Specific Visual Features Research Articles

Features are more than just filling in the blanks on body size scales

ABSTRACT Despite widespread misperceptions of body size and shape, the specific visual features influencing such judgments remain unclear. This study assessed the contribution of internal body features (e.g., colour, shading) to the accuracy of female body size judgements and whether that contribution varies with body size. Using a bodyline task, 100 female participants evaluated images of female bodies, either depicted as real bodies including internal features (feature stimuli) or images of bodies as silhouettes only (silhouette stimuli). The results showed greater overall errors in body size estimation for silhouettes compared to full feature images. However, the direction of error differed for large and small bodies: small silhouettes were judged larger than their full-feature counterparts, while large silhouettes were judged smaller. These findings emphasize the importance of internal body features in shaping perceptions of female body size and highlight the complexity of judgement accuracy across different body sizes.

Cross-Subject EEG Feedback for Implicit Image Generation.

Generative models are powerful tools for producing novel information by learning from example data. However, the current approaches require explicit manual input to steer generative models to match human goals. Furthermore, how these models would integrate implicit, diverse feedback and goals of multiple users remains largely unexplored. Here, we present a first-of-its-kind system that produces novel images of faces by inferring human goals directly from cross-subject brain signals while study subjects are looking at example images. We report on an experiment where brain responses to images of faces were recorded using electroencephalography in 30 subjects, focusing on specific salient visual features (VFs). Preferences toward VFs were decoded from subjects' brain responses and used as implicit feedback for a generative adversarial network (GAN), which generated new images of faces. The results from a follow-up user study evaluating the presence of the target salient VFs show that the images generated from brain feedback represent the goal of the study subjects and are comparable to images generated with manual feedback. The methodology provides a stepping stone toward humans-in-the-loop image generation.

Optimization-Based Pairwise Interaction Point Process (O-PIPP): A Precise and Universal Retinal Mosaic Modeling Approach.

A retinal mosaic, the spatial organization of a population of homotypic neurons, is thought to sample a specific visual feature into the feedforward visual pathway. The purpose of this study was to propose a universal modeling approach for precisely generating retinal mosaics and overcoming the limitations of previous models, especially in modeling abnormal mosaic patterns under disease conditions. Here, we developed the optimization-based pairwise interaction point process (O-PIPP). It incorporates optimization techniques into previous simulation approaches, enabling directional control of the simulation process according to the user-designed optimization target. For the convenience of the community, we implemented the O-PIPP approach into a Python package and a website application. We showed that the O-PIPP can generate more precise neural spatial patterns of healthy and diseased mosaics compared to previous phenomenological approaches. Notably, through modeling the retinal neural circuitry with O-PIPP-simulated retinitis pigmentosa cone mosaics, we elucidated how the cone mosaic rearrangement impacted the information processing of ganglion cells. The O-PIPP provides a precise and universal tool to simulate realistic mosaics, which could help to investigate the function of retinal mosaics in vision.

Diversity of visual inputs to Kenyon cells of the Drosophila mushroom body

The arthropod mushroom body is well-studied as an expansion layer representing olfactory stimuli and linking them to contingent events. However, 8% of mushroom body Kenyon cells in Drosophila melanogaster receive predominantly visual input, and their function remains unclear. Here, we identify inputs to visual Kenyon cells using the FlyWire adult whole-brain connectome. Input repertoires are similar across hemispheres and connectomes with certain inputs highly overrepresented. Many visual neurons presynaptic to Kenyon cells have large receptive fields, while interneuron inputs receive spatially restricted signals that may be tuned to specific visual features. Individual visual Kenyon cells randomly sample sparse inputs from combinations of visual channels, including multiple optic lobe neuropils. These connectivity patterns suggest that visual coding in the mushroom body, like olfactory coding, is sparse, distributed, and combinatorial. However, the specific input repertoire to the smaller population of visual Kenyon cells suggests a constrained encoding of visual stimuli.

Visual periodicity reveals distinct attentional signatures for face and non-face categories.

Observers can selectively deploy attention to regions of space, moments in time, specific visual features, individual objects, and even specific high-level categories-for example, when keeping an eye out for dogs while jogging. Here, we exploited visual periodicity to examine how category-based attention differentially modulates selective neural processing of face and non-face categories. We combined electroencephalography with a novel frequency-tagging paradigm capable of capturing selective neural responses for multiple visual categories contained within the same rapid image stream (faces/birds in Exp 1; houses/birds in Exp 2). We found that the pattern of attentional enhancement and suppression for face-selective processing is unique compared to other object categories: Where attending to non-face objects strongly enhances their selective neural signals during a later stage of processing (300-500ms), attentional enhancement of face-selective processing is both earlier and comparatively more modest. Moreover, only the selective neural response for faces appears to be actively suppressed by attending towards an alternate visual category. These results underscore the special status that faces hold within the human visual system, and highlight the utility of visual periodicity as a powerful tool for indexing selective neural processing of multiple visual categories contained within the same image sequence.

