Sight Research Articles

Visual coding along multiple brain areas

This study focuses on understanding visual coding in multiple brain areas and its implications for neural processing in the visual system. It highlights the use of simultaneous recordings of large neuronal populations to investigate how visual information is encoded and processed in the brain. By studying the activity of multiple brain areas, the paper aims to uncover the mechanisms underlying brain-wide visual perception and provide insights into the neural basis of visual processing. The findings of this research contribute to the broader field of neuroscience and have implications for understanding visual disorders and developing therapeutic interventions

Activity-dependent synapse clustering underlies eye-specific competition in the developing retinogeniculate system.

Co-active synaptic connections are often spatially clustered to facilitate local dendritic computations underlying learning, memory, and basic sensory processing. In the mammalian visual system, retinal ganglion cell (RGC) axons converge to form clustered synaptic inputs that enable local signal integration in the dorsal lateral geniculate nucleus (dLGN) of the thalamus. While visual experience promotes retinogeniculate synapse clustering after eye-opening, the earliest events in cluster formation prior to visual experience are unknown. Here, using volumetric super-resolution single-molecule localization microscopy and eye-specific labeling of developing retinogeniculate synapses in mice, we show that synaptic clustering is eye-specific and activity-dependent during retinogeniculate refinement in the first postnatal week. We identified a subset of retinogeniculate synapses with multiple active zones that are surrounded by like-eye synapses and depleted of synapse clustering from the opposite eye. In mutant mice with disrupted spontaneous retinal wave activity, synapses with multiple active zones still form, but do not exhibit the synaptic clustering seen in controls. These results highlight a role for spontaneous retinal activity in regulating eye-specific synaptic clustering in circuits essential for visual perception and behavior.

Bridging the gap in talent: A framework for interdisciplinary research on autism spectrum disorder persons in accounting and information systems

The accounting profession continues to face a talent shortage. In addition, the skills and demands of accountants continue to evolve as technology changes. At the same time, many autism spectrum disorder (ASD) persons remain unemployed or underemployed. Companies, including the Big 4, have begun hiring programs looking at a diverse set of individuals, including ASD persons. General anecdotes suggest ASD persons are thriving in terms of innovation, efficiency, and productivity. While neurotypical (NT) persons have been shown in multiple studies to rely on simple heuristics, which reduce judgment quality, we argue that there are neuropsychological differences in visual perception and attention that play a vital role in why ASD persons may be less susceptible to heuristics and biases; hence, increasing judgment quality. This paper presents an interdisciplinary research framework that adapts the human information processor model of cognition to bring together the judgment and decision-making, autism, and neuropsychological literatures to focus on areas where ASD persons may have judgment-related advantages over NT persons. We discuss the framework and present example research questions for two areas where ASD accountants are performing tasks: cybersecurity and software development. Our framework presents a guide for future empirical research where accounting academics can lead an interdisciplinary effort to support company inclusion programs of ASD persons and, more broadly, of neurodivergent persons.

Evolution of the “4-D Approach” to Dynamic Vision for Vehicles

Spatiotemporal models for the 3-D shape and motion of objects allowed large progress in the 1980s in visual perception of moving objects observed from a moving platform. Despite the successes demonstrated with several vehicles, the “4-D approach” has not been accepted generally. Its advantage is that only the last image of the sequence needs to be analyzed in detail to allow the full state vectors of moving objects, including their velocity components, to be reconstructed by the feedback of prediction errors. The vehicle carrying the cameras can, thus, together with conventional measurements, directly create a visualization of the situation encountered. In 1994, at the final demonstration of the project PROMETHEUS, two sedan vehicles using this approach were the only ones worldwide capable of driving autonomously in standard heavy traffic on three-lane Autoroutes near Paris at speeds up to 130 km/h (convoy driving, lane changes, passing). Up to ten vehicles nearby could be perceived. In this paper, the three-layer architecture of the perception system is reviewed. At the end of the 1990s, the system evolved from mere recognition of objects in motion, to understanding complex dynamic scenes by developing behavioral capabilities, like fast saccadic changes in the gaze direction for flexible concentration on objects of interest. By analyzing motion of objects over time, the situation for decision making was assessed. In the third-generation system “EMS-vision” behavioral capabilities of agents were represented on an abstract level for characterizing their potential behaviors. These maneuvers form an additional knowledge base. The system has proven capable of driving in networks of minor roads, including off-road sections, with avoidance of negative obstacles (ditches). Results are shown for road vehicle guidance. Potential transitions to a robot mind and to the now-favored CNN are touched on.

