Explaining the Timing of Natural Scene Understanding with a Computational Model of Perceptual Categorization

PLoS Computational Biology
Imri SoferThomas Serre

Abstract

Observers can rapidly perform a variety of visual tasks such as categorizing a scene as open, as outdoor, or as a beach. Although we know that different tasks are typically associated with systematic differences in behavioral responses, to date, little is known about the underlying mechanisms. Here, we implemented a single integrated paradigm that links perceptual processes with categorization processes. Using a large image database of natural scenes, we trained machine-learning classifiers to derive quantitative measures of task-specific perceptual discriminability based on the distance between individual images and different categorization boundaries. We showed that the resulting discriminability measure accurately predicts variations in behavioral responses across categorization tasks and stimulus sets. We further used the model to design an experiment, which challenged previous interpretations of the so-called "superordinate advantage." Overall, our study suggests that observed differences in behavioral responses across rapid categorization tasks reflect natural variations in perceptual discriminability.

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Citations

Mar 22, 2016·Wiley Interdisciplinary Reviews. Cognitive Science·Thomas Serre
Jan 4, 2017·Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences·Iris I A GroenChris I Baker
Jul 3, 2020·The European Journal of Neuroscience·Marlene PoncetRamakrishna Chakravarthi
Feb 18, 2021·Journal of Vision·Matt D AndersonWendy J Adams
May 20, 2021·Journal of Vision·Miguel Granja Espírito Santo, Johan Wagemans
Sep 5, 2021·The European Journal of Neuroscience·Amirhossein FarzmahdiReza Ebrahimpour

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