Modeling second-order boundary perception: A machine learning approach
Fig 12
Model comparison for Experiment 3, boundary orientation identification with textures composed of two kinds of micropatterns.
(a) Two competing models of FRF architecture fit to psychophysical trial data. Top: Model 1 (“late summation”) assumes that each first-stage orientation channel is analyzed by its own pair of (L/R) second-stage filters and then pooled. Bottom: Model 2 (“early summation”) assumes that each (L/R) second-stage filter integrates over both first-stage channels. (b) Bayesian model comparison making use of all data reveals a strong consistent preference (as measured by the Bayes factor—see text) for Model 2 (“early summation”) for all three observers (blue line and symbols). Thick black line indicates the conventional criterion for a “very strong” preference for Model 2. (c) Bootstrapping analysis, in which model likelihood is evaluated on novel data not used for model training, also reveals a preference for Model 2. Plotted points indicate mean +/- SEM for 50 bootstrapped samples. (d) Difference between predicted and observed proportions where observer chooses “right oblique” for all observers and both models. Negative modulation depths indicate left-oblique stimuli.