Anticipatory and evoked visual cortical dynamics of voluntary temporal attention.

We can often anticipate the precise moment when a stimulus will be relevant for our behavioral goals. Voluntary temporal attention, the prioritization of sensory information at task-relevant time points, enhances visual perception. However, the neural mechanisms of voluntary temporal attention have not been isolated from those of temporal expectation, which reflects timing predictability rather than relevance. Here we use time-resolved steady-state visual evoked responses (SSVER) to investigate how temporal attention dynamically modulates visual activity when temporal expectation is controlled. We recorded magnetoencephalography while participants directed temporal attention to one of two sequential grating targets with predictable timing. Meanwhile, a co-localized SSVER probe continuously tracked visual cortical modulations both before and after the target stimuli. We find that in the pre-target period, the SSVER gradually ramps up as the targets approach, reflecting temporal expectation. Furthermore, we find a low-frequency modulation of the SSVER, which shifts approximately half a cycle in phase according to which target is attended. In the post-target period, temporal attention to the first target transiently modulates the SSVER shortly after target onset. Thus, temporal attention dynamically modulates visual cortical responses via both periodic pre-target and transient post-target mechanisms to prioritize sensory information at precise moments.

Analysis of Influencing Factors of Green View Index Based on Street View Segmentation

Abstract. The calculation of green view index in street view image has become an emerging choice for many researchers. This article proposes a PSAT-Net semantic segmentation model and specifically designs a pyramid pooling attention module. This module cleverly integrates channel and spatial information, implicitly implementing a three-dimensional attention mechanism, and significantly improving performance indicators. By performing detailed semantic segmentation on street view image, this article deeply analyzes the impact of different street view parameter configurations on green view index, and studies in detail how these parameters affect the visual perception of urban greening. The experiment found that: (1) The Green view index(GVI) of most sampling points decreases as the FOV increases. The increase in GVI is mainly affected by the crown width and pitch angle, and the high-value points of GVI are more easily affected. (2) Pitch angle usually causes a slight increase in GVI, which is mainly affected by the distribution of tall trees and low vegetation. Tall trees cause an increase in GVI, while low vegetation causes a decrease in GVI. Similarly, high value points of GVI are more likely to produce larger differences. (3) The Heading parameter has no obvious impact on the overall visual impact of GVI. The difference in independent sampling points comes from the angle between the lens acquisition direction and the road forward direction.These findings not only enrich the theoretical connotation of green view index research, but also provide valuable references for practical applications.

Effects of outlier and familiar context in trend-line estimates in scatterplots.

Lately, there has been a growing fascination with blending research on visualizing data and understanding how our basic visual perception works. Taking this path, this research delved into the connection between ensemble perception, which involves quickly and accurately grasping essential information from sets of visually similar objects, and how we process scatterplots. Across two experiments, we aimed to answer a couple of connected questions. First, we investigated whether having an outlier in a scatterplot affects how people draw trend-line estimates. Second, we explored whether what we are familiar with and the presence of outliers that match the trend affect how we draw trend-line estimates in scatterplots. In both experiments, we showed participants scatterplots for a short time, manipulating whether there were outliers or not. Then, using a computer mouse, participants drew their trend-line estimates. By comparing what they drew with possible trend-line solutions, we discovered that when there is no context, the outlier and the other points in a scatterplot are seen as equally important in drawing the trend-line estimate. But when the scatterplot depicted a familiar context and the outlier fitted the trend, people tended to give more weight to those outlier points in their drawings. This suggested that what we already believe can sway how we draw trend-line estimates even from quickly shown scatterplots.

