Vision Science seminars

Classification of neurons, long viewed as a fairly boring enterprise, has emerged as a major bottleneck in analysis of neural circuits. High throughput single cell RNA-seq has provided a new way to improve the situation. We initially applied this method to mouse retina, showing that its five neuronal classes (photoreceptors, three groups of interneurons, and retinal ganglion cells) can be divided into 130 discrete types. We then applied the method to other species including human, macaque, zebrafish and chick. With the atlases in hand, we are now using them to address questions about how retinal cell types diversify, how they differ in their responses to injury and disease, and the extent to which cell classes and types are conserved among vertebrates.

When we remember a stimulus we rarely maintain a full fidelity representation of the observed item. Our working memory instead maintains a mixture of the observed feature values and categorical/gist information. I will discuss evidence from computational models supporting a mix of categorical and fine-detail information in working memory. Having established the need for two memory formats in working memory, I will discuss whether categorical and fine-detailed information for a stimulus are represented separately or as a single unified representation. Computational models of these two potential cognitive structures make differing predictions about the pattern of responses in visual working memory recall tests. The present study required participants to remember the orientation of stimuli for later reproduction. The pattern of responses are used to test the competing representational structures and to quantify the relative amount of fine-detailed and categorical information maintained. The effects of set size, encoding time, serial order, and response order on memory precision, categorical information, and guessing rates are also explored. (This is a 60 min talk).

How our senses work both separately and together involves rich computational problems. I will discuss the spatial and representational problems faced by the visual and auditory system, focusing on two issues. 1. How does the brain correct for discrepancies in the visual and auditory spatial reference frames? I will describe our recent discovery of a novel type of otoacoustic emission, the eye movement related eardrum oscillation, or EMREO (Gruters et al, PNAS 2018). 2. How does the brain encode more than one stimulus at a time? I will discuss evidence for neural time-division multiplexing, in which neural activity fluctuates across time to allow representations to encode more than one simultaneous stimulus (Caruso et al, Nat Comm 2018). These findings all emerged from experimentally testing computational models regarding spatial representations and their transformations within and across sensory pathways. Further, they speak to several general problems confronting modern neuroscience such as the hierarchical organization of brain pathways and limits on perceptual/cognitive processing.

Previous work looking at individual differences in face identity processing (FIP) has found that most commonly used lab-based performance assessments are unfortunately not sufficiently sensitive on their own for measuring performance in both the upper and lower tails of the general population simultaneously. So more recently, researchers have begun incorporating multiple testing procedures into their assessments. Still, though, the growing consensus seems to be that at the individual level, there is quite a bit of variability between test scores. The overall consequence of this is that extreme scores will still occur simply by chance in large enough samples. To mitigate this issue, our recent work has developed measures of intra-individual FIP consistency to refine selection of those with superior abilities (i.e. from the upper tail). For starters, we assessed consistency of face matching and recognition in neurotypical controls, and compared them to a sample of SRs. In terms of face matching, we demonstrated psychophysically that SRs show significantly greater consistency than controls in exploiting spatial frequency information than controls. Meanwhile, we showed that SRs’ recognition of faces is highly related to memorability for identities, yet effectively unrelated among controls. So overall, at the high end of the FIP spectrum, consistency can be a useful tool for revealing both qualitative and quantitative individual differences. Finally, in conjunction with collaborators from the Rheinland-Pfalz Police, we developed a pair of bespoke work samples to get bias-free measures of intraindividual consistency in current law enforcement personnel. Officers with higher composite scores on a set of 3 challenging FIP tests tended to show higher consistency, and vice versa. Overall, this suggests that not only is consistency a reasonably good marker of superior FIP abilities, but could present important practical benefits for personnel selection in many other domains of expertise.

