Showing papers on "Visual perception published in 2016"
TL;DR: In this article, an artificial system based on a Deep Neural Network that creates artistic images of high perceptual quality is presented. But the system uses neural representations to separate and recombine content and style of arbitrary images, providing a neural algorithm for the creation of artistic images.
Abstract: In fine art, especially painting, humans have mastered the skill to create unique visual experiences through composing a complex interplay between the content and style of an image. Thus far the algorithmic basis of this process is unknown and there exists no artificial system with similar capabilities. However, in other key areas of visual perception such as object and face recognition near-human performance was recently demonstrated by a class of biologically inspired vision models called Deep Neural Networks. Here we introduce an artificial system based on a Deep Neural Network that creates artistic images of high perceptual quality. The system uses neural representations to separate and recombine content and style of arbitrary images, providing a neural algorithm for the creation of artistic images. Moreover, in light of the striking similarities between performance-optimised artificial neural networks and biological vision, our work offers a path forward to an algorithmic understanding of how humans create and perceive artistic imagery.
838 citations
TL;DR: This work suggests that none of these hundreds of studies – either individually or collectively – provides compelling evidence for true top-down effects on perception, or “cognitive penetrability,” and suggests that these studies all fall prey to only a handful of pitfalls.
Abstract: What determines what we see? In contrast to the traditional "modular" understanding of perception, according to which visual processing is encapsulated from higher-level cognition, a tidal wave of recent research alleges that states such as beliefs, desires, emotions, motivations, intentions, and linguistic representations exert direct top-down influences on what we see. There is a growing consensus that such effects are ubiquitous, and that the distinction between perception and cognition may itself be unsustainable. We argue otherwise: none of these hundreds of studies - either individually or collectively - provide compelling evidence for true top-down effects on perception, or "cognitive penetrability". In particular, and despite their variety, we suggest that these studies all fall prey to only a handful of pitfalls. And whereas abstract theoretical challenges have failed to resolve this debate in the past, our presentation of these pitfalls is empirically anchored: in each case, we show not only how certain studies could be susceptible to the pitfall (in principle), but how several alleged top-down effects actually are explained by the pitfall (in practice). Moreover, these pitfalls are perfectly general, with each applying to dozens of other top-down effects. We conclude by extracting the lessons provided by these pitfalls into a checklist that future work could use to convincingly demonstrate top-down effects on visual perception. The discovery of substantive top-down effects of cognition on perception would revolutionize our understanding of how the mind is organized; but without addressing these pitfalls, no such empirical report will license such exciting conclusions. Language: en
707 citations
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TL;DR: Three types of low-level statistical features in both spatial and frequency domains are designed to quantify super-resolved artifacts and a two-stage regression model is learned to predict the quality scores of super-resolution images without referring to ground-truth images.
Abstract: Numerous single-image super-resolution algorithms have been proposed in the literature, but few studies address the problem of performance evaluation based on visual perception. While most super-resolution images are evaluated by fullreference metrics, the effectiveness is not clear and the required ground-truth images are not always available in practice. To address these problems, we conduct human subject studies using a large set of super-resolution images and propose a no-reference metric learned from visual perceptual scores. Specifically, we design three types of low-level statistical features in both spatial and frequency domains to quantify super-resolved artifacts, and learn a two-stage regression model to predict the quality scores of super-resolution images without referring to ground-truth images. Extensive experimental results show that the proposed metric is effective and efficient to assess the quality of super-resolution images based on human perception.
267 citations
TL;DR: It is revealed that locomotion increases the activity of vasoactive intestinal peptide, somatostatin and parvalbumin-positive interneurons during visual stimulation, challenging the disinhibition model and establishing that modulation of neuronal activity by locomotion is context-dependent.
