September 2015
Volume 15, Issue 12
Vision Sciences Society Annual Meeting Abstract  |   September 2015
Long-term visual search: Examining trial-by-trial learning over extended visual search experiences
Author Affiliations
  • Justin Ericson
    Center for Cognitive Neuroscience, Duke University
  • Adam Biggs
    Center for Cognitive Neuroscience, Duke University
  • Jonathan Winkle
    Center for Cognitive Neuroscience, Duke University
  • Christina Gancayco
    Center for Cognitive Neuroscience, Duke University
  • Stephen Mitroff
    Center for Cognitive Neuroscience, Duke University
Journal of Vision September 2015, Vol.15, 1108. doi:
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      Justin Ericson, Adam Biggs, Jonathan Winkle, Christina Gancayco, Stephen Mitroff; Long-term visual search: Examining trial-by-trial learning over extended visual search experiences. Journal of Vision 2015;15(12):1108. doi:

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

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Airport security personnel search for a large number of prohibited items that vary in size, shape, color, category-membership, and more. This highly varied search set creates challenges for search accuracy, including how searchers are trained in identifying a myriad of potential targets. This challenge has both practical and theoretical implications (i.e., determining how best to obtain high accuracy, and how large memory sets interact with visual search performance, respectively). Recent research on “hybrid visual and memory search” (e.g., Wolfe, 2012) has begun to address such issues, but many questions remain. The current study addressed a difficult problem for traditional laboratory-based research—how does trial-by-trial learning develop over time for a large number of target types? This issue, which we call “long-term visual search,” is key for understanding how reoccurring information in retained in memory so that it can aid future searches. Through the use of “big data” from the mobile application Airport Scanner (Kedlin Co.), it is possible to address such previously intractable questions. Airport Scanner is a game where players serve as an airport security officers looking for prohibited items in simulated bags. The game has over 7 million downloads and provides a powerful tool for psychological research (Mitroff et al., 2014 JEP:HPP). Trial-by-trial learning for multiple different targets was addressed by analyzing data from 50,000 participants. Distinct learning curves for each specific target revealed that accuracy rises asymptotically across trials without deteriorating to initially low starting levels. Additionally, an investigation into the number of to-be-searched-for target items indicated that performance accuracy remained high even as the memorized set size increased. The results suggest that items stored in memory generate their own item-specific template that is reinforced from repeated exposures. These findings offer insight into how novices develop into experts at target detection over the course of training.

Meeting abstract presented at VSS 2015


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