In this talk I’ll discuss our recent work on how visual and auditory cues to space are integrated as we move. There are at least 3 reasons why this turns out to be a difficult problem for the brain to solve (and us to understand!). First, vision and hearing start off in different coordinates (eye-centred vs head-centred), so they need a common reference frame in which to communicate. By preventing eye and head movements, this problem has been neatly sidestepped in the literature, yet self-movement is the norm. Second, self-movement creates visual and auditory image motion. Correct interpretation therefore requires some form of compensation. Third, vision and hearing encode motion in very different ways: vision contains dedicated motion detectors sensitive to speed, whereas hearing does not. We propose that some (all?) of these problems could be solved by considering the perception of audiovisual space as the integration of separate body-centred visual and auditory cues, the latter formed by integrating image motion with motor system signals and vestibular information. To test this claim, we use a classic cue integration framework, modified to account for cues that are biased and partially correlated. We find good evidence for the model based on simple judgements of audiovisual motion within a circular array of speakers and LEDs that surround the participant while they execute self-controlled head movement.

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.

It is an open question whether preferences for visual art can be lawfully predicted from the basic constituent elements of a visual image. Here, we developed and tested a computational framework to investigate how aesthetic values are formed. We show that it is possible to explain human preferences for a visual art piece based on a mixture of low- and high-level features of the image. Subjective value ratings could be predicted not only within but also across individuals, using a regression model with a common set of interpretable features. We also show that the features predicting aesthetic preference can emerge hierarchically within a deep convolutional neural network trained only for object recognition. Our findings suggest that human preferences for art can be explained at least in part as a systematic integration over the underlying visual features of an image.

Natural behavior reveals the way that gaze serves the needs of the current task, and the complex cognitive control mechanisms that are involved. It has become increasingly clear that even the simplest actions involve complex decision processes that depend on an interaction of visual information, knowledge of the current environment, and the intrinsic costs and benefits of actions choices. I will explore these ideas in the context of walking in natural terrain, where we are able to recover the 3D structure of the visual environment. We show that subjects choose flexible paths that depend on the flatness of the terrain over the next few steps. Subjects trade off flatness with straightness of their paths towards the goal, indicating a nuanced trade-off between stability and energetic costs on both the time scale of the next step and longer-range constraints.

I will discuss recent findings from our lab about the effects of partial photoreceptor loss on the retinal circuit’s structure and function. I will relate this work to the question of whether the visual system can distinguish between changes in light level and photoreceptor number.

Neuronal computations are matched to optimally encode the sensory information that is available and relevant for the animal. However, the physical distribution of sensory information is often shaped by the animal’s own behavior. One prominent example is the encoding of optic flow fields that are generated during self-motion of the animal and will therefore depend on the type of locomotion. How evolution has matched computational resources to the behavioral constraints of an animal is not known. Here we use in vivo two photon imaging to record from a population of >3.500 local-direction selective cells. Our data show that the local direction-selective T4/T5 neurons in Drosophila form a population code that is matched to represent optic flow fields generated during translational and rotational self-motion of the fly. This coding principle for optic flow is reminiscent to the population code of local direction-selective ganglion cells in the mouse retina, where four direction-selective ganglion cells encode four different axes of self-motion encountered during walking (Sabbah et al., 2017). However, in flies we find six different subtypes of T4 and T5 cells that, at the population level, represent six axes of self-motion of the fly. The four uniformly tuned T4/T5 subtypes described previously represent a local snapshot (Maisak et al. 2013). The encoding of six types of optic flow in the fly as compared to four types of optic flow in mice might be matched to the high degrees of freedom encountered during flight. Thus, a population code for optic flow appears to be a general coding principle of visual systems, resulting from convergent evolution, but matching the individual ethological constraints of the animal.

Materials with complex appearances, like textiles and foodstuffs, pose challenges for conventional theories of vision. How does the brain learn to see properties of the world—like the glossiness of a surface—that cannot be measured by any other senses? Recent advances in unsupervised deep learning may help shed light on material perception. I will show how an unsupervised deep neural network trained on an artificial environment of surfaces that have different shapes, materials and lighting, spontaneously comes to encode those factors in its internal representations. Most strikingly, the model makes patterns of errors in its perception of material that follow, on an image-by-image basis, the patterns of errors made by human observers. Unsupervised deep learning may provide a coherent framework for how many perceptual dimensions form, in material perception and beyond.

