Modeling second-order boundary perception: A machine learning approach
Fig 7
Analysis of model accuracy for Experiment 1-VAR.
(a) Plots of model vs. observer performance (proportion correct), averaged across observers (left sub-panels, circles, N = 7) or test folds (right sub-panels, solid symbols, N = 28). Observer and model performance were compared on a set of novel test stimuli (N = 500) not used for model estimation or hyper-parameter optimization. Top: Deterministic model with AVG (average) downsampling (AVG-DET). Correlation coefficients (r values) are color coded for each choice of Bayesian prior (red: ridge; green: ridge + smooth). Middle: Stochastic model with AVG downsampling (AVG-STO). Bottom: Deterministic model with downsampling implemented with subsampling (SUB-DET). (b) Difference between observer and model performance for each individual test fold (4 folds per observer) for all models shown in (a). Lines show 95% confidence intervals of the difference (binomial proportion difference test). Colors indicate different choices of Bayesian prior (red: ridge; green: ridge + smooth).