Biology subjects

Smithson, H. E.

Publications and source records attributed to Smithson, H. E..

15 recordsLinked to original sources

bioRxiv2026

Active manipulation of surface orientation under directional illumination improves color discrimination

Two surfaces that appear different colors under one illumination might appear the same color under another illumination. This phenomenon is known as illuminant metamerism and is thought to limit surface color discrimination. However, real-world lighting environments are not directionally uniform. In theory, surfaces that appear the same color at one tilt might not appear the same color at another tilt where they sample a different illumination spectrum. We tested whether human observers could use this directional variation to improve surface color discrimination. Our results confirm that human color discrimination is improved when observers are able to tilt surfaces to sample directional lighting. Observer sampling motions were spatiotemporally structured and appeared to be directed towards the general lighting geometry rather than systematically maximizing color discriminating information for specific surface pairs.

neuroscience↗

bioRxiv2026

Identification of surface colors across environmental illuminations

When objects are embedded in a complex lighting environment, the illumination they receive depends on their geometric relationship to the environment. We measured performance in an odd-one-out color constancy task, with objects rendered in complex lighting environments. Observers identified the odd object among four objects, with two rendered under each of two lighting environments, sampled from a wide range of object orientations and camera positions. Constancy was high. The presence of a surrounding context increased accuracy. Gloss provided no measurable benefit. We also tested a condition in which the global color impression provided misleading information about the lighting environment, but this impaired performance only slightly. These findings suggest that human color constancy remains robust even under complex lighting environments, contrary to predictions of many simple models of illuminant discounting.

neuroscience↗

bioRxiv2026

The interplay between detection and localization in human vision

Detection and localization are fundamental but distinct tasks of sensory systems: detecting a signal requires accumulating evidence for its presence, whereas localizing it requires extracting spatial information. How active sampling should be organized when both tasks must be performed simultaneously remains unclear. Here, using an analytically tractable model of early visual processing and Bayesian ideal-observer inference, we establish a direct relation between the two tasks: localizing a stimulus is equivalent to detecting its spatial gradient. Consequently, detection and localization favor fundamentally different sampling dynamics when the stimulus size exceeds the effective blur scale of the visual system. Applied to fixational eye movements (FEMs), which continually translate stationary visual stimuli across the adapting retina, this distinction yields two competing optimal movement scales. Localization is optimized when the eye moves approximately one retinal blur length during the adaptation time, whereas detection is optimized by motion on the scale of the stimulus size. The scale of physiological FEMs lies near the predicted localization optimum, while stimulus detection remains comparatively robust to variations in eye motion. Our model recovers established laws of temporal and spatial summation while predicting additional scaling regimes arising from FEMs. We propose an experimental paradigm that tests these predictions by measuring localization at matched detectability, eliminating the unknown internal-noise amplitude. Our results reveal a fundamental trade-off between acquiring evidence for the presence and position of spatially extended signals and suggest that human fixational eye movements preferentially support spatial localization.

neuroscience↗

bioRxiv2026

When the brightest is not the best: illuminant estimation from the geometry of specular highlights

Colour constancy supports stable object-colour perception across changes in illumination. Illuminant colour can be inferred from white surfaces or specular highlights, and many models adopt a "brightest is white" heuristic to identify illuminant colour. We tested an alternative: observers use structured changes in the proximal image to locate highlights even when they are not the brightest elements. Using computer-rendered scenes, we manipulated the reliability of two cues--brightest element and highlight geometry. Each scene showed a single textured sphere lit by multiple point lights with identical spectra. The sphere had uniform spectral reflectance, while a noise texture attenuated the reflectance by a variable scale factor. We tested three specularity levels (matte, low, mid). Observers viewed a 1.5 s animation and judged whether chromatic changes reflected an illuminant or material change. Performance for matte surfaces was near chance but improved sharply with increasing specularity. Observers outperformed an ideal observer relying solely on the brightest element. When highlights landed on darker texture regions, performance improved further, contrary to the brightest-element prediction. Phase scrambling, which disrupted highlight geometry while preserving global statistics, substantially reduced performance. Overall, observers do not rely solely on the brightest element; they exploit diffuse-specular regularities to resolve surface-illuminant ambiguity.

