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Alec Scharff, John Palmer; Is object recognition serial or parallel?. Journal of Vision 2010;10(7):228. doi: 10.1167/10.7.228.
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© ARVO (1962-2015); The Authors (2016-present)
Can one recognize multiple objects in parallel, as if they were simple features? Or does one “read” objects one-by-one, as if they were words? We consider three models of divided attention: a standard serial model, an unlimited-capacity, parallel model, and a fixed-capacity, parallel model. The standard serial model analyzes objects one-by-one. The unlimited-capacity, parallel model analyzes objects independently and simultaneously. The fixed-capacity, parallel model analyzes objects simultaneously, but acquires information at a fixed rate. Methods: For stimuli, we used images of similar animal categories (e.g., bear, wolf, fox). Observers searched a brief display of animal images for target categories. This set of stimuli minimized low-level differences between categories, such as object textures and spatial-frequency spectra. For the experiment, we used several variations on the simultaneous-sequential paradigm to distinguish among the three models. Previously, this paradigm has shown that simple features are processed by an unlimited-capacity, parallel model and words are processed by a standard serial model. Results: Current results for objects favor the fixed-capacity, parallel model over the standard serial model. And, more decisively, the results reject the unlimited-capacity, parallel model.
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