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Analytic Guided-Search Model of Human Performance Accuracy in Target- Localization Search TasksCurrent models of human visual search have extended the traditional serial/parallel search dichotomy. Two successful models for predicting human visual search are the Guided Search model and the Signal Detection Theory model. Although these models are inherently different, it has been difficult to compare them because the Guided Search model is designed to predict response time, while Signal Detection Theory models are designed to predict performance accuracy. Moreover, current implementations of the Guided Search model require the use of Monte-Carlo simulations, a method that makes fitting the model's performance quantitatively to human data more computationally time consuming. We have extended the Guided Search model to predict human accuracy in target-localization search tasks. We have also developed analytic expressions that simplify simulation of the model to the evaluation of a small set of equations using only three free parameters. This new implementation and extension of the Guided Search model will enable direct quantitative comparisons with human performance in target-localization search experiments and with the predictions of Signal Detection Theory and other search accuracy models.
Document ID
20010038016
Acquisition Source
Ames Research Center
Document Type
Technical Memorandum (TM)
Authors
Eckstein, Miguel P. (California Univ. Los Angeles, CA United States)
Beutter, Brent R. (San Jose State Univ. CA United States)
Stone, Leland S. (NASA Ames Research Center Moffett Field, CA United States)
Date Acquired
September 7, 2013
Publication Date
April 1, 2000
Subject Category
Behavioral Sciences
Report/Patent Number
NAS 1.15:209594
IH-011
NASA/TM-2000-209594
Report Number: NAS 1.15:209594
Report Number: IH-011
Report Number: NASA/TM-2000-209594
Funding Number(s)
CONTRACT_GRANT: NCC2-1027
PROJECT: RTOP 711-51-12
CONTRACT_GRANT: NASA-50827
PROJECT: RTOP 131-20-30
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.

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