neuroscience↗

bioRxiv2025

Information, Movement and Adaptation in Human Vision

Our eyes are never still. Even when fixating, they exhibit small, jittery motions. While it has long been argued that these fixational eye movements (FEMs) aid the acquisition of visual information, a complete theoretical description of their impact on the information available in the early visual system has been lacking. Here we build FEMs into a minimal theoretical model of the early visual response, including the critical process of adaptation (a fading response to a fixed image). We establish the effect of FEMs on the mutual information between a visual stimulus and this response. Our approach identifies the key dimensionless parameters that characterise the effect of fixational eye movements, and reveals the regimes in which this effect can be beneficial, detrimental or negligible. Taking parameter values appropriate for human vision, we show that the spatio-temporal couplings induced by fixational eye movements can explain the qualitative features of the human contrast sensitivity function as well as classic experiments on temporal integration. To our knowledge, the consideration of fixational eye movements in this latter context is novel, and our results suggest the need for future experiments to determine the mechanisms by which spatial and temporal responses are coupled in the human visual system.

neuroscience↗

bioRxiv2025

Human gloss perception reproduced by tiny neural networks

A key goal of visual neuroscience is to explain how our brains infer object properties like colour, curvature, or gloss. Here, we used machine learning to identify computations underlying human gloss judgments from object images--traditionally considered a challenging inference. Thousands of object images of varied shapes were rendered with a Ward reflectance model under diverse lighting and viewpoints, and we crowdsourced gloss estimates for each image. Curiously, observers judgments were highly consistent with one another, yet systematically deviated from reality. We compared these responses with neural networks trained either to estimate physical reflectance ("ground-truth networks") or to reproduce human judgments ("human-like networks"). By reducing network size, we identified the minimum computations needed for each objective. While estimating physical reflectance required deep networks, shallow networks--with as few as three layers--accurately replicated human judgments. Remarkably, even a miniscule network with just a single filter could predict human judgments better than the best ground-truth network. The human-like networks also captured known gloss illusions outside the training range. These results suggest human gloss judgments rely on simpler, more general-purpose computations than previously thought, and demonstrate the power of using tiny but interpretable neural networks to uncover functional computations in human brains.

neuroscience↗

bioRxiv2024

How many colours can you see? Real environmental lighting increases discriminability of surface colours

Color supports object identification. However, two objects that differ in color under one light can appear indiscriminable under a second light. This phenomenon, known as illuminant metamerism, underlies the difficulty faced by consumers of selecting matching fabric or paint colors in a store only to find that they appear not to match under home lighting. The frequency of illuminant metamerism has been evaluated only under single, uniform illuminants. However, in real world conditions, the spectral content of light falling on an object varies with direction (Morimoto et al. 2019), meaning that a surface will sample different spectra depending on its angle within the environment. Here we used computer-graphics techniques to simulate a pair of planar surfaces placed under newly measured hyperspectral illumination maps that quantify the directional variability of real-world lighting environments. We counted the instances of illuminant metamerism that can be solved simply by viewing surfaces tilted to a different direction. Results show that most instances of illuminant metamerism can in theory be resolved for both trichromatic and dichromatic observers. Color deficient observers benefit more than trichromats implying that the directional variability allows the recovery of the missing dimension in their colour vision systems. This study adds a new perspective to the classic trichromatic theory of human vision and emphasizes the importance of carefully considering the environments in which biological vision operates in daily life. It is striking that the physical directional variability available in natural lighting environments substantially mitigates the biological limitations of trichromacy or dichromacy.

