Category structures used in the rule-based (A) and information-integration conditions (B). Solid lines denote the optimal decision bounds, and the open squares, filled squares, open triangles, and filled triangles denote stimuli from categories A–D, respectively. Each category was defined as a bivariate normal distribution along the two stimulus dimensions with mean vectors *μ*_{A}, *μ*_{B}, *μ*_{C}, and *μ*_{D} (in length-orientation stimulus space) and common variance–covariance matrix *Σ: μ*_{A} = [72 100]′, *μ*_{B} = [100 128]′, *μ*_{C} = [100 72]′, *μ*_{D} = [128 100]′ and *Σ* = *Σ*_{A} = *Σ*_{B} = *Σ*_{C} = *Σ*_{D} = [100 0; 0 100]. Optimal accuracy was 95%. Twenty-five random samples were drawn from each of these category distributions for a total of 100 unique stimuli. Each sample was linearly transformed so that the sample mean vector and sample variance–covariance matrix exactly equaled the population mean vector and variance–covariance matrix. Each random sample (*x*, *y*) was converted to a stimulus by deriving the length in pixels *l* = *x*, and the orientation (in degrees counterclockwise from horizontal) as *o* = *yπ*/600. These scaling factors were chosen to roughly equate the salience of each dimension. The resulting 100 stimuli were randomized separately for each participant in each block.

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