An object’s colour, brightness and pattern are all influenced by its surroundings, and a number of visual phenomena and “illusions” have been discovered that highlight these often dramatic effects. Explanations for these phenomena range from low-level neural mechanisms to high-level processes that incorporate contextual information or prior knowledge. Importantly, few of these phenomena can currently be accounted for when measuring an object’s perceived colour. Here we ask to what extent colour appearance is predicted by a model based on the principle of coding efficiency. The model assumes that the image is encoded by noisy spatio-chromatic filters at one octave separations, which are either circularly symmetrical or oriented. Each spatial band’s lower threshold is set by the contrast sensitivity function, and the dynamic range of the band is a fixed multiple of this threshold, above which the response saturates. Filter outputs are then reweighted to give equal power in each channel for natural images. We demonstrate that the model fits human behavioural performance in psychophysics experiments, and also primate retinal ganglion responses. Next we systematically test the model’s ability to qualitatively predict over 35 brightness and colour phenomena, with almost complete success. This implies that contrary to high-level processing explanations, much of colour appearance is potentially attributable to simple mechanisms evolved for efficient coding of natural images, and is a basis for modelling the vision of humans and other animals.

A central goal of vision science is to understand the principles underlying the perception and neural coding of the complex visual environment of our everyday experience. In the visual cortex, foundational work with artificial stimuli, and more recent work combining natural images and deep convolutional neural networks, have revealed much about the tuning of cortical neurons to specific image features. However, a major limitation of this existing work is its focus on single-neuron response strength to isolated images. First, during natural vision, the inputs to cortical neurons are not isolated but rather embedded in a rich spatial and temporal context. Second, the full structure of population activity—including the substantial trial-to-trial variability that is shared among neurons—determines encoded information and, ultimately, perception. In the first part of this talk, I will argue for a normative approach to study encoding of natural images in primary visual cortex (V1), which combines a detailed understanding of the sensory inputs with a theory of how those inputs should be represented. Specifically, we hypothesize that V1 response structure serves to approximate a probabilistic representation optimized to the statistics of natural visual inputs, and that contextual modulation is an integral aspect of achieving this goal. I will present a concrete computational framework that instantiates this hypothesis, and data recorded using multielectrode arrays in macaque V1 to test its predictions. In the second part, I will discuss how we are leveraging this framework to develop deep probabilistic algorithms for natural image and video segmentation.

To better understand human scene understanding, we extracted features from images using CLIP, a neural network model of visual concept trained with supervision from natural language. We then constructed voxelwise encoding models to explain whole brain responses arising from viewing natural images from the Natural Scenes Dataset (NSD) - a large-scale fMRI dataset collected at 7T. Our results reveal that CLIP, as compared to convolution based image classification models such as ResNet or AlexNet, as well as language models such as BERT, gives rise to representations that enable better prediction performance - up to a 0.86 correlation with test data and an r-square of 0.75 - in higher-level visual cortex in humans. Moreover, CLIP representations explain distinctly unique variance in these higher-level visual areas as compared to models trained with only images or text. Control experiments show that the improvement in prediction observed with CLIP is not due to architectural differences (transformer vs. convolution) or to the encoding of image captions per se (vs. single object labels). Together our results indicate that CLIP and, more generally, multimodal models trained jointly on images and text, may serve as better candidate models of representation in human higher-level visual cortex. The bridge between language and vision provided by jointly trained models such as CLIP also opens up new and more semantically-rich ways of interpreting the visual brain.

Animal sensory systems are optimally adapted to those features typically encountered in natural surrounds, thus allowing neurons that have a limited bandwidth to encode almost impossibly large input ranges. Importantly, natural scenes are not random, and peripheral visual systems have therefore evolved to reduce the predictable redundancy. The vertebrate visual cortex is also optimally tuned to the spatial statistics of natural scenes, but much less is known about how the insect brain responds to these. We are redressing this deficiency using several techniques. Olga Dyakova uses exquisite image manipulation to give natural images unnatural image statistics, or vice versa. Marissa Holden then uses these images as stimuli in electrophysiological recordings of neurons in the fly optic lobes, to see how the brain codes for the statistics typically encountered in natural scenes, and Olga Dyakova measures the behavioral optomotor response on our trackball set-up.

Current computational models of visual salience accurately predict the distribution of fixations on isolated visual stimuli. It is not known, however, whether the global salience of a stimulus, that is its effectiveness in the competition for attention with other stimuli, is a function of the local salience or an independent measure. Further, do task and familiarity with the competing images influence eye movements? In this talk, I will present the analysis of a computational model of the global salience of natural images. We trained a machine learning algorithm to learn the direction of the first saccade of participants who freely observed pairs of images. The pairs balanced the combinations of new and already seen images, as well as task and task-free trials. The coefficients of the model provided a reliable measure of the likelihood of each image to attract the first fixation when seen next to another image, that is their global salience. For example, images of close-up faces and images containing humans were consistently looked first and were assigned higher global salience. Interestingly, we found that global salience cannot be explained by the feature-driven local salience of images, the influence of task and familiarity was rather small and we reproduced the previously reported left-sided bias. This computational model of global salience allows to analyse multiple other aspects of human visual perception of competing stimuli. In the talk, I will also present our latest results from analysing the saccadic reaction time as a function of the global salience of the pair of images.

Popular notions of how the retina encodes visual stimuli typically focus on the center-surround receptive fields of retinal ganglion cells, the output neurons of the retina. In this view, the receptive field acts as a linear filter on the visual stimulus, highlighting spatial contrast and providing efficient representations of natural images. Yet, we also know that many ganglion cells respond vigorously to fine spatial gratings that should not activate the linear filter of the receptive field. Thus, ganglion cells may integrate visual signals nonlinearly across space. In this talk, I will discuss how these (and other) nonlinearities relate to the encoding of natural visual stimuli in the retina. Based on electrophysiological recordings of ganglion and bipolar cells from mouse and salamander retina, I will present methods for assessing nonlinear processing in different cell types and examine their importance and potential function under natural stimulation.