Vision Science seminars

One challenge in exploring the internal representation of faces is the lack of controlled stimuli transformations. Researchers are often limited to verbalizable transformations in the creation of a dataset. An alternative approach to verbalization for interpretability is finding image-based measures that allow us to quantify image transformations. In this study, we explore whether PCA could be used to create controlled transformations to a face by testing the effect of these transformations on human perceptual similarity and on computational differences in Gabor, Pixel and DNN spaces. We found that perceptual similarity and the three image-based spaces are linearly related, almost perfectly in the case of the DNN, with a correlation of 0.94. This provides a controlled way to alter the appearance of a face. In experiment 2, the effect of familiarity on the perception of multidimensional transformations was explored. Our findings show that there is a positive relationship between the number of components transformed and both the perceptual similarity and the same three image-based spaces used in experiment 1. Furthermore, we found that familiar faces are rated more similar overall than unfamiliar faces. That is, a change to a familiar face is perceived as making less difference than the exact same change to an unfamiliar face. The ability to quantify, and thus control, these transformations is a powerful tool in exploring the factors that mediate a change in perceived identity.

The limited capacity of visual working memory constrains the quantity and quality of the information we can store in mind for ongoing processing. Research from our lab has demonstrated that verbal labeling/categorization of visual inputs increases its retention and fidelity in visual working memory. In this talk, I will outline the hypotheses that explain the interaction between visual and verbal inputs in working memory, leading to the boosts we observed. I will further show how manipulations of the categorical distinctiveness of the labels, the timing of their occurrence, to which item labels are applied, as well as their validity modulate the benefits one can draw from combining visual and verbal inputs to alleviate capacity limitations. Finally, I will discuss the implications of these results to our understanding of working memory and its interaction with prior knowledge.

Not all individuals are equally competent at recognizing the faces they interact with. Revealing how the brains of different individuals support variations in this ability is a crucial step to develop an understanding of real-world human visual behaviour. In this talk, I will present findings from a large high-density EEG dataset (>100k trials of participants processing various stimulus categories) and computational approaches which aimed to characterise the brain representations behind real-world proficiency of “super-recognizers”—individuals at the top of face recognition ability spectrum. Using decoding analysis of time-resolved EEG patterns, we predicted with high precision the trial-by-trial activity of super-recognizers participants, and showed that evidence for face recognition ability variations is disseminated along early, intermediate and late brain processing steps. Computational modeling of the underlying brain activity uncovered two representational signatures supporting higher face recognition ability—i) mid-level visual & ii) semantic computations. Both components were dissociable in brain processing-time (the first around the N170, the last around the P600) and levels of computations (the first emerging from mid-level layers of visual Convolutional Neural Networks, the last from a semantic model characterising sentence descriptions of images). I will conclude by presenting ongoing analyses from a well-known case of acquired prosopagnosia (PS) using similar computational modeling of high-density EEG activity.

Prosopometamorphopsia (PMO) is a disorder characterized by face perception distortions. People with PMO see facial features that appear to melt, stretch, and change size and position. I'll discuss research on PMO carried out by my lab and others that sheds light on the cognitive and neural organization of face perception. https://facedistortion.faceblind.org/

At any given moment the brain receives more sensory information than it can use to guide adaptive behaviour, creating the need for mechanisms that promote efficient processing of incoming sensory signals. One way in which the brain might reduce its sensory processing load is to encode successive presentations of the same stimulus in a more efficient form, a process known as neural adaptation. Conversely, when a stimulus violates an expected pattern, it should evoke an enhanced neural response. Such a scheme for sensory encoding has been formalised in predictive coding theories, which propose that recent experience establishes expectations in the brain that generate prediction errors when violated. In this webinar, Professor Jason Mattingley will discuss whether the encoding of elementary visual features is modulated when otherwise identical stimuli are expected or unexpected based upon the history of stimulus presentation. In humans, EEG was employed to measure neural activity evoked by gratings of different orientations, and multivariate forward modelling was used to determine how orientation selectivity is affected for expected versus unexpected stimuli. In mice, two-photon calcium imaging was used to quantify orientation tuning of individual neurons in the primary visual cortex to expected and unexpected gratings. Results revealed enhanced orientation tuning to unexpected visual stimuli, both at the level of whole-brain responses and for individual visual cortex neurons. Professor Mattingley will discuss the implications of these findings for predictive coding theories of sensory encoding. Professor Jason Mattingley is a Laureate Fellow and Foundation Chair in Cognitive Neuroscience at The University of Queensland. His research is directed toward understanding the brain processes that support perception, selective attention and decision-making, in health and disease.