Abstract: How we perceive what we see depends on the context in which we see it, such as what we are doing at the time. For example, we perceive a park landscape differently when we are running through it than when we are sitting on a park bench. Behavior can also alter neuronal responses in the brain. Indeed, the neurons in the part of the brain that receives information related to vision (known as the visual cortex) respond differently to visual stimuli when an animal is moving compared to when the animal is still. However, while some recent studies revealed that specific types of neurons become more or less responsive during movement, others reported the opposite results. One hypothesis that would explain these contradictory findings would be if the way that behavior, in this case movement, affects neuronal responses also depends on the external context in which the movement happens. Now, Pakan et al. have tested this hypothesis by imaging the activity of different types of neurons in the primary visual cortex of mice that were either running on a treadmill or staying still. The experiments were conducted in two different contexts: in total darkness (in which the mice could not see) and in the presence of display screens (which provided the mice with visual stimulation). Pakan et al. confirmed that running does indeed affect the activity of specific neurons in different ways in different contexts. For example, when the mice received visual stimulation, the three main classes of neurons that send inhibitory signals in the visual cortex became more active during running. However, when the mouse ran in the dark, two of these neuron types became more active during running while the third type of neuron was unresponsive. This finding reveals more about the dynamic nature of inhibitory activity that strongly depends on the animal’s behaviour. It also shows how these neurons influence the excitatory neurons in the visual cortex, which send information to the rest of the brain for further processing towards perception. The next step will be to identify what precise mechanism makes these neurons respond differently in unique contexts, and to tease apart how these movement-dependent signals affect the way animals perceive visual stimuli.
237 citations
TL;DR: In this paper, the authors argue that although we see more than the handful of objects, claimed by prominent models of visual attention and working memory, we still see far less than we think we do.
232 citations
TL;DR: It is shown that many of these connections instantiate a "processing principle," according to which perceived time is positively related to perceptual vividity and the ease of extracting information from the stimulus, which generates testable predictions and provides a starting-point for integrated theoretical frameworks.
Abstract: Time is a universal psychological dimension, but time perception has often been studied and discussed in relative isolation. Increasingly, researchers are searching for unifying principles and integrated models that link time perception to other domains. In this review, we survey the links between temporal cognition and other psychological processes. Specifically, we describe how subjective duration is affected by nontemporal stimulus properties (perception), the allocation of processing resources (attention), and past experience with the stimulus (memory). We show that many of these connections instantiate a "processing principle," according to which perceived time is positively related to perceptual vividity and the ease of extracting information from the stimulus. This empirical generalization generates testable predictions and provides a starting-point for integrated theoretical frameworks. By outlining some of the links between temporal cognition and other domains, and by providing a unifying principle for understanding these effects, we hope to encourage time-perception researchers to situate their work within broader theoretical frameworks, and that researchers from other fields will be inspired to apply their insights, techniques, and theorizing to improve our understanding of the representation and judgment of time. (PsycINFO Database Record
230 citations
TL;DR: It is shown that, with experience in a virtual environment, the activity of neurons in layer 2/3 of mouse primary visual cortex (V1) becomes increasingly informative of spatial location, consistent with the hypothesis that visual cortex forms an internal representation of the visual scene based on spatial location.
Abstract: The authors find that activity in rodent visual cortex can depend on the animal's location in a virtual environment and can predict upcoming visual stimuli. Omitting a stimulus that a mouse expects to see results in a strong mismatch signal, implying that visual cortex compares visual signals to expectations in familiar environments.
221 citations
TL;DR: Overall, consistent with previous univariate findings, the results indicate that superior IPS, but not occipital cortex, has a central role in VSTM storage.
Abstract: Using fMRI multi-voxel pattern decoding, human superior IPS, but not occipital cortex, was found to closely track behavioral measures of information storage in visual short-term memory (VSTM) across distractor presence and predictability. This suggests that superior IPS, and not occipital cortex, has a central role in VSTM storage in the human brain.
214 citations
01 Jan 2016
TL;DR: Kolb et al. as mentioned in this paper used a machine learning approach to the visual perception of acoustic information during motor control and action do expertise and the degree of perception for recognition and action in sport.
