Detecting changes between real-world objects using spatiochromatic filters |
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Authors: | Email author" target="_blank">Gregory?J?ZelinskyEmail author |
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Institution: | (1) George Mason University, Fairfax, Virginia;(2) Rowan University, Glassboro, New Jersey;(3) Department of Psychology, Louisiana State University, 236 Audubon Hall, 70803 Baton Rouge, LA |
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Abstract: | A behavioral and computational treatment of change detection is reported. The behavioral task was to judge whether a single
object substitution change occurred between two “flickering” 9-object scenes. Detection performance was found to vary with
the similarity of the changing objects; object changes violating orientation and category yielded the fastest and most accurate
detection responses. To account for these data, theBOLAR model was developed, which uses color, orientation, and scale selective filters to compute the visual dissimilarity between
the pre- and postchange objects from the behavioral study. Relating the magnitude of the BOLAR difference signals to change
detection performance revealed that object pairs estimated as visually least similar were the same object pairs most easily
detected by observers. The BOLAR model advances change detection theory by (1) demonstrating that the visual similarity between
the change patterns can account for much of the variability in change detection behavior, and (2) providing a computational
technique for quantifying these visual similarity relationships for real-world objects. |
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Keywords: | |
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