Henning Tiedemann

The paper investigates how humans classify novel objects by combining component features, showing that a Bayesian classifier can predict classification performance based on part labels.

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 humans can generate novel variations of abstract shapes from a single exemplar by preserving key features and modifying parts while maintaining structure.

The paper develops a model called ShapeComp that predicts human shape similarity judgments with high accuracy using shape features from animal silhouettes.