Thomas S. A. Wallis

This paper introduces HOPE, a method for efficiently estimating high-dimensional psychometric functions by adaptively selecting stimuli, reducing uncertainty faster than traditional methods.

This study investigates how visual uncertainty in predicted moving trajectories affects causality judgments in launching displays, finding that higher uncertainty increases perceived causality.

The paper trains a deep convolutional neural network to estimate surface reflectance and tests its susceptibility to lightness illusions, finding it performs well compared to classical models.

This study compares how deep neural networks and human observers achieve lightness constancy by estimating albedo and illumination in controlled conditions.

The paper shows that low-level image features predict perceived similarity between naturalistic scene regions, supporting the role of basic visual features in scene organization.

This study shows that visual attention operates in real-world coordinates, with high precision and minimal disruption during eye movements.

The paper shows that semantically inconsistent objects attract more fixations than consistent ones, but contextualized meaning maps fail to capture this relationship.

The paper tests the hypothesis that target detection in natural images is inversely related to spatial similarity with the background, finding that high similarity increases detectability due to phase alignment.

This study compares meaning maps and saliency models in predicting human fixations, finding that deep neural networks outperform meaning maps in accuracy and responsiveness to meaning changes.

The paper shows that real and model-generated images can be distinguished at V2 scaling, challenging the interpretation of Bouma’s Law in crowding.

The paper compares texture models using deep CNN features and finds that while CNN models perform well under foveal inspection, no model perfectly matches all textures across viewing conditions.

The paper investigates how visual crowding affects the ability to detect spatial distortions in peripherally presented letters, finding that crowding impairs distortion detection and alters distortion sensitivity tuning.