgrounds of interest, which would enable it to break camouflage by comparing the statistics of a background with a target versus the statistics of the same background without a target. To determine whether the brain uses this strategy, we digitally created novel camouflaged scenes that had only the general statistical properties of the background in common. When subjects learned to break camouflage, their ability to detect a camouflaged target improved significantly not only for previously unseen instances of a camouflaged scene, but also for scenes that contained novel targets. Moreover, performance improved even for scenes that did not contain an actual target but had the statistical properties of backgrounds with a target. These results reveal that learning backgrounds is a powerful, versatile strategy by which the brain can learn to break camouflage.

Mesh:

Year:  2012        PMID: 23064405     DOI: 10.1177/0956797612445315

Source DB:  PubMed          Journal:  Psychol Sci        ISSN: 0956-7976


  9 in total

1.  Rapid perceptual processing in two- and three-dimensional prostate images.

Authors:  Melissa Treviño; Baris Turkbey; Bradford J Wood; Peter A Pinto; Marcin Czarniecki; Peter L Choyke; Todd S Horowitz
Journal:  J Med Imaging (Bellingham)       Date:  2020-01-03

2.  Heuristic Vetoing: Top-Down Influences of the Anchoring-and-Adjustment Heuristic Can Override the Bottom-Up Information in Visual Images.

Authors:  Fallon Branch; Erin Park; Jay Hegdé
Journal:  Front Neurosci       Date:  2022-05-20       Impact factor: 5.152

3.  Training and transfer of training in rapid visual search for camouflaged targets.

Authors:  Mark B Neider; Cher Wee Ang; Michelle W Voss; Ronald Carbonari; Arthur F Kramer
Journal:  PLoS One       Date:  2013-12-27       Impact factor: 3.240

4.  On the Hunt: Searching for Poorly Defined Camouflaged Targets.

Authors:  Alyssa S Hess; Andrew J Wismer; Corey J Bohil; Mark B Neider
Journal:  PLoS One       Date:  2016-03-28       Impact factor: 3.240

5.  Optimal background matching camouflage.

Authors:  Constantine Michalis; Nicholas E Scott-Samuel; David P Gibson; Innes C Cuthill
Journal:  Proc Biol Sci       Date:  2017-07-12       Impact factor: 5.349

6.  Expert camouflage-breakers can accurately localize search targets.

Authors:  Fallon Branch; Allison JoAnna Lewis; Isabella Noel Santana; Jay Hegdé
Journal:  Cogn Res Princ Implic       Date:  2021-04-06

7.  Satisfaction of Search Can Be Ameliorated by Perceptual Learning: A Proof-of-Principle Study.

Authors:  Erin Park; Fallon Branch; Jay Hegdé
Journal:  Vision (Basel)       Date:  2022-08-10

8.  Intuitively detecting what is hidden within a visual mask: familiar-novel discrimination and threat detection for unidentified stimuli.

Authors:  Anne M Cleary; Anthony J Ryals; Jason S Nomi
Journal:  Mem Cognit       Date:  2013-10

9.  Deep learning can be used to train naïve, nonprofessional observers to detect diagnostic visual patterns of certain cancers in mammograms: a proof-of-principle study.

Authors:  Jay Hegdé
Journal:  J Med Imaging (Bellingham)       Date:  2020-02-04
  9 in total

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