July 2013
Volume 13, Issue 9
Free
Vision Sciences Society Annual Meeting Abstract  |   July 2013
Visual Expertise: Insights Gained by Comparing Professional Populations
Author Affiliations
  • Kait Clark
    Duke University, Center for Cognitive Neuroscience, Department of Psychology & Neuroscience
  • Adam T. Biggs
    Duke University, Center for Cognitive Neuroscience, Department of Psychology & Neuroscience
  • Elise F. Darling
    Duke University, Center for Cognitive Neuroscience, Department of Psychology & Neuroscience
  • Matthew S. Cain
    Brown University, Department of Cognitive, Linguistic, & Psychological Sciences
  • Tate H. Jackson
    University of North Carolina at Chapel Hill, Department of Orthodontics
  • Ehsan Samei
    Duke University Medical Center, Department of Radiology
  • Jay A. Baker
    Duke University Medical Center, Department of Radiology
  • Stephen R. Mitroff
    Duke University, Center for Cognitive Neuroscience, Department of Psychology & Neuroscience\nUniversity of North Carolina at Chapel Hill, Department of Orthodontics
Journal of Vision July 2013, Vol.13, 300. doi:10.1167/13.9.300
  • Views
  • Share
  • Tools
    • Alerts
      ×
      This feature is available to Subscribers Only
      Sign In or Create an Account ×
    • Get Citation

      Kait Clark, Adam T. Biggs, Elise F. Darling, Matthew S. Cain, Tate H. Jackson, Ehsan Samei, Jay A. Baker, Stephen R. Mitroff; Visual Expertise: Insights Gained by Comparing Professional Populations. Journal of Vision 2013;13(9):300. doi: 10.1167/13.9.300.

      Download citation file:


      © ARVO (1962-2015); The Authors (2016-present)

      ×
  • Supplements
Abstract

Professionals whose careers depend on visual skills typically demonstrate superior performance on career-related tasks; farmers better determine the sex of day-old chicks (Biederman & Shiffrar, 1987), and bank tellers better detect counterfeit currency (Klein, Gadbois, & Christie, 2004). Perceptual expertise has implications for learning and malleability, but the interpretation of expertise benefits is not straightforward. Complications arise, in part, because standard methodologies typically compare professionals to laypersons, raising concerns about confounding differences (e.g., motivation, speed/accuracy tradeoff, self-selection). Additionally, the mechanisms responsible for improvement may be ambiguous (e.g., enhanced sensory discrimination vs. improved strategies). To account for these issues, we analyzed performance across different professional groups on tasks related and unrelated to their careers. We assessed two groups of visual search experts (radiologists, airport security screeners), one group of facial symmetry experts (orthodontists), and one non-professional group (university participants). As expected, the professional groups demonstrated superior accuracy for career-related tasks (radiologists and airport security screeners for search, orthodontists for facial symmetry), and additional comparisons provided further insight. To evaluate potential differences in motivation, we compared performance on a task unrelated to the professionals’ expertise—temporal order judgment—and found no accuracy differences between professionals and non-professionals. Relatedly, comparisons between professional groups minimized speed/accuracy tradeoff concerns; professionals were slower on all tasks but only showed enhanced accuracy for career-relevant tasks. To elucidate whether benefits arose from self-selection or experience, we compared early- versus late-career individuals within the same profession and found differences related to amount of experience. Finally, we explored the nature of experts’ improvements and found evidence supporting superior top-down strategy selection (search professionals choose more effective strategies) as well as bottom-up stimulus-specific processing (orthodontists revealed enhanced symmetry discrimination for faces only). By comparing different types of experts on multiple tasks, we can better inform the nature of visual expertise.

Meeting abstract presented at VSS 2013

×
×

This PDF is available to Subscribers Only

Sign in or purchase a subscription to access this content. ×

You must be signed into an individual account to use this feature.

×