Abstract: The best ebooks about Visual Perception And Action In Sport that you can get for free here by download this Visual Perception And Action In Sport and save to your desktop. This ebooks is under topic such as 'how perception guides action: examples from sport and understanding perception and action in sport: how can studies in perception and action vi v 6 full download free download visual perception and action in sport book visual control of braking in goal-directed action and sport perception and action in sport: half-time comments on the action induction by visual perception of rotational motion performance psychology: perception, action, cognition, and sport neuroscience revisited (?): a commentary how important is perception-action coupling in the tennis zeitschrift fur sportpsychologie (german journal of sport visual perception and introduction action in golf putting perception for recognition and action (pra) studies in perception and action vi v 6 ebook | slangsurfing non-conscious visual cues related to affect and action interactive video training of perceptual decision-making the role of sound in motor perception and execution high performance circuit e-journal john kolb, phd a machine learning approach to the visual perception of acoustic information during motor control and action do expertise and the degree of perception^action coupling perception and action in golf putting: skill differences free download aviation visual perception book intercepting a 3d versus 2d videoed opponent: visual representative learning design in dynamic interceptive actions demonstration and verbal instructions perception of maximum stepping and leaping distance training perceptual skill by orienting visual attention the influence of video-based perceptual training on physical characteristics of elite female karate athletes vision testing and visual training in sport the use of video game technology for investigating action feedback affects the perception of action-related neurotracker as a supportive indicator for rtp decision making representative learning design and functionality of model-based control of perceptioniaction visual search strategy and interception accuracy ideals multidimensional perceptual-cognitive skills training visual scanning in sports actions: comparison between journal of experimental psychology: human perception and
201 citations
TL;DR: It is shown that memory-based expectations in human visual cortex are related to the hippocampal mechanism of pattern completion, and this helps model predictive coding frame perception as a generative process in which expectations constrain sensory representations.
Abstract: Models of predictive coding frame perception as a generative process in which expectations constrain sensory representations. These models account for expectations about how a stimulus will move or change from moment to moment, but do not address expectations about what other, distinct stimuli are likely to appear based on prior experience. We show that such memory-based expectations in human visual cortex are related to the hippocampal mechanism of pattern completion.
198 citations
TL;DR: The evidence implicating dorsal object representations is reviewed, and an account of the anatomical organization, functional contributions, and origins of these representations in the service of perception is proposed.
TL;DR: The neural dynamics underlying the maintenance of variably visible stimuli using magnetoencephalography are investigated and it is suggested that invisible information can be briefly maintained within the higher processing stages of visual perception.
TL;DR: It is shown that cardiac interoceptive signals modulate awareness for visual stimuli such that visual stimuli occurring at the cardiac frequency take longer to access visual awareness and are more difficult to discriminate.
Abstract: The processing of interoceptive signals in the insular cortex is thought to underlie self-awareness. However, the influence of interoception on visual awareness and the role of the insular cortex in this process remain unclear. Here, we show in a series of experiments that the relative timing of visual stimuli with respect to the heartbeat modulates visual awareness. We used two masking techniques and show that conscious access for visual stimuli synchronous to participants' heartbeat is suppressed compared with the same stimuli presented asynchronously to their heartbeat. Two independent brain imaging experiments using high-resolution fMRI revealed that the insular cortex was sensitive to both visible and invisible cardio-visual stimulation, showing reduced activation for visual stimuli presented synchronously to the heartbeat. Our results show that interoceptive insular processing affects visual awareness, demonstrating the role of the insula in integrating interoceptive and exteroceptive signals and in the processing of conscious signals beyond self-awareness.
Posted Content•
TL;DR: A new data set is introduced, which started from 3+ million weakly labeled images of different emotions and ended up 30 times as large as the current largest publicly available visual emotion data set, to encourage further research on visual emotion analysis.
