Three example models, signal detection theory with the latency-distance hypothesis, stochastic general recognition theory, and a random walk model of exemplar classification, are interrogated to determine what type of interaction they predict factors will yield in a hypothetical factorial (choice) reaction time experiment in which the 'empirical' factors' effects are manifest as parameter changes.
Pictures from the following categories were presented in a random order: Crisps (8 exemplars); chocolate (8 exemplars); pasta (8 exemplars); pancakes (8 exemplars); non-food items (household/garden objects and clothes e.g. clock, rake, gloves; 32 exemplars).
This makes random exploration for such exemplars inefficient.
Pictures from the following categories were presented in a random order: Food (including 8 exemplars of crisps and 8 exemplars of other foods; pasta, pizza, cakes, pancakes), or non-food items (32 exemplars; household/garden objects and clothes e.g. clock, rake, gloves).
In the model, expression type (n = 4) was a fixed factor and subject (n = 69) was a random factor within which the individual exemplars of each expression were nested.
Subject was included as a random intercept and the specific background exemplar was also included as a random slope.
Patch correspondences between an input photo and multiple exemplars are established using a Markov Random Field (MRF), which enables accurate local energy transfer via Laplacian stacks.
Thus, rather than using search engines to find category exemplars, we crawled the site DMOZ.org for 1795 random URLs from our selected categories.
Within one block, all stimuli (8 exemplars * 9 stimulus categories) were presented 4 times in random order, summing up to a total of 128 trials for each stimulus condition.
This collection of columns by George F. Will, each an exemplar of considered judgment and nuanced expression, may have its greatest impact when opened at random and read intermittently.