neuroscience↗

bioRxiv2022

Color and gloss constancy under diverse lighting environments

When we look at an object, we simultaneously see how glossy or matte it is, how light or dark, and what color. Yet, at each point on the objects surface, both diffuse and specular reflections are mixed in different proportions, resulting in substantial spatial chromatic and luminance variations. To further complicate matters, this pattern changes radically when the object is viewed under different lighting conditions. The purpose of this study was to simultaneously measure our ability to judge color and gloss using an image set capturing diverse object and illuminant properties. Participants adjusted the hue, lightness, chroma, and specular reflectance of a reference object so that it appeared to be made of the same material as a test object. Critically, the two objects were presented under different lighting environments. We found that hue matches were highly accurate, except for under a chromatically atypical illuminant. Chroma and lightness constancy were generally poor, but these failures correlated well with simple image statistics. Gloss constancy was particularly poor, and these failures were only partially explained by reflection contrast. Importantly, across all measures, participants were highly consistent with one another in their deviations from constancy. Although color and gloss constancy hold well in simple conditions, the variety of lighting and shape in the real world presents significant challenges to our visual systems ability to judge intrinsic material properties.

neuroscience↗

bioRxiv2022

Modelling surface color discrimination under different lighting environments using image chromatic statistics and convolutional neural networks

We modeled discrimination thresholds for object colors under different lighting environments [1]. Firstly we built models based on chromatic statistics, testing 60 models in total. Secondly we trained convolutional neural networks (CNNs), using 160,280 images labeled either by the ground-truth or by human responses. No single chromatic statistics model was sufficient to describe human discrimination thresholds across conditions, while human-response-trained CNNs nearly perfectly predicted human thresholds. Guided by region-of-interest analysis of the network, we modified the chromatic statistics models to use only the lower regions of the objects, which substantially improved performance.

neuroscience↗

bioRxiv2021

Hyperspectral characterisation of natural illumination in woodland and forest environments

Light in nature is complex and dynamic, and varies along spectrum, space, direction, and time. While both spectrally resolved measurements and spatially resolved measurements are widely available, spectrally and spatially resolved measurements are technologically more challenging. Here, we present a portable imaging system using off-the-shelf components to capture the full spherical light environment in a spectrally and spatially resolved fashion. The method relies on imaging the 4{pi}-steradian light field reflected from a mirrored chrome sphere using a commercial hyperspectral camera (400-1000 nm) from multiple directions and an image-processing pipeline for extraction of the mirror sphere, removal of saturated pixels, correction of specular reflectance of the sphere, promotion to a high dynamic range, correction of misalignment of images, correction of intensity compression, erasure of the imaging system, unwrapping of the spherical images, filling-in blank regions, and stitching images collected from different angles. We applied our method to Wytham Woods, an ancient semi-natural woodland near Oxford, UK. We acquired a total of 168 images in two sites with low and high abundance of ash, leading to differences in canopy, leading to a total 14 hyperspectral light probes. Our image-processing pipeline corrected small (<3 {degrees}) field-based misalignment adequately. Our novel hyperspectral imaging method is adapted for field conditions and opens up novel opportunities for capturing the complex and dynamic nature of the light environment.

systems biology↗

bioRxiv2021

Beyond colour gamuts: Novel metrics for thereproduction of photoreceptor signals

Colour gamuts describe the chromaticity reproduction capabilities of a display, i.e. its ability to reproduce the relative cone excitations from real-world radiance spectra. While the cones dominate "canonical" visual function (i.e. perception of colour, space, and motion) under photopic light levels, they are not the only photoreceptors in the human retina. Rods and melanopsin-containing intrinsically photosensitive retinal ganglion cells (ipRGCs) also respond to light and contribute to both visual and non-visual light responses, including circadian rhythms, sleep-wake control, mood, pupil size, and alertness. Three-primary display technologies, with their focus on reproducing colour, are not designed to reproduce the rod and melanopsin excitations. Moreover, conventional display metrics used to characterize three-primary displays fail to describe the displays ability (or inability) to reproduce rod and melanopsin excitations, and thus do not capture the displays ability to reproduce the full human physiological response to light. In this paper, three novel physiologically relevant metrics are proposed for quantifying the reproduction and distortion of the photoreceptor signals by visual displays. A novel equal-luminance photoreceptor excitation diagram is proposed, extending the well-known MacLeod-Boynton chromaticity diagram, to allow visualizations of the five-dimensional photoreceptor signal space in a three-dimensional projection.

neuroscience↗

bioRxiv2021

ERICA: Emulated Retinal Image CApture - A tool for testing, training and validating retinal image processing methods