Abstract: Psychological research results have confirmed that people can have different emotional reactions to different visual stimuli. Several papers have been published on the problem of visual emotion analysis. In particular, attempts have been made to analyze and predict people's emotional reaction towards images. To this end, different kinds of hand-tuned features are proposed. The results reported on several carefully selected and labeled small image data sets have confirmed the promise of such features. While the recent successes of many computer vision related tasks are due to the adoption of Convolutional Neural Networks (CNNs), visual emotion analysis has not achieved the same level of success. This may be primarily due to the unavailability of confidently labeled and relatively large image data sets for visual emotion analysis. In this work, we introduce a new data set, which started from 3+ million weakly labeled images of different emotions and ended up 30 times as large as the current largest publicly available visual emotion data set. We hope that this data set encourages further research on visual emotion analysis. We also perform extensive benchmarking analyses on this large data set using the state of the art methods including CNNs.
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TL;DR: Several common visual problems in older adults that cause performance problems in the visual tasks of everyday living and when exacerbated are related to the development of common eye conditions and diseases of aging.
Abstract: Research on aging and vision has increased dramatically over the past few decades. Changes in our visual capacities in later adulthood have the potential to impact our ability to perform common everyday visual tasks such as recognizing objects, reading, engaging in mobility activities, and driving, thus influencing the quality of our life and well-being. Here, we discuss several common visual problems in older adults that cause performance problems in the visual tasks of everyday living and when exacerbated are related to the development of common eye conditions and diseases of aging.
TL;DR: Results showed that although both foveal and auditory loads reduced Gabor orientation sensitivity, only thefoveal load interacted with retinal eccentricity to produce tunnel vision, clearly demonstrating task-specific changes to the form of the UFOV.
Abstract: A fundamental issue in visual attention is the relationship between the useful field of view (UFOV), the region of visual space where information is encoded within a single fixation, and eccentricity. A common assumption is that impairing attentional resources reduces the size of the UFOV (i.e., tunnel vision). However, most research has not accounted for eccentricity-dependent changes in spatial resolution, potentially conflating fixed visual properties with flexible changes in visual attention. Williams (1988, 1989) argued that foveal loads are necessary to reduce the size of the UFOV, producing tunnel vision. Without a foveal load, it is argued that the attentional decrement is constant across the visual field (i.e., general interference). However, other research asserts that auditory working memory (WM) loads produce tunnel vision. To date, foveal versus auditory WM loads have not been compared to determine if they differentially change the size of the UFOV. In two experiments, we tested the effects of a foveal (rotated L vs. T discrimination) task and an auditory WM (N-back) task on an extrafoveal (Gabor) discrimination task. Gabor patches were scaled for size and processing time to produce equal performance across the visual field under single-task conditions, thus removing the confound of eccentricity-dependent differences in visual sensitivity. The results showed that although both foveal and auditory loads reduced Gabor orientation sensitivity, only the foveal load interacted with retinal eccentricity to produce tunnel vision, clearly demonstrating task-specific changes to the form of the UFOV. This has theoretical implications for understanding the UFOV.
TL;DR: Evidence suggesting that content-specific information can be flexibly maintained in areas across the cortical hierarchy ranging from early visual cortex to PFC is reviewed.
TL;DR: It is found that neural representations in early visual cortex are biased toward previous perceptual decisions, suggesting that biases in perceptual decisions induced by previous stimuli may result from neural biases in sensory cortex induced by recent perceptual history.
