Roland W. Fleming

This study investigates how humans pour liquids into containers, finding that fill level remains consistent despite varying movement patterns and conditions.

The paper shows that humans use specific eye movements to mentally rotate objects and make shape-based decisions.

This study investigates how humans perceive object elasticity using visual cues and proposes a resource-rational model based on motion statistics.

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

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 investigates whether findings from precision grip research apply to unconstrained, multidigit grasping by comparing participant grasp strategies with and without constraints.

The paper shows humans can categorize novel natural shapes based on their biological origins without explicit knowledge of the underlying processes.

The paper shows that a simple optical flow model predicts why certain object viewpoints are perceptually special by analyzing discrimination performance across different angles.

The paper investigates brain networks involved in visually guided grasping during planning and execution using fMRI, revealing distinct neural encoding of grasp axis, size, and object mass in different brain regions.

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.

This paper presents a method to estimate contact regions between hands and objects during grasping using motion capture and hand mesh reconstruction.

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.

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 investigates whether humans prioritize grasp stability or natural grasp configuration during precision grip tasks, finding that stability overrides comfort.

The paper shows humans can judge grasp quality from visual input and improve through visual and haptic feedback.

The paper presents a model that predicts human precision grip grasp locations on 3D objects using behavioral data and five cost functions, showing high accuracy across different conditions.

The paper re-analyzes grasping data to determine whether spatial biases are due to energy minimization or visibility maximization, finding visibility is the primary factor.