Modeling second-order boundary perception: A machine learning approach
Fig 3
Estimated second-stage filter weights for two representative observers in Experiment 1 (orientation identification).
(a) Second-stage filter weights learned by the model for representative observer AMA in Experiment 1-VAR (varying modulation depth) for two different priors (left: ridge; right: ridge + smooth) with 16x16 AVG downsampling. Top panels show the 2-D filter weights (averaged over 4 training folds) and bottom panels show these 2-D weights collapsed into 1-D profiles (black dashed lines) by averaging along the matrix diagonals (left-oblique) or anti-diagonals (right-oblique). Thick lines (red: ridge; green: ridge + smooth) denote averages over 30 resampled bootstrapped training sets, and thin dashed lines show +/- 1 SEM. (b) Same as (a) but for observer JJF in Experiment 1-FIX (fixed, near-threshold modulation depth). (c) Results for ideal observer for Experiment 1-VAR. Organization as in (a), (b) except thick black lines denote averages over 4 training folds and thin dashed lines show fits to individual folds.