Abstract: Sensory signals are highly structured in both space and time. These regularities allow expectations about future stimulation to be formed, thereby facilitating decisions about upcoming visual features and objects. One such regularity is that the world is generally stable over short time scales. This feature of the world is exploited by the brain, leading to a bias in perception called serial dependence: previously seen stimuli bias the perception of subsequent stimuli, making them appear more similar to previous input than they really are. What are the neural processes that may underlie this bias in perceptual choice? Does serial dependence arise only in higher-level areas involved in perceptual decision-making, or does such a bias occur at the earliest levels of sensory processing? In this study, human subjects made decisions about the orientation of grating stimuli presented in the left or right visual field while activity patterns in their visual cortex were recorded using fMRI. In line with previous behavioral reports, reported orientation on the current trial was consistently biased toward the previously reported orientation. We found that the orientation signal in V1 was similarly biased toward the orientation presented on the previous trial. Both the perceptual decision and neural effects were spatially specific, such that the perceptual decision and neural representations on the current trial were only influenced by previous stimuli at the same location. These results suggest that biases in perceptual decisions induced by previous stimuli may result from neural biases in sensory cortex induced by recent perceptual history. SIGNIFICANCE STATEMENT We perceive a stable visual scene, although our visual input is constantly changing. This experience may in part be driven by a bias in visual perception that causes images to be perceived as similar to those previously seen. Here, we provide evidence for a sensory bias that may underlie this perceptual effect. We find that neural representations in early visual cortex are biased toward previous perceptual decisions. Our results suggest a direct neural correlate of serial dependencies in visual perception. These findings elucidate how our perceptual decisions are shaped by our perceptual history.
TL;DR: It is confirmed that visual-motor functional connectivity is disrupted in ASD and the observed temporal incongruity between visual and motor systems was predictive of the severity of social deficits and may contribute to impaired social-communicative skill development in children with ASD.
TL;DR: A perspective of how multiple bottom-up visual cues are flexibly integrated with a range of top-down processes to form perceptions is outlined, and a set of key brain regions involved are identified.
TL;DR: New evidence suggests that the pulvinar's comparatively modest input from structures such as the retina and superior colliculus may critically shape the functional organization of the visual cortex, particularly during early development.
Proceedings Article•
12 Feb 2016TL;DR: In this paper, the authors introduce a new data set, which started from 3+ million weakly labeled images of different emotions and ended up 30 times as large as the current largest publicly available visual emotion data set.
Abstract: Psychological research results have confirmed that people can have different emotional reactions to different visual stimuli. Several papers have been published on the problem of visual emotion analysis. In particular, attempts have been made to analyze and predict people's emotional reaction towards images. To this end, different kinds of hand-tuned features are proposed. The results reported on several carefully selected and labeled small image data sets have confirmed the promise of such features. While the recent successes of many computer vision related tasks are due to the adoption of Convolutional Neural Networks (CNNs), visual emotion analysis has not achieved the same level of success. This may be primarily due to the unavailability of confidently labeled and relatively large image data sets for visual emotion analysis. In this work, we introduce a new data set, which started from 3+ million weakly labeled images of different emotions and ended up 30 times as large as the current largest publicly available visual emotion data set. We hope that this data set encourages further research on visual emotion analysis. We also perform extensive benchmarking analyses on this large data set using the state of the art methods including CNNs.
TL;DR: The evidence for abstract categorical encoding in the primate brain is discussed, the relationship with other perceptual decision paradigms is considered and neuronal category representations are considered as abstract internal cognitive states.
Abstract: Categorization is our ability to flexibly assign sensory stimuli into discrete, behaviorally relevant groupings. Categorical decisions can be used to study decision making more generally by dissociating category identity of stimuli from the actions subjects use to signal their decisions. Here we discuss the evidence for such abstract categorical encoding in the primate brain and consider the relationship with other perceptual decision paradigms. Recent work on visual categorization has examined neuronal activity across a hierarchically organized network of cortical areas in monkeys trained to group visual stimuli into arbitrary categories. This has revealed a transformation of visual-feature encoding in early visual cortical areas into more flexible categorical representations in downstream parietal and prefrontal areas. These neuronal category representations are encoded as abstract internal cognitive states because they are not rigidly linked with either specific sensory stimuli or the actions that the monkeys use to signal their categorical choices.
Book•
30 Nov 2016TL;DR: Crowdsourcing in Computer Vision describes the types of annotations computer vision researchers have collected using crowdsourcing, and how they have ensured that this data is of high quality while annotation effort is minimized.
