Curtis L. Baker

The paper investigates contrast modulation perception and finds that chromatic stimuli are more affected by luminance masking noise, suggesting magnocellular pathway involvement.

This paper develops a model combining cortical state signals and visual stimuli to better predict neural responses in the visual cortex, showing improved accuracy for neurons with higher response variability.

The paper develops a convolutional neural network to estimate receptive fields of simple and complex visual cortex cells from natural image responses, revealing a continuum of simple/complex behavior and lower reliability in complex-like cells.

The paper investigates why V1 neurons respond more strongly to dark stimuli by showing slower inhibition to dark stimuli, not stronger excitation.

The paper evaluates human psychophysical performance with contrast modulation stimuli to investigate nonlinear retinal Y-cell contributions to visual perception.

The paper investigates human perception of texture regularity using conjoint measurements and a wavelet response-distribution model, finding that edge-to-edge distances and wavelet skew across orientations contribute to perceived regularity.

This study investigates how humans segment luminance texture boundaries and proposes a two-stage model to explain performance in the presence of interfering luminance step boundaries.

This paper introduces a machine learning framework to model human perception of second-order visual stimuli and finds humans use region-based and edge-based strategies for orientation tasks.