Many animals are highly visual. They view their world through photoreceptors sensitive to different wavelengths of light. Animal survival and optimal behavioral performance may select for varying photoreceptor sensitivities depending on animal habitat or visual tasks. Our goal is to understand what drives visual diversity from both an evolutionary and molecular perspective. The group of more than 2000 cichlid fish species are an ideal system for examining such diversity. Cichlid are a colorful group of fresh water fishes. They have undergone adaptive radiation throughout Africa and the new world and occur in rivers and lakes that vary in water clarity. They are also behaviorally complex, having diverse behaviors for foraging, mate choice and even parental care. As a result, cichlids have highly diverse visual systems with cone sensitivities shifting by 30-90 nm between species. Although this group has seven cone opsin genes, individual species differ in which subset of the cone opsins they express. Some species show developmental shifts in opsin expression, switching from shorter to longer wavelength opsins through ontogeny. Other species modify that developmental program to express just one of the sets, causing the large sensitivity differences. Cichlids are therefore natural mutants for opsin expression. We have used cichlid diversity to explore the relationship between visual sensitivities and ecology. We have also exploited the genomic power of the cichlid system to identify genes and mutations that cause opsin expression shifts. Ultimately, our goal is to learn how different cichlid species see the world and whether differences matter. Behavioral experiments suggest they do indeed use color vision to survive and thrive. Cichlids therefore are a unique model for exploring how visual systems evolve in a changing world.

Colour vision plays important roles in regulating animal behaviour, yet understanding of how such information is processed in the brain is still incomplete. Here I discuss our work addressing this issue in mice where, despite aspects of retinal organisation that might suggest limited capacity for colour vision, we find evidence of extensive cone-dependent spectral opponency across subcortical visual centres. In particular, our data both reveals important contributions of such colour signals to non-image-forming functions (regulation of the circadian system) but also indicate surprisingly sophisticated support for more conventional aspects of colour vision.

One of the most important questions in psychology and neuroscience is understanding how the outside world maps to internal representations. Classical psychophysics approaches to this problem have a number of limitations: they mostly study low dimensional perpetual spaces, and are constrained in the number and diversity of participants and experiments. As ecologically valid perception is rich, high dimensional, contextual, and culturally dependent, these impediments severely bias our understanding of perceptual representations. Recent technological advances—the emergence of so-called “Virtual Labs”— can significantly contribute toward overcoming these barriers. Here I present a number of specific strategies that my group has developed in order to probe representations across a number of dimensions. 1) Massive online experiments can increase significantly the amount of participants and experiments that can be carried out in a single study, while also significantly diversifying the participant pool. We have developed a platform, PsyNet, that enables “experiments as code,” whereby the orchestration of computer servers, recruiting, compensation of participants, and data management is fully automated and every experiment can be fully replicated with one command line. I will demonstrate how PsyNet allows us to recruit thousands of participants for each study with a large number of control experimental conditions, significantly increasing our understanding of auditory perception. 2) Virtual lab methods also enable us to run experiments that are nearly impossible in a traditional lab setting. I will demonstrate our development of adaptive sampling, a set of behavioural methods that combine machine learning sampling techniques (Monte Carlo Markov Chains) with human interactions and allow us to create high-dimensional maps of perceptual representations with unprecedented resolution. 3) Finally, I will demonstrate how the aforementioned methods can be applied to the study of perceptual priors in both audition and vision, with a focus on our work in cross-cultural research, which studies how perceptual priors are influenced by experience and culture in diverse samples of participants from around the world.

“Call me a marine vision scientist. Some years ago - never mind how long precisely - having little or no money in my purse, and nothing particular to interest me on shore, I thought I would sail about a little and see what animals see in the watery part of the world. It is a way I have of dividing off the spectrum, and regulating circular polarisation.” Sometimes I wish I had just set out to harpoon a white whale as it would have been easier than studying stomatopod (mantis shrimp) vision. Nowhere near as much fun of course and certainly less dangerous so in this presentation I track the history of discovery and confusion that stomatopods deliver in trying to understand what the do actually see. The talk unashamedly borrows from that of Mike Bok a few weeks ago (April 13th 2021 “The Blurry Beginnings: etc” talk) as an introduction to the system (do go look at his talk again, it is beautiful!) and goes both backwards and forwards in time, trying to provide an explanation for the design of this visual system. The journey is again one of retinal anatomy and physiology, neuroanatomy, electrophysiology, behaviour and body ornaments but this time focusses more on polarisation vision (Mike covered the colour stuff well). There is a comparative section looking at the cephalopods too and by the end, I hope you will understand where we are at with trying to understand this extraordinary way of seeing the world and why we ‘pod-people’ wave our arms around so much when asked to explain; what do stomatopods see? Maybe, to butcher another quote: “mantis shrimp have been rendered visually beautiful for vision’s sake.”