High resolution retinal imaging systems, such as adaptive optics scanning laser ophthalmoscopes (AOSLO), are increasingly being used for clinical and fundamental studies in neuroscience. These systems offer unprecedented spatial and temporal resolution of retinal structures in vivo. However, a major challenge is the development of robust and automated methods for processing and analysing these images. We present ERICA (Emulated Retinal Image CApture), a simulation tool that generates realistic synthetic images of the human cone mosaic, mimicking images that would be captured by an AOSLO, with specified image quality and with corresponding ground truth data. The simulation includes a self-organising mosaic of photoreceptors, the eye movements an observer might make during image capture, and data capture through a real system incorporating diffraction, residual optical aberrations and noise. The retinal photoreceptor mosaics generated by ERICA have a similar packing geometry to human retina, as determined by expert labelling of AOSLO images of real eyes. In the current implementation ERICA outputs convincingly realistic en face images of the cone photoreceptor mosaic but extensions to other imaging modalities and structures are also discussed. These images and associated ground-truth data can be used to develop, test and validate image processing and analysis algorithms or to train and validate machine learning approaches. The use of synthetic images has the advantage that neither access to an imaging system, nor to human participants is necessary for development.

bioengineering↗

bioRxiv2021

Modeling metallicity: low level visual features support robust material perception

The human visual system is able to rapidly and accurately infer the material properties of objects and surfaces in the world. Yet an inverse optics approach--estimating the bi-directional reflectance distribution function of a surface, given its geometry and environment, and relating this to the optical properties of materials--is both intractable and computationally unaffordable. Rather, previous studies have found that the visual system may exploit low-level spatio-chromatic statistics as heuristics for material judgment. Here, we present results from psychophysics and modelling that supports the use of image statistics heuristics in the judgement of metallicity--the quality of appearance that suggests an object is made from metal. Using computer graphics, we generated stimuli that varied along two physical dimensions: the smoothness of a metal object, and the evenness of its transparent coating. This allowed for the manipulation of low-level image statistics, whilst ensuring that each stimulus was a naturalistic, physically plausible image. A conjoint-measurement task decoupled the contributions of these dimensions to the perception of metallicity. Low-level image features, as represented in the activations of oriented linear filters at different spatial scales, were found to correlate with the dimensions of the stimulus space, and decision-making models using these activations replicated observer performance in judging metal smoothness, coating bumpiness, and metallicity. Importantly, the performance of these models did not deteriorate when objects were rotated within their simulated scene, with corresponding changes in image properties. We therefore conclude that low-level image features may provide reliable cues for the robust perception of metallicity.

neuroscience↗

bioRxiv2019

Hyperspectral environmental illumination maps: Characterizing directional spectral variation in natural environments

Objects placed in real-world scenes receive incident light from every direction, and the spectral content of this light may vary from one direction to another. In computer graphics, environmental illumination is approximated using maps that specify illumination at a point as a function of incident angle. However, to-date, existing public databases of environmental illumination maps specify only three colour channels (RGB). We have captured a new set of 12 environmental illumination maps (eight outdoor scenes; four indoor scenes) using a hyperspectral imaging system with 33 spectral channels. The data reveal a striking directional variation of spectral distribution of lighting in natural environments. We discuss limitations of using daylight models to describe natural environmental illumination.

neuroscience↗

bioRxiv2018

Self-organising coordinate transformation with peaked and monotonic gain modulation in the primate dorsal visual pathway

We study a self-organising neural network model of how visual representations in the primate dorsal visual pathway are transformed from an eye-centred to head-centred frame of reference. The model has previously been shown to robustly develop head-centred output neurons with a standard trace learning rule [1], but only under limited conditions. Specifically it fails when incorporating visual input neurons with monotonic gain modulation by eye-position. Since eye-centred neurons with monotonic gain modulation are so common in the dorsal visual pathway, it is an important challenge to show how efferent synaptic connections from these neurons may self-organise to produce head-centred responses in a subpopulation of postsynaptic neurons. We show for the first time how a variety of modified, yet still biologically plausible, versions of the standard trace learning rule enable the model to perform a coordinate transformation from eye-centred to head-centred reference frames when the visual input neurons have monotonic gain modulation by eye-position.

neuroscience↗