Abstract: Computer vision systems require large amounts of manually annotated data to properly learn challenging visual concepts. Crowdsourcing platforms offer an inexpensive method to capture human knowledge and understanding, for a vast number of visual perception tasks. Crowdsourcing in Computer Vision describes the types of annotations computer vision researchers have collected using crowdsourcing, and how they have ensured that this data is of high quality while annotation effort is minimized. It begins by discussing data collection on both classic vision tasks, such as object recognition, and recent vision tasks, such as visual story-telling. It then summarizes key design decisions for creating effective data collection interfaces and workflows, and presents strategies for intelligently selecting the most important data instances to annotate. It concludes with some thoughts on the future of crowdsourcing in computer vision. Crowdsourcing in Computer Vision provides an overview of how crowdsourcing has been used in computer vision, enabling a computer vision researcher who has previously not collected non-expert data to devise a data collection strategy. It will also be of help to researchers who focus broadly on crowdsourcing to examine how the latter has been applied in computer vision, and to improve the methods that can be employed to ensure the quality and expedience of data collection.
TL;DR: To explain how the temporal resolution of human vision can be fast compared to sluggish conscious perception, this work proposes a novel conceptual framework in which features of objects are quasi-continuously and unconsciously analyzed with high temporal resolution.
Abstract: We experience the world as a seamless stream of percepts. However, intriguing illusions and recent experiments suggest that the world is not continuously translated into conscious perception. Instead, perception seems to operate in a discrete manner, just like movies appear continuous although they consist of discrete images. To explain how the temporal resolution of human vision can be fast compared to sluggish conscious perception, we propose a novel conceptual framework in which features of objects, such as their color, are quasi-continuously and unconsciously analyzed with high temporal resolution. Like other features, temporal features, such as duration, are coded as quantitative labels. When unconscious processing is "completed," all features are simultaneously rendered conscious at discrete moments in time, sometimes even hundreds of milliseconds after stimuli were presented.
TL;DR: It is suggested that visual attention span may play a role, but a minor one, at least in this population of dyslexic children, and phonological deficits confirmed, but the results do not support any involvement of visual stress in dyslexia.
Abstract: In this study, we concurrently investigated 3 possible causes of dyslexia-a phonological deficit, visual stress, and a reduced visual attention span-in a large population of 164 dyslexic and 118 control French children, aged between 8 and 13 years old. We found that most dyslexic children showed a phonological deficit, either in terms of response accuracy (92.1% of the sample), speed (84.8%), or both (79.3%). Deficits in visual attention span, as measured by partial report ability, affected 28.1% of dyslexic participants, all of which also showed a phonological deficit. Visual stress, as measured by subjective reports of visual discomfort, affected 5.5% of dyslexic participants, not more than controls (8.5%). Although phonological variables explained a large amount of variance in literacy skills, visual variables did not explain any additional variance. Finally, children with comorbid phonological and visual deficits did not show more severe reading disability than children with a pure phonological deficit. These results (a) confirm the importance of phonological deficits in dyslexia; (b) suggest that visual attention span may play a role, but a minor one, at least in this population; (c) do not support any involvement of visual stress in dyslexia. Among the factors that may explain some differences with previously published studies, the present sample is characterized by very stringent inclusion criteria, in terms of the severity of reading disability and in terms of exclusion of comorbidities. This may exacerbate the role of phonological deficits to the detriment of other factors playing a role in reading acquisition. (PsycINFO Database Record
TL;DR: Direct neural recordings, electrical brain stimulation, and pre-/postsurgical neuropsychological testing provided strong evidence that the lmFG supports an orthographically specific “visual word form” system that becomes specialized for the representation of orthographic knowledge.
