Literature DB >> 27149294

Decision processes in visual search as a function of target prevalence.

Chad Peltier1, Mark W Becker1.   

Abstract

The probability of missing a target increases in low target prevalence search tasks. Wolfe and Van Wert (2010) propose 2 causes of this effect: reducing the quitting threshold, and conservatively shifting the decision making criterion used to evaluate each item. Reducing the quitting threshold predicts that target absent responses will be made without fully inspecting the display, increasing misses due to never inspecting the target (selection errors). The shift in decision criterion increases the likelihood of failing to recognize an inspected target (identification errors). Though there is robust evidence that target prevalence rates shift quitting thresholds, the proposed shift in decision making criterion has little support. In Experiment 1 we eye-tracked participants during searches of high, medium, and low prevalence. Eye movements were used to classify misses as selection or identification errors. Identification errors increased as prevalence decreased, supporting the claim that decision criterion becomes more conservative as prevalence decreases. In addition, as prevalence decreased, the dwell time on targets increased while dwell times on distractors decreased. We propose that the effect of prevalence on decision making for individual items is best modeled as a shift in criterion in a drift diffusion model, rather than signal detection, as drift diffusion accounts for this pattern of decision times. In Experiment 2 we replicate these findings while presenting stimuli in an rapid serial visual presentation (RSVP) stream. Experiments 1 and 2 were consistent with the conclusion that prevalence rate influences the item-by-item decision criterion, and are consistent with a drift diffusion model of this decision process. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

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Year:  2016        PMID: 27149294     DOI: 10.1037/xhp0000248

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


  9 in total

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Authors:  Megan H Papesh; Juan D Guevara Pinto
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2.  Temporal integration of feature probability distributions.

Authors:  Sabrina Hansmann-Roth; Sóley Þorsteinsdóttir; Joy J Geng; Árni Kristjánsson
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3.  Quitting thresholds in visual search are impacted by target present detection times but not their variability.

Authors:  Mark W Becker; Andrew Rodriguez; Dana Pontious
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Review 4.  Avoiding potential pitfalls in visual search and eye-movement experiments: A tutorial review.

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Journal:  Atten Percept Psychophys       Date:  2021-06-04       Impact factor: 2.199

5.  Individual differences predict low prevalence visual search performance.

Authors:  Chad Peltier; Mark W Becker
Journal:  Cogn Res Princ Implic       Date:  2017-01-30

Review 6.  Using Eye Movements to Understand how Security Screeners Search for Threats in X-Ray Baggage.

Authors:  Nick Donnelly; Alex Muhl-Richardson; Hayward J Godwin; Kyle R Cave
Journal:  Vision (Basel)       Date:  2019-06-04

7.  Eye movements reflect expertise development in hybrid search.

Authors:  Megan H Papesh; Michael C Hout; Juan D Guevara Pinto; Arryn Robbins; Alexis Lopez
Journal:  Cogn Res Princ Implic       Date:  2021-02-15

8.  The optimal use of computer aided detection to find low prevalence cancers.

Authors:  Melina A Kunar
Journal:  Cogn Res Princ Implic       Date:  2022-02-04

9.  Eye movement feedback fails to improve visual search performance.

Authors:  Chad Peltier; Mark W Becker
Journal:  Cogn Res Princ Implic       Date:  2017-11-22
  9 in total

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