The talk will give a tour of Guided Search 6.0 (GS6), the latest evolution of the Guided Search model of visual search. Part 1 describes The Mechanics of Search. Because we cannot recognize more than a few items at a time, selective attention is used to prioritize items for processing. Selective attention to an item allows its features to be bound together into a representation that can be matched to a target template in memory or rejected as a distractor. The binding and recognition of an attended object is modeled as a diffusion process taking > 150 msec/item. Since selection occurs more frequently than that, it follows that multiple items are undergoing recognition at the same time, though asynchronously, making GS6 a hybrid serial and parallel model. If a target is not found, search terminates when an accumulating quitting signal reaches a threshold. Part 2 elaborates on the five sources of Guidance that are combined into a spatial “priority map” to guide the deployment of attention (hence “guided search”). These are (1) top-down and (2) bottom-up feature guidance, (3) prior history (e.g. priming), (4) reward, and (5) scene syntax and semantics. Finally, in Part 3, we will consider the internal representation of what we are searching for; what is often called “the search template”. That search template is really two templates: a guiding template (probably in working memory) and a target template (in long term memory). Put these pieces together and you have GS6.

I will discuss the mechanisms that determine whether a weak visual stimulus will reach consciousness or not. If the stimulus is simple, early visual cortex acts as a relay station that sends the information to higher visual areas. If the stimulus arrives at a minimal strength, it will be stored in working memory and can be reported. However, during more complex visual perceptions, which for example depend on the segregation of a figure from the background, early visual cortex’ role goes beyond a simply relay. It now acts as a cognitive blackboard and conscious perception depends on it. Our results inspire new approaches to create a visual prosthesis for the blind, by creating a direct interface with the visual brain. I will discuss how high-channel-number interfaces with the visual cortex might be used to restore a rudimentary form of vision in blind individuals.

Many models of attention assume that attentional selection takes place at a specific moment in time which demarcates the critical transition from pre-attentive to attentive processing of sensory input. We argue that this intuitively appealing account is not only inaccurate, but has led to substantial conceptual confusion (to the point where some attention researchers offer to abandon the term ‘attention’ altogether). As an alternative, we offer a “diachronic” framework that describes attentional selectivity as a process that unfolds over time. Key to this view is the concept of attentional episodes, brief periods of intense attentional amplification of sensory representations that regulate access to working memory and response-related processes. We describe how attentional episodes are linked to earlier attentional mechanisms and to recurrent processing at the neural level. We present data showing that multiple sequential events can be involuntarily encoded in working memory when they appear during the same attentional episode, whether they are relevant or not. We also discuss the costs associated with processing multiple events within a single episode. Finally, we argue that breaking down the dichotomy between pre-attentive and attentive (as well as early vs. late selection) offers new solutions to old problems in attention research that have never been resolved. It can provide a unified and conceptually coherent account of the network of cognitive and neural processes that produce the goal-directed selectivity in perceptual processing that is commonly referred to as “attention”.

Sensory experience often depends on one’s own actions, including self-motion. Theories of predictive coding postulate that actions are regulated by calculating prediction error, which is the difference between sensory experience and expectation based on self-generated actions. Signals consistent with prediction error have been reported in mouse visual cortex (V1) when visual flow coupled to running was unexpectedly stopped. Here, we show such signals can be elicited by visual stimuli uncoupled to animal’s running. We recorded V1 neurons while presenting drifting gratings that unexpectedly stopped. We found strong responses to visual perturbations, which were enhanced during running. Perturbation responses were strongest in the preferred orientation of individual neurons and perturbation responsive neurons were more likely to prefer slow visual speeds. Our results indicate that prediction error signals can be explained by the convergence of known motor and sensory signals, providing a purely sensory and motor explanation for purported mismatch signals.

A recent formulation of predictive coding theory proposes that a subset of neurons in each cortical area encodes sensory prediction errors, the difference between predictions relayed from higher cortex and the sensory input. Here, we test for evidence of prediction error responses in spiking responses and local field potentials (LFP) recorded in primary visual cortex and area V4 of macaque monkeys, and in complementary electroencephalographic (EEG) scalp recordings in human participants. We presented a fixed sequence of visual stimuli on most trials, and violated the expected ordering on a small subset of trials. Under predictive coding theory, pattern-violating stimuli should trigger robust prediction errors, but we found that spiking, LFP and EEG responses to expected and pattern-violating stimuli were nearly identical. Our results challenge the assertion that a fundamental computational motif in sensory cortex is to signal prediction errors, at least those based on predictions derived from temporal patterns of visual stimulation.