OPTICAL BLUR AFFECTS DIFFERENTLY ON AND OFF VISUAL PATHWAYS.

Optical blur affects ON and OFF pathways differently.Blur expands ON but shrinks OFF receptive fields, weakening OFF more than ON signals.Magnification from positive blur reduces OFF more than ON signal strength and latency.ON-OFF response balance can signal retinal image quality and guide eye growth. Pons et al. demonstrate that optical blur affects differently the receptive field properties and responses from ON and OFF visual pathways, revealing a new relation between ON/OFF pathway balance and blurred visual perception. The results highlight a new role of ON and OFF pathways in optimizing retinal image quality that can have implications for the regulation of eye growth.

Cascade contour-enhanced panoptic segmentation for robotic vision perception.

Panoptic segmentation plays a crucial role in enabling robots to comprehend their surroundings, providing fine-grained scene understanding information for robots' intelligent tasks. Although existing methods have made some progress, they are prone to fail in areas with weak textures, small objects, etc. Inspired by biological vision research, we propose a cascaded contour-enhanced panoptic segmentation network called CCPSNet, attempting to enhance the discriminability of instances through structural knowledge. To acquire the scene structure, a cascade contour detection stream is designed, which extracts comprehensive scene contours using channel regulation structural perception module and coarse-to-fine cascade strategy. Furthermore, the contour-guided multi-scale feature enhancement stream is developed to boost the discrimination ability for small objects and weak textures. The stream integrates contour information and multi-scale context features through structural-aware feature modulation module and inverse aggregation technique. Experimental results show that our method improves accuracy on the Cityscapes (61.2 PQ) and COCO (43.5 PQ) datasets while also demonstrating robustness in challenging simulated real-world complex scenarios faced by robots, such as dirty cameras and rainy conditions. The proposed network promises to help the robot perceive the real scene. In future work, an unsupervised training strategy for the network could be explored to reduce the training cost.

Research on a Multi-Scale Clustering Method for Buildings Taking into Account Visual Cognition

Building clustering is a key problem that needs to be solved in the realization of the automatic synthesis of large-scale maps. The selection of different feature and spatial distance calculation methods has a great impact on the clustering results, and the need to manually select appropriate feature and distance metrics leads to the problem of not being able to fully consider the complexity and diversity of buildings. In this paper, we propose a multi-scale clustering method for buildings that takes visual perception into account using the Gestalt principle to simulate how humans classify buildings through visual perception. Moreover, by analyzing the spatial features and texture attributes of buildings, a visual distance is designed to be used as a condition for building classification to assess the similarity between buildings, solving the complexity of manually selecting feature vectors and spatial distances and realizing the adaptive selection of features. Through experimental validation at different scales (macro, meso and micro), the present method is able to achieve the accurate clustering of buildings, and a frequency threshold of 91% is found, which is able to determine the optimal clustering results. The experimental results show that the proposed method can not only fully consider the complexity and diversity of buildings but also effectively support the understanding and analysis of urban spatial structure and provide a scientific decision-making basis for urban planning and management.

Leveraging user-generated content (UGC) in pre-travel behavior: A PLS-SEM and necessary conditions analysis (NCA) approach

Online review reading is almost ritualistic before making any purchases. Building on social cognition theory, uses and gratification theory, and stimulus organism response theory, this study explores user-generated content (UGC) attributes affecting intentions to travel via a mediating variable (destination image). Using partial least squares structural equation modeling (PLS-SEM) analysis and necessary conditions analysis (NCA) on responses ( N = 392) collected online in India, the findings reveal that the perceived novelty of content (PNC) and perceived value of content (PVC) positively influence destination image and behavioral intention. Destination image mediates the relationship between PNC, PVC, and content users’ behavioral intentions. Perceived reliability of content (PRC) positively affects behavioral intention. The findings suggest that adequate PNC, PRC, and PVC levels are necessary to enhance destination image and generate intention to travel. This study contributes to the UGC literature and provides recommendations for stakeholders to improve user experience and enhance destination image perception.

