Katherine R. Storrs

This paper shows that tiny neural networks can accurately replicate human gloss perception judgments better than larger networks estimating physical reflectance.

This study measures how well participants can judge color and gloss of objects under different lighting conditions, finding high accuracy in hue but poor performance in chroma, lightness, and gloss.

This study uses visual aftereffects to determine that high-level shape attributes, derived from natural shape statistics, drive perceptual distortions in human vision.

The paper shows that Euclidean distance in a 3D morphable face model predicts human face dissimilarity and identity judgements as well as deep neural networks.

The paper compares nine DNN architectures to explain human inferior temporal cortex representational geometry, finding trained models outperform untrained ones and all architectures achieve similar performance after training and fitting.

This study uses unsupervised learning to model glossy surface images and finds that the resulting representations predict human perception and misperception of gloss.

The paper uses fMRI and representational similarity analysis to show that FFA encodes higher-level face identity information, while OFA encodes low-level image properties.

The paper corrects an error in noise ceiling calculations for human data from a previous study, showing the best model does not significantly fall below the noise ceiling lower bound.