Detection of motion is essential for survival, but how the visual system processes moving stimuli is not fully understood. Here, based on a detailed analysis of glutamate release from bipolar cells, we outline the rules that govern the representation of object motion in the early processing stages. Our main findings are as follows: (1) Motion processing begins already at the first retinal synapse. (2) The shape and the amplitude of motion responses cannot be reliably predicted from bipolar cell responses to stationary objects. (3) Enhanced representation of novel objects - particularly in bipolar cells with transient dynamics. (4) Response amplitude in bipolar cells matches visual salience reported in humans: suddenly appearing objects > novel motion > existing motion. These findings can be explained by antagonistic interactions in the center-surround receptive field, demonstrate that despite their simple operational concepts, classical center-surround receptive fields enable sophisticated visual computations.

Visual knowledge obtained from our lifelong experience of the world plays a critical role in our ability to build short-term memories. We propose a mechanistic explanation of how working memory (WM) representations are built from the latent representations of visual knowledge and can then be reconstructed. The proposed model, Memory for Latent Representations (MLR), features a variational autoencoder with an architecture that corresponds broadly to the human visual system and an activation-based binding pool of neurons that binds items’ attributes to tokenized representations. The simulation results revealed that shape information for stimuli that the model was trained on, can be encoded and retrieved efficiently from latents in higher levels of the visual hierarchy. On the other hand, novel patterns that are completely outside the training set can be stored from a single exposure using only latents from early layers of the visual system. Moreover, the representation of a given stimulus can have multiple codes, representing specific visual features such as shape or color, in addition to categorical information. Finally, we validated our model by testing a series of predictions against behavioral results acquired from WM tasks. The model provides a compelling demonstration of visual knowledge yielding the formation of compact visual representation for efficient memory encoding.

In Europe, the number of people aged 65 and older is increasing dramatically, and research related to ageing is more crucial than ever. The main research dedicated to age-related changes concentrates on cognitive or sensory deficits. This is also the case in vision research. However, the majority of older adults ages without major cognitive or optical or deficits. These are foremost good news, but even in the absence of neurodegenerative or eye diseases changes in visual perception occur. It has been suggested that age-related changes are due to a general decline of cognitive, perceptual and sensory functions. However, more recent studies reveal large individual differences within the ageing population and whereas some functions show age-related deterioration, others are surprisingly unaffected. Overall, it becomes increasingly apparent that perceptual changes in healthy ageing cannot be attributed to one single underlying factor. I will present studies from various areas of visual perception that challenge the view that age-related changes are primarily related to decline. Instead, our findings suggest that age-related changes are the result of visual experience, such that the brain ages optimally given the input it receives.

In order to successfully navigate through the environment, animals must accurately estimate the status of their motion with respect to the surrounding scene and objects. In this talk, I will present our recent work on how retrosplenial cortical (RSC) neurons combine vestibular and visual signals to reliably encode the direction and speed of head turns during passive motion and active navigation. I will discuss these data in the context of RSC long-range connectivity and further show our ongoing work on building population-level models of motion representation across cortical and subcortical networks.

There is a surprising consistency in the images we remember and forget – across observers, certain images are intrinsically more memorable than others in spite of our diverse individual experiences. The perception of images at different memorability levels also results in stereotyped patterns in visual and mnemonic regions in the brain, regardless of an individual’s actual memory for that item. In this talk, Dr. Bainbridge will discuss our current neuroscientific understanding of how memorability is represented in patterns in the brain, potentially serving as a signal for how stimulus information is prioritized for eventual memory encoding.

Eyes abound in the animal kingdom. Some are large as basketballs and others are just fractions of a millimetre. Eyes also come in many different types, such as the compound eyes of insects, the mirror eyes of scallopsor our own camera-like eyes. Common to all animal eyes is that they serve the same fundamental role of collecting external information for guidingthe animal’s behaviour. But behaviours vary tremendously across the animal kingdom, and it turns outthis is the key to understand how eyes evolved. The lecture will take a tour from the first animals that could only sense the presence of light, to those that saw the first crude image of the world and finally to animals that use acute vision for interacting with otheranimals. Amazingly, all these stages of eye evolution still exist in animals living today, and this is how we can unravel the evolution of behaviours that has been the driving force behind eye evolution

The ventral visual pathway extends from the occipital to the anterior temporal regions, and is specialized in giving meaning to objects and people that are perceived through vision. Numerous studies in functional magnetic resonance imaging have focused on the cerebral basis of visual recognition. However, this technique is susceptible to magnetic artefacts in ventral anterior temporal regions and it has led to an underestimation of the role of these regions within the ventral visual stream, especially with respect to face recognition and semantic representations. Moreover, there is an increasing need for implicit methods assessing these functions as explicit tasks lack specificity. In this talk, I will present three studies using fast periodic visual stimulation (FPVS) in combination with scalp and/or intracerebral EEG to overcome these limitations and provide high SNR in temporal regions. I will show that, beyond face recognition, FPVS can be extended to investigate semantic representations using a face-name association paradigm and a semantic categorisation paradigm with written words. These results shed new light on the role of temporal regions and demonstrate the high potential of the FPVS approach as a powerful electrophysiological tool to assess various cognitive functions in neurotypical and clinical populations.