Crowds and Smiles: Visual Opportunity Structures and the Communication of European Political Leaders During the COVID-19 Pandemic

ABSTRACT The growing interest in political leaders’ visual communication often emphasizes specific visual features without focusing on the driving factors behind these strategies. Our study introduces the Visual Opportunity Structure (VOS) theory, aiming to explain the use of specific visual elements based on their suitability within the socio-political context. We examined the COVID-19 pandemic, analyzing a large dataset (N = 73,379) of Instagram posts by 28 European national party leaders coded through automatic facial and emotional recognition algorithms. The findings reveal a negative link between the use of inappropriate visual features during pandemic waves, like depicting happiness and groups of people, and the severity of the pandemic’s impact. Political leaders significantly reduce these inappropriate visuals during severe waves, reintroducing them in calmer periods. This trend is particularly pronounced among government party leaders. Our research not only unveils a pattern in the visual communication tactics used by political figures during the pandemic but also provides deeper insights into how visual strategies align with the broader context. By shedding light on these nuances, the study contributes to a more comprehensive understanding of visual political communication online.

Mapping model units to visual neurons reveals population code for social behaviour

The rich variety of behaviours observed in animals arises through the interplay between sensory processing and motor control. To understand these sensorimotor transformations, it is useful to build models that predict not only neural responses to sensory input1–5 but also how each neuron causally contributes to behaviour6,7. Here we demonstrate a novel modelling approach to identify a one-to-one mapping between internal units in a deep neural network and real neurons by predicting the behavioural changes that arise from systematic perturbations of more than a dozen neuronal cell types. A key ingredient that we introduce is ‘knockout training’, which involves perturbing the network during training to match the perturbations of the real neurons during behavioural experiments. We apply this approach to model the sensorimotor transformations of Drosophila melanogaster males during a complex, visually guided social behaviour8–11. The visual projection neurons at the interface between the optic lobe and central brain form a set of discrete channels12, and prior work indicates that each channel encodes a specific visual feature to drive a particular behaviour13,14. Our model reaches a different conclusion: combinations of visual projection neurons, including those involved in non-social behaviours, drive male interactions with the female, forming a rich population code for behaviour. Overall, our framework consolidates behavioural effects elicited from various neural perturbations into a single, unified model, providing a map from stimulus to neuronal cell type to behaviour, and enabling future incorporation of wiring diagrams of the brain15 into the model.

Visual properties and perceived restorativeness in green offices: a photographic evaluation of office environments with various degrees of greening.

Office environments play a critical role in employee wellbeing and productivity. While the benefits of incorporating nature into workspaces have been recognized, the specific visual characteristics that contribute to restorativeness remain unclear. This study investigates how visual characteristics of office environments, specifically the presence of greenery and color complexity, are associated with perceived restorativeness. In Study 1, we developed a scale based on Attention Restoration Theory to measure the restorative characteristics of office environments, consisting of three subscales: Being Away, Fascination, and Extent. In Study 2, we used this scale to examine the correlation between the restorative characteristics of offices and the visual properties of office photographs. The results showed that the square root of the percentage of green area, the color fractal dimension, and the brightness fractal predicted perceived restorativeness. Notably, the color fractal dimension often showed a stronger effect than the amount of greenery per se. These findings suggest that both the presence of greenery and the overall complexity of color transitions in office spaces contribute to their restorative potential. Our study provides insights for designing more restorative office environments, emphasizing the importance of not only increasing greenery but also mimicking natural color patterns. The predictive model developed provides a practical tool for estimating the restorative potential of office designs. Although there are limitations such as the use of photographic assessments and the inability to fully explain the Extent component of restorativeness, this study contributes to our understanding of how to create more psychologically supportive work environments.

Automated Visual Analysis for the Study of Social Media Effects: Opportunities, Approaches, and Challenges

ABSTRACT To advance our understanding of social media effects, it is crucial to incorporate the increasingly prevalent visual media into our investigation. In this article, we discuss the theoretical opportunities of automated visual analysis for the study of social media effects and present an overview of existing computational methods that can facilitate this. Specifically, we highlight the gap between the outputs of existing computer vision tools and the theoretical concepts relevant to media effects research. We propose multiple approaches to bridging this gap in automated visual analysis, such as justifying the theoretical significance of specific visual features in existing tools, developing supervised learning models to measure a visual attribute of interest, and applying unsupervised learning to discover meaningful visual themes and categories. We conclude with a discussion about future directions for automated visual analysis in computational communication research, such as the development of benchmark datasets designed to reflect more theoretically meaningful concepts and the incorporation of large language models and multimodal channels to extract insights.

