Modeling second-order boundary perception: A machine learning approach
Fig 10
Simulated ground-truth observers (left columns) and filter shapes recovered (right columns) from simulated datasets generated by these observers.
(a) Ideal observers implementing various sub-optimal spatial filtering models for Experiment 1. Top row: Simulated observer monitors two adjacent “pizza slice” shaped regions (2-slice). Second row: Simulated observer monitors three adjacent regions (3-slice). Third row: Simulated observer only monitors one boundary (1-filter). Bottom row: Simulated observer randomly monitors 1 of 4 pairs of informative adjacent pizza slices (2-slice—random). (b) Ideal observer implementing a spatial filtering model comprised of two perceptual filters, each monitoring one potential boundary (2-filter). The recovered filters (right) are most similar to those observed in our results (Fig 3).