Infographics as a tool for presentation of an organisation in the media space

Infographics are used to display information in a visual form with a graphic component, and have become widespread due to the convergence of the media space, which has transformed the processes of collecting, processing, broadcasting, and, most importantly, presenting information. The purpose of the article is to consider infographics as a media format for self-presentation of an organisation, as a tool for managing its image. The conducted analysis allows us to show a classification of the infographics according to the following criteria: display method, data type, purpose, presentation format, presence of interactivity, and time perspective. The functionality of the infographics is quite wide: these are functions, such as cognitive, analytical, factual, instructive, argumentative, ascertaining, attractive, expressive, aesthetic, identification, decorative, etc. However, some functions are still not sufficiently disclosed. The article examines the function of presenting the infographics in the media space and, based on a rapid survey, shows its importance in terms of informing the audience about the content of the organisation’s activities, revealing the viral potential of the infographics and its impact on the positive perception of the audience. This all points to the possibility of using the infographics as a media format for self-presentation of an organisation in various target communities, as an effective tool for managing image perception.

Mixed reality-based coordinate system for enhancing survey knowledge acquisition: a study on allocentric and egocentric perspectives

ABSTRACT Spatial knowledge, especially survey knowledge, plays a pivotal role in people’s daily spatial tasks. However, the inherently limited visual perception and integration of spatial information often leads to inaccuracies in survey knowledge, with far-reaching consequences that affect performance, emotion, and safety in completing spatial tasks. In this study, we developed a mixed reality (MR)-based coordinate system and conducted a comparative user experiment involving 56 participants to explore whether perceiving metric and global spatial references during navigation may enhance the accuracy of user-acquired spatial knowledge. The experiment encompassed a spatial learning task where participants navigated and learned the selected indoor environment, followed by a desktop-based landmark recalling task to evaluate the accuracy of participants’ acquired survey knowledge. The results revealed the following insights: (1) the use of MR-based coordinate system significantly improved egocentric and allocentric spatial relations in participants’ survey knowledge; (2) participants’ errors in spatial relations among landmarks accumulate over distance, whereas the MR-based coordinate system helps maintain the errors at a consistent level; (3) no significant correlations were found between errors in participants’ survey knowledge and environmental familiarity, suggesting that the aiding effect of the MR-based coordinate system was independent of users’ prior knowledge of the environment; (4) participants reported increased mental demand using the MR-based coordinate system, but this did not result in diminished spatial learning performance or additional time pressure and learning efforts. These empirical results contribute to the future study of spatial knowledge acquisition and the design of spatial learning interfaces in mixed reality.

Generalization in perceptual learning across stimuli and tasks

Perceptual learning, known to improve visual perception, demonstrates the plasticity of brain processes underlying vision. Early studies, using the backward-masked texture discrimination task (TDT), focused on the lack of generalizing learning to stimulus features, relating learning specificity to the selectivity of the brain networks involved in the visual task. Learning was found to be highly specific to the stimulus features, as expected from the processing selectivity found in early visual areas as well as to the task employed in training, pointing to top-down effects. More recent studies demonstrate the generalization of learning to untrained features under specifically designed training procedures. Here we suggest that transfer of learning takes place when the trained and untrained stimuli and task activate overlapping brain processes. We tested the effect of TDT learning, under conditions with and without visual adaptation, on the contrast detection (CD) of localized Gabor targets, either alone or backward masked (BM). At the TDT peripheral-target location, we found that the transfer of learning between TDT to CD and BM occurs under the TDT adaptation condition, but not under the no-adaptation condition, whereas at the TDT center-target location we found that transfer occurs for both conditions. Our results suggest that learning generalization across experimental conditions depends on overlapping neural processes within brain networks, here dominated by the inhibitory effects involved in adaptation and in spatiotemporal masking. Importantly, increased adaptation during training, due to increased stimulus consistency, enabled the transfer of learning to other tasks limited by sensory adaptation.

How do design factors of stacked directional signs affect their visual cognition?

