Table 1.

Summary of categorization time predictions using visual search data. Notes: For each image, we calculated the within-category similarity (CRT) and between-category similarity (NRT) using visual search data. We then fit a model that uses a linear combination of NRT and CRT to account for categorization times. The resulting correlations and model coefficients are depicted above. Asterisks represent the statistical significance of the correlation (*p < 0.05, **p < 0.005, ***p < 0.0005, ****p < 0.00005).

Task	Item type	Correlation between categorization & search	Model coefficients RT = a × NRT + b × CRT + c
			a	b	c
Animal/dog/Labrador in canonical views (Experiment 1)	All items	0.85****	0.16	−0.04	0.61
	Category	0.91****
	Noncategory	0.85****
Animals in oblique and profile views (Experiment 2)	All items	0.72****
	Category	0.52*	0.08	−0.02	0.62
	Noncategory	0.56*	0.06	0.02	0.61
Vehicles (Experiment 3)	All items	0.71****
	Category	0.43*	0.17	−0.05	0.63
	Noncategory	0.48*	0.10	0.11	0.59
Tools Experiment 4)	All items	0.62****
	Category	0.70***	0.58	−0.13	0.44
	Noncategory	0.45*	0.20	−0.07	0.73