Filipp Schmidt

This paper develops a non-invasive method for continuous lactate monitoring using NIR spectroscopy and shows that dynamic offset correction improves long-term accuracy.

The paper investigates whether the visual system prioritizes curvature extrema or high-surprisal points in shape representation, finding that high-surprisal points yield better performance in shape matching tasks.

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 anticipatory eye movements to investigate dynamic predictions of material behavior and finds that intuitive expectations influence these predictions even when they conflict with learned information.

This paper compares how humans and generative AI models integrate visual cues in shape completion tasks, finding that humans use both low- and high-level cues while AI models rely primarily on low-level pattern continuation.

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.