Stacked directional signs are widely used on Chinese roads, but research on them is relatively scarce. This study aimed to investigate the effects of the Language used, Layout format, Amount of information, and use of Separating lines on the visual cognition of stacked directional signs. A visual cognition experiment involving thirty-two drivers was conducted, and the reaction time and response accuracy were recorded. The results revealed that a greater Amount of information was correlated with increased visual cognition reaction times. Bilingual signs exhibited significantly greater reaction times compared to monolingual counterparts, while a simpler Layout format resulted in shorter reaction times. The Language used, Amount of information, Layout format, and use of Separating lines were in decreasing order of importance for visual cognition reaction time, with a high Amount of information having the greatest effect. The implications of these findings can be applied to the design and optimisation of stacked directional signs.

Visual body size estimation in adolescent anorexia nervosa: Behavioural and neurophysiological data suggest intact visual perception and biased emotional attention

Body image disturbance is a key symptom of anorexia nervosa (AN). AN patients report body dissatisfaction and overestimate their own body size in several tasks. This study aimed to clarify whether this overestimation arises from deficits in visual perception. To this end, 36 adolescent restrictive-type AN patients and 42 matched healthy controls performed metric and depictive body size estimation (BSE) tasks. Magneto- and electroencephalography were measured during the size estimation of 66 computer-generated body pictures varying in size from underweight to overweight. AN patients versus controls showed overestimation across self-referential metric and depictive BSE tasks, but similar performance in a depictive BSE task without self-reference and similar early neurophysiological responses. Starting mid-latency (200 ms), AN patients showed relatively more neural activity in response to underweight body pictures and less neural activity in response to higher-weight body pictures in distributed brain regions. A secondary comparison of AN patients with slight vs. distinct overestimation during self-referential BSE uncovered relatively stronger neural responses to body pictures corresponding to the estimated body mass index. These results suggest that body image disturbances in adolescent restrictive-type AN patients depend on self-reference and do not represent a deficit of visual perception, but rather biased emotional attention.

Visual imagination can influence visual perception - towards an experimental paradigm to measure imagination.

During visual imagination, a perceptual representation is activated in the absence of sensory input. This is sometimes described as seeing with the mind's eye. A number of physiological studies indicate that the brain uses more or less the same neural resources for visual perception of sensory information and visual imagination. The intensity of visual imagination is typically assessed with questionnaires, while more objective measures are missing. Aim of the present study was, to test a new experimental paradigm that may allow to objectively quantify imagination. For this, we used priming and adaptation effects during observation of ambiguous figures. Our perception of an ambiguous stimulus is unstable and alternates spontaneously between two possible interpretations. If we first observe an unambiguous stimulus variant (the conditioning stimulus), the subsequently presented ambiguous stimulus can either be perceived in the same way as the test stimulus (priming effect) or in the opposite way (adaptation effect) as a function of the conditioning time. We tested for these conditioning effects (priming and adaptation) using an ambiguous Necker Cube and an ambiguous Letter /Number stimulus as test stimuli and unambiguous variants thereof as conditioning stimuli. In a second experimental condition, we tested whether the previous imagination of an unambiguous conditioning stimulus variant - instead of its observation - can have similar conditioning effects on the subsequent test stimulus. We found no systematic conditioning effect on the group level, neither for the two stimulus types (Necker Cube stimuli and Letter /Number stimuli) nor for the two conditions (Real and Imaginary). However, significant correlations between effects of Real and Imaginary Condition were observed for both stimulus types. The absence of conditioning effects at the group level may be explained by using only one conditioning time, which may fit with individual priming and adaptation constants of some of our participants but not of others. Our strong correlation results indicate that observers with clear conditioning effects have about the same type (priming or adaptation) and intensity of imaginary conditioning effects. As a consequence, not only past perceptual experiences but also past imaginations can influence our current percepts. This is further confirmation that the mechanisms underlying perception and imagination are similar. Our post-hoc qualitative observations from three self-defined aphantasic observers indicate that our paradigm may be a promising objective measure to identify aphantasia.

