The Speed of Categorization: A Priority for People?
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".