Explaining the Timing of Natural Scene Understanding with a Computational Model of Perceptual Categorization.
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.
Cognitive, Linguistic & Psychological Sciences Department, Brown Institute for Brain Science, Brown University, Providence, Rhode Island, United States of America.
Imri
I
Sofer
Cognitive, Linguistic & Psychological Sciences Department, Brown Institute for Brain Science, Brown University, Providence, Rhode Island, United States of America.
Sébastien M
SM
Crouzet
Cognitive, Linguistic & Psychological Sciences Department, Brown Institute for Brain Science, Brown University, Providence, Rhode Island, United States of America.
Thomas
T
Serre
D000328:Adult; D000465:Algorithms; D019295:Computational Biology; D005260:Female; D006801:Humans; D008297:Male; D008954:Models, Biological; D010364:Pattern Recognition, Visual / Q000502:physiology*; D011930:Reaction Time / Q000502:physiology*; D013647:Task Performance and Analysis; D055815:Young Adult