POI VizNet: New QGIS Tool to construct Visibility Networks in Cities

Abstract. Network science and graph theory offer attractive models to describe urban phenomena and examine cases of urban planning and design. However, none of the existing network analysis tools consider simultaneously human visual perception and locations of urban activities. In this paper we introduce POI VizNet, a new QGIS plug-in that constructs various 2-dimentianal undirected graphs of unobstructed lines of sight. The plug-in integrates an increasing amount of available GIS-based data of Point Of Interest (POI) and visibility into one readily accessible analytical framework. Graphs are created between two types of decision points during urban travel – street intersections and POIs, origins, and destinations of travel, by connecting these potential observer’s decision locations in an open space between buildings. In addition, POI VizNet provides advanced options to build graphs using a predefined viewing distance and perceptual perspective. Visibility graphs are constructed and visualised as new layers in QGIS and delivered as network files suitable for further exploration, analysis, and visualisation in various network software packages.

Combining style generative adversarial networks with particle swarm optimisation-support vector regression to design affective social robot for public health intervention

In order to improve the attractiveness of social robot serving health interventions in public workspace, we propose a product design method with the combination of Style-generative adversarial network (StyleGAN) model and particle swarm optimisation-support vector regression (PSO-SVR). This paper aims to explore the modelling generation method of social robots for health intervention based on artificial intelligence generated content (AIGC) and a mapping model between the shape characteristics and users’ visual perception based on Kansei Engineering (KE). Firstly, to address the defects of typical KE over-reliance on existing samples, we introduce the StyleGAN model in AIGC to learn and train existing robots’ shape samples and generate new robots’ shape sample images. Secondly, the morphological deconstruction method is used to deconstruct the shape features of the new robot sample. Factor analysis (FA) is also used to reduce dimension and cluster emotional words and establish a Likert scale between the shape features of robots and users’ emotional vocabulary. Finally, particle swarm optimisation-support vector regression (PSO-SVR) is used to establish a mapping model between the shape features of social robots and the emotional images of users, thus obtaining the most attractive shape combination scheme. The research results showed that the StyleGAN model in AIGC can be used to assist industrial designers in creative expression and provide rich sample sources for KE; and the PSO-SVR of machine learning can be used to build a mapping model among users’ visual feelings, emotional images and shape features. In the end, we designed an attractive social robot for public health intervention.

Assessing the factors associated with body image perception and quality of life of Palestinian women undergoing breast cancer treatment: a cross-sectional study

BackgroundWorldwide, breast cancer has replaced lung cancer and has become the most commonly diagnosed malignancy. Breast cancer poses a significant burden on the health and quality of life of women and can lead to substantial physical burdens and significant psychological problems, including distress, anxiety, depression, and sexuality-related issues, including negative body image. This study was conducted to assess how women diagnosed with, treated, and/or receiving treatment for breast cancer perceived their body image.MethodsA cross-sectional study design was used to assess body image perceptions among Palestinian women diagnosed with breast cancer. The study was conducted in four different hospitals located in the West Bank of Palestine where women with breast cancer received their treatments or visited for follow-up care in the period between November 2023 and March 2024. An interviewer-administered questionnaire was used to collect the data. Body perception was assessed using the body image self-rating questionnaire for breast cancer (BISQ-BC).ResultsIn this study, 376 women with breast cancer completed the interviewer-administered questionnaire (response rate = 75.2%. The mean age of the women was 44.0 ± 5.3 years. Of the women, 74.7% agreed or strongly agreed that they cared about their body image. On the other hand, 43.1% of the women were not satisfied with their body image and 58.8% did not think that their body image was attractive. Multiple linear regression showed that younger age, employment status, living in cities, marital status, educational level, presence of comorbidities, and longer time passes since diagnosis with breast cancer were associated with the different aspects of body image perception and behavior.ConclusionThe findings of this study indicated that Palestinian women with breast cancer were concerned about their body image. The findings also indicated that breast cancer and its treatment posed a significant burden on the physical and psychosocial well-being of the affected women and impacted the different dimensions of their lives, including their perceptions of their body image. Patients with significant physical and body image changes might benefit from reconstructive surgeries, psychosocial support, cognitive behavioral therapy, and nutritional, and physical activity interventions.