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Michael Mack, Thomas Palmeri; The Speed of Categorization: A Priority for People?. Journal of Vision 2010;10(7):988. doi: 10.1167/10.7.988.
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Objects are typically categorized fastest at the basic level (“dog”) relative to more superordinate (“animal”) or subordinate (“labrador retriever”) levels (Rosch et al., 1976). A traditional explanation for this basic-level advantage is that an initial stage of processing first categorizes objects at the basic level (Grill-Spector & Kanwisher, 2005; Jolicoeur, Gluck, & Kosslyn, 1984), but this has been challenged by more recent findings (e.g., Bowers & Jones, 2008; Mace et al., 2009; Mack et al., 2008, 2009; Rogers & Patterson, 2007). In the current study, we explored whether there is temporal priority in processing people by measuring the time course of categorization and evaluating behavioral data using a computational model of perceptual decision making (Ratcliff, 1978). We contrasted speeded categorization of people versus speeded categorization of dogs, manipulating the similarity between the targets and distractors (similar distractors were other animals and dissimilar distractors were nonliving objects) and the homogeneity of the set of distractors (two versus ten object categories). Participants were more accurate and faster for both people and dogs when distractors were dissimilar to the targets and the homogeneity of distractors did not have an effect on performance. But critically, we found a temporal advantage for categorizing people both in overall reaction times and in measures of minimal processing time for successful categorization. Not only were people categorized faster than dogs, they were also categorized earlier. Model predictions suggested that a temporal advantage for categorizing people arises from both a priority in perceptual encoding and a faster accumulation of evidence for a decision. The current study significantly extends recent work by further characterizing the time course of categorization at different levels and for different kinds of objects and investigating the underlying mechanisms within a computational framework.
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