Abstract: The nature of the visual representation for words has been fiercely debated for over 150 y. We used direct brain stimulation, pre- and postsurgical behavioral measures, and intracranial electroencephalography to provide support for, and elaborate upon, the visual word form hypothesis. This hypothesis states that activity in the left midfusiform gyrus (lmFG) reflects visually organized information about words and word parts. In patients with electrodes placed directly in their lmFG, we found that disrupting lmFG activity through stimulation, and later surgical resection in one of the patients, led to impaired perception of whole words and letters. Furthermore, using machine-learning methods to analyze the electrophysiological data from these electrodes, we found that information contained in early lmFG activity was consistent with an orthographic similarity space. Finally, the lmFG contributed to at least two distinguishable stages of word processing, an early stage that reflects gist-level visual representation sensitive to orthographic statistics, and a later stage that reflects more precise representation sufficient for the individuation of orthographic word forms. These results provide strong support for the visual word form hypothesis and demonstrate that across time the lmFG is involved in multiple stages of orthographic representation.
15 Apr 2016
TL;DR: An overview on the state of research in the field of machine vision for intelligent vehicles covers the range from advanced driver assistance systems to autonomous driving and addresses computing architectures suited to real-time implementation.
Abstract: Humans assimilate information from the traffic environment mainly through visual perception. Obviously, the dominant information required to conduct a vehicle can be acquired with visual sensors. However, in contrast to most other sensor principles, video signals contain relevant information in a highly indirect manner and hence visual sensing requires sophisticated machine vision and image understanding techniques. This paper provides an overview on the state of research in the field of machine vision for intelligent vehicles. The functional spectrum addressed covers the range from advanced driver assistance systems to autonomous driving. The organization of the article adopts the typical order in image processing pipelines that successively condense the rich information and vast amount of data in video sequences. Data-intensive low-level “early vision” techniques first extract features that are later grouped and further processed to obtain information of direct relevance for vehicle guidance. Recognition and classification schemes allow to identify specific objects in a traffic scene. Recently, semantic labeling techniques using convolutional neural networks have achieved impressive results in this field. High-level decisions of intelligent vehicles are often influenced by map data. The emerging role of machine vision in the mapping and localization process is illustrated at the example of autonomous driving. Scene representation methods are discussed that organize the information from all sensors and data sources and thus build the interface between perception and planning. Recently, vision benchmarks have been tailored to various tasks in traffic scene perception that provide a metric for the rich diversity of machine vision methods. Finally, the paper addresses computing architectures suited to real-time implementation. Throughout the paper, numerous specific examples and real world experiments with prototype vehicles are presented.
01 Jan 2016
TL;DR: It is demonstrated that the study of visual processing abnormalities in schizophrenia offers a unifying perspective on the etiology, development, pathophysiology, and course of the disorder.
Abstract: The purpose of this chapter is to demonstrate that the study of visual processing abnormalities in schizophrenia offers a unifying perspective on the etiology, development, pathophysiology, and course of the disorder. This chapter contains six sections. In the first, I provide a brief overview of the importance and promise of studying vision in schizophrenia. In the second, I provide examples of altered visual experience, in multiple aspects of vision, as reported by patients. The third reviews research and controversies related to the most prominent schizophrenia-related visual task deficits, including their psychophysiological and neurobiological aspects. In the fourth, I introduce the construct of contextual modulation and discuss how excesses and reductions in components of this function, in addition to changes in overall level of stimulus sensitivity, can account for many of the visual task deficits associated with schizophrenia. Informed by all of this evidence, I then briefly return to the issue of what the world looks and feels like for people with schizophrenia, and how this may change across illness phases. The paper concludes with a section on future directions for research in the area of vision and schizophrenia.
TL;DR: This study provides proof of concept that non-invasive real-time computer-based visual feedback compensates for the SPV in DBN, and may be a promising aid for patients suffering from oscillopsia and impaired text reading on screen.
Abstract: Background
Patients with downbeat nystagmus syndrome suffer from oscillopsia, which leads to an unstable visual perception and therefore impaired visual acuity. The aim of this study was to use real-time computer-based visual feedback to compensate for the destabilizing slow phase eye movements.