Literature DB >> 23437633

Overcoming hurdles in translating visual search research between the lab and the field.

Kait Clark1, Matthew S Cain, Stephen H Adamo, Stephen R Mitroff.   

Abstract

Research in visual search can be vital to improving performance in careers such as radiology and airport security screening. In these applied, or "field," searches, accuracy is critical, and misses are potentially fatal; however, despite the importance of performing optimally, radiological and airport security searches are nevertheless flawed. Extensive basic research in visual search has revealed cognitive mechanisms responsible for successful visual search as well as a variety of factors that tend to inhibit or improve performance. Ideally, the knowledge gained from such laboratory-based research could be directly applied to field searches, but several obstacles stand in the way of straightforward translation; the tightly controlled visual searches performed in the lab can be drastically different from field searches. For example, they can differ in terms of the nature of the stimuli, the environment in which the search is taking place, and the experience and characteristics of the searchers themselves. The goal of this chapter is to discuss these differences and how they can present hurdles to translating lab-based research to field-based searches. Specifically, most search tasks in the lab entail searching for only one target per trial, and the targets occur relatively frequently, but field searches may contain an unknown and unlimited number of targets, and the occurrence of targets can be rare. Additionally, participants in lab-based search experiments often perform under neutral conditions and have no formal training or experience in search tasks; conversely, career searchers may be influenced by the motivation to perform well or anxiety about missing a target, and they have undergone formal training and accumulated significant experience searching. This chapter discusses recent work that has investigated the impacts of these differences to determine how each factor can influence search performance. Knowledge gained from the scientific exploration of search can be applied to field searches but only when considering and controlling for the differences between lab and field.

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Year:  2012        PMID: 23437633     DOI: 10.1007/978-1-4614-4794-8_7

Source DB:  PubMed          Journal:  Nebr Symp Motiv        ISSN: 0146-7875


  6 in total

1.  Improvement in visual search with practice: mapping learning-related changes in neurocognitive stages of processing.

Authors:  Kait Clark; L Gregory Appelbaum; Berry van den Berg; Stephen R Mitroff; Marty G Woldorff
Journal:  J Neurosci       Date:  2015-04-01       Impact factor: 6.167

2.  Real-World Visual Experience Alters Baseline Brain Activity in the Resting State: A Longitudinal Study Using Expertise Model of Radiologists.

Authors:  Jiaxi Su; Xiaoyan Zhang; Ziyuan Zhang; Hongmei Wang; Jia Wu; Guangming Shi; Chenwang Jin; Minghao Dong
Journal:  Front Neurosci       Date:  2022-05-25       Impact factor: 5.152

3.  Individual differences predict low prevalence visual search performance.

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

Review 4.  Visual Illusions in Radiology: Untrue Perceptions in Medical Images and Their Implications for Diagnostic Accuracy.

Authors:  Robert G Alexander; Fahd Yazdanie; Stephen Waite; Zeshan A Chaudhry; Srinivas Kolla; Stephen L Macknik; Susana Martinez-Conde
Journal:  Front Neurosci       Date:  2021-06-11       Impact factor: 5.152

5.  The Efficiency of a Visual Skills Training Program on Visual Search Performance.

Authors:  Justyna Krzepota; Teresa Zwierko; Lidia Puchalska-Niedbał; Mikołaj Markiewicz; Beata Florkiewicz; Wojciech Lubiński
Journal:  J Hum Kinet       Date:  2015-07-10       Impact factor: 2.193

6.  Detecting Bombs in X-Ray Images of Hold Baggage: 2D Versus 3D Imaging.

Authors:  Nicole Hättenschwiler; Marcia Mendes; Adrian Schwaninger
Journal:  Hum Factors       Date:  2018-09-24       Impact factor: 2.888

  6 in total

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