Literature DB >> 25915073

Failures of perception in the low-prevalence effect: Evidence from active and passive visual search.

Michael C Hout1, Stephen C Walenchok2, Stephen D Goldinger2, Jeremy M Wolfe3.   

Abstract

In visual search, rare targets are missed disproportionately often. This low-prevalence effect (LPE) is a robust problem with demonstrable societal consequences. What is the source of the LPE? Is it a perceptual bias against rare targets or a later process, such as premature search termination or motor response errors? In 4 experiments, we examined the LPE using standard visual search (with eye tracking) and 2 variants of rapid serial visual presentation (RSVP) in which observers made present/absent decisions after sequences ended. In all experiments, observers looked for 2 target categories (teddy bear and butterfly) simultaneously. To minimize simple motor errors, caused by repetitive absent responses, we held overall target prevalence at 50%, with 1 low-prevalence and 1 high-prevalence target type. Across conditions, observers either searched for targets among other real-world objects or searched for specific bears or butterflies among within-category distractors. We report 4 main results: (a) In standard search, high-prevalence targets were found more quickly and accurately than low-prevalence targets. (b) The LPE persisted in RSVP search, even though observers never terminated search on their own. (c) Eye-tracking analyses showed that high-prevalence targets elicited better attentional guidance and faster perceptual decisions. And (d) even when observers looked directly at low-prevalence targets, they often (12%-34% of trials) failed to detect them. These results strongly argue that low-prevalence misses represent failures of perception when early search termination or motor errors are controlled. (c) 2015 APA, all rights reserved).

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Year:  2015        PMID: 25915073      PMCID: PMC5543182          DOI: 10.1037/xhp0000053

Source DB:  PubMed          Journal:  J Exp Psychol Hum Percept Perform        ISSN: 0096-1523            Impact factor:   3.332


  50 in total

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Authors:  Mary J Bravo; Hany Farid
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Authors:  Hayward J Godwin; Stephen C Walenchok; Joseph W Houpt; Michael C Hout; Stephen D Goldinger
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3.  The cost of search for multiple targets: effects of practice and target similarity.

Authors:  Tamaryn Menneer; Kyle R Cave; Nick Donnelly
Journal:  J Exp Psychol Appl       Date:  2009-06

4.  False feedback increases detection of low-prevalence targets in visual search.

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5.  Spatial and temporal separation fails to counteract the effects of low prevalence in visual search.

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8.  Visual long-term memory has a massive storage capacity for object details.

Authors:  Timothy F Brady; Talia Konkle; George A Alvarez; Aude Oliva
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9.  A bayesian optimal foraging model of human visual search.

Authors:  Matthew S Cain; Edward Vul; Kait Clark; Stephen R Mitroff
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10.  Varying target prevalence reveals two dissociable decision criteria in visual search.

Authors:  Jeremy M Wolfe; Michael J Van Wert
Journal:  Curr Biol       Date:  2010-01-14       Impact factor: 10.834

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  21 in total

1.  Spotting rare items makes the brain "blink" harder: Evidence from pupillometry.

Authors:  Megan H Papesh; Juan D Guevara Pinto
Journal:  Atten Percept Psychophys       Date:  2019-11       Impact factor: 2.199

2.  The confirmation and prevalence biases in visual search reflect separate underlying processes.

Authors:  Stephen C Walenchok; Stephen D Goldinger; Michael C Hout
Journal:  J Exp Psychol Hum Percept Perform       Date:  2020-03       Impact factor: 3.332

3.  Comparing visual search and eye movements in bilinguals and monolinguals.

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4.  Implicit object naming in visual search: Evidence from phonological competition.

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5.  The influence of category representativeness on the low prevalence effect in visual search.

Authors:  Ryan E O'Donnell; Brad Wyble
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6.  Hybrid foraging search: Searching for multiple instances of multiple types of target.

Authors:  Jeremy M Wolfe; Avigael M Aizenman; Sage E P Boettcher; Matthew S Cain
Journal:  Vision Res       Date:  2016-01-20       Impact factor: 1.886

Review 7.  Avoiding potential pitfalls in visual search and eye-movement experiments: A tutorial review.

Authors:  Hayward J Godwin; Michael C Hout; Katrín J Alexdóttir; Stephen C Walenchok; Anthony S Barnhart
Journal:  Atten Percept Psychophys       Date:  2021-06-04       Impact factor: 2.199

8.  Feedback moderates the effect of prevalence on perceptual decisions.

Authors:  Wanyi Lyu; David E Levari; Makaela S Nartker; Daniel S Little; Jeremy M Wolfe
Journal:  Psychon Bull Rev       Date:  2021-06-25

9.  Understanding the contribution of target repetition and target expectation to the emergence of the prevalence effect in visual search.

Authors:  Hayward J Godwin; Tamaryn Menneer; Charlotte A Riggs; Dominic Taunton; Kyle R Cave; Nick Donnel
Journal:  Psychon Bull Rev       Date:  2016-06

10.  A Graph is Worth a Thousand Words: How Overconfidence and Graphical Disclosure of Numerical Information Influence Financial Analysts Accuracy on Decision Making.

Authors:  Ricardo Lopes Cardoso; Rodrigo Oliveira Leite; André Carlos Busanelli de Aquino
Journal:  PLoS One       Date:  2016-08-10       Impact factor: 3.240

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