David H. Brainard

This paper investigates the relationship between detection and localization in human vision, showing that localization is optimized by specific eye movement scales while detection remains robust.

This study characterizes human color discrimination thresholds using a novel psychophysical model and adaptive trial placement, achieving comprehensive results with fewer trials.

The paper introduces a Bayesian adaptive method to estimate individual sensory representations by optimizing stimulus distortions based on perceptual judgments.

This study shows tree shrews can recognize objects despite lacking a fovea, using computational models and behavioral tests to demonstrate transformation-tolerant object recognition.

The paper develops a computational model of midget retinal ganglion cell receptive fields that simulates physiological experiments and explores how their organization affects visual performance.

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.

This paper models fixational eye movements in a theoretical framework to analyze their impact on visual information and adaptation in human vision.

The paper measures temporal dynamics of human color processing using a tracking task and finds similar elliptical response contours for detection and tracking, but reduced sensitivity to S-cone signals in tracking.

The paper shows that orthogonal neural representations in V4 allow stable perception of object position despite irrelevant background features in natural scenes.

This paper introduces a computational method to estimate human cone fundamentals using only color matching data from dichromatic observers, without requiring trichromatic data or knowledge of primary lights.

The paper studies foveolar retinal ganglion cells in Macaca fascicularis using adaptive optics and calcium imaging to analyze their chromatic responses and spatial transfer functions.

The paper measures how background reflectance variation affects lightness discrimination thresholds and finds that external noise has a modest effect compared to internal noise.

This paper develops a Bayesian image reconstruction framework to model initial visual encoding, analyzing information loss and perceptual effects in human vision.

This study investigates whether melanopsin stimulation affects sensitivity to luminance flicker and finds no significant effect.

The paper assesses the role of ipRGCs and cones in reflexive eye closure as a measure of photophobia in migraine, finding enhanced responses in MwA compared to other groups.

The paper develops a quadratic color model (QCM) to explain human V1 BOLD fMRI responses to chromatic direction and contrast variations, finding it matches a GLM with fewer parameters.

The paper models how fixational eye movements and phototransduction affect spatial contrast sensitivity in a computational observer, bringing its sensitivity closer to human levels.

This study investigates how melanopsin and cone signals contribute to photophobia in migraine and finds that migraineurs amplify ipRGC signals specifically for discomfort, not pupil responses.

The paper introduces ISET3d, a software tool that simulates 3D image formation through human optics models, enabling analysis of retinal irradiance and its impact on visual perception.

This study compares human ability to discriminate illumination with a computational model of cone isomerizations, identifying aspects of performance that require further explanation.

This study quantifies how color and material influence object selection using a novel observer model and finds large individual differences in the relative importance of these properties.

The paper develops a computational observer model to simulate human spatial contrast sensitivity, showing how cone mosaic structure, optics, and inference engines affect performance.

The paper investigates how scene variability affects the estimation of object luminance reflectance using supervised statistical learning on simulated cone photoreceptor responses from naturalistic images.

This study measures pupil responses to melanopsin-directed contrast pulses and finds they are reliable and insensitive to background radiance changes.

This paper presents a computational framework to simulate visual perception with a retinal prosthesis and assesses how learning might improve inferred visual perception.

The paper investigates how neuronal populations in monkey V1 and V4 represent lightness perception through context-dependent stimuli and finds that while luminance and context are represented, their interaction remains unclear.

The paper investigates how optical aberrations and fixational eye movements affect spatial summation in the human fovea, finding that post-receptoral neural pooling limits summation.

This paper reviews methods and models for understanding how perceived color and material are used in natural tasks, focusing on computational observer models and selection-based approaches.

The paper investigates the perceptual effects of melanopsin-directed stimulation and finds a distinct cortical response and conscious percept.