What drives our aesthetic attraction to birds?

In the Anthropocene, the era when the imprint of humans on nature is pervasive across the planet, it is of utmost importance to understand human relationships with other species. The aesthetics of nature, and of species, is one of the values that plays a role in shaping human-nature relationships. Birds are ubiquitous across the world. The beauty of birds exerts a powerful tug on human emotions, and bird-rich areas attract scores of eco-tourists. People naturally find some birds more beautiful or interesting than others, but we currently lack a global understanding of the specifics of what makes a species aesthetically attractive. Here, we used a global citizen-science database on bird attractiveness covering nearly all extant bird species, to show that there are specific visual features that drive our aesthetic appeal for some bird species over others. First, our aesthetic attraction is highest for smaller birds with specific, vivid colors (e.g., blue and red, and departing from brown-grey) and extreme ornaments (a long crest or tail). Second, our aesthetic attraction is highest for species with broad ranges, possibly because such species may be more familiar to us. The features that make us attracted to a particular bird strongly align with broad human visual aesthetic preferences in modern society. Unveiling the visual features underpinning our aesthetic attraction to birds is a critical step towards optimizing conservation (e.g., via conservation marketing) and education campaigns, and leverage the cultural ecosystem service potential of birds.

Top-down predictions of specific visual features in the brain speed up their bottom-up categorizations for perceptual decision

Top-down predictions of specific visual features in the brain speed up their bottom-up categorizations for perceptual decision

Usability and Acceptability of Clinical Dashboards in Aged Care: Systematic Review.

The use of clinical dashboards in aged care systems to support performance review and improve outcomes for older adults receiving care is increasing. Our aim was to explore evidence from studies of the acceptability and usability of clinical dashboards including their visual features and functionalities in aged care settings. A systematic review was conducted using 5 databases (MEDLINE, Embase, PsycINFO, Cochrane Library, and CINAHL) from inception to April 2022. Studies were included in the review if they were conducted in aged care environments (home-based community care, retirement villages, and long-term care) and reported a usability or acceptability evaluation of a clinical dashboard for use in aged care environments, including specific dashboard visual features (eg, a qualitative summary of individual user experience or metrics from a usability scale). Two researchers independently reviewed the articles and extracted the data. Data synthesis was performed via narrative review, and the risk of bias was measured using the Mixed Methods Appraisal Tool. In total, 14 articles reporting on 12 dashboards were included. The quality of the articles varied. There was considerable heterogeneity in implementation setting (home care 8/14, 57%), dashboard user groups (health professionals 9/14, 64%), and sample size (range 3-292). Dashboard features included a visual representation of information (eg, medical condition prevalence), analytic capability (eg, predictive), and others (eg, stakeholder communication). Dashboard usability was mixed (4 dashboards rated as high), and dashboard acceptability was high for 9 dashboards. Most users considered dashboards to be informative, relevant, and functional, highlighting the use and intention of using this resource in the future. Dashboards that had the presence of one or more of these features (bar charts, radio buttons, checkboxes or other symbols, interactive displays, and reporting capabilities) were found to be highly acceptable. A comprehensive summary of clinical dashboards used in aged care is provided to inform future dashboard development, testing, and implementation. Further research is required to optimize visualization features, usability, and acceptability of dashboards in aged care.

Calcium Imaging in Mouse Superior Colliculus.

The superior colliculus (SC), an evolutionarily conserved midbrain structure in all vertebrates, is the most sophisticated visual center before the emergence of the cerebral cortex. It receives direct inputs from ~30 types of retinal ganglion cells (RGCs), with each encoding a specific visual feature. It remains elusive whether the SC simply inherits retinal features or if additional and potentially de novo processing occurs in the SC. To reveal the neural coding of visual information in the SC, we provide here a detailed protocol to optically record visual responses with two complementary methods in awake mice. One method uses two-photon microscopy to image calcium activity at single-cell resolution without ablating the overlaying cortex, while the other uses wide-field microscopy to image the whole SC of a mutant mouse whose cortex is largely undeveloped. This protocol details these two methods, including animal preparation, viral injection, headplate implantation, plug implantation, data acquisition, and data analysis. The representative results show that the two-photon calcium imaging reveals visually evoked neuronal responses at single-cell resolution, and the wide-field calcium imaging revealsneural activity across the entire SC. By combining these two methods, one can reveal the neural coding in the SC at different scales, and such combination can also be applied to other brain regions.

“Shiny Happy People Laughing”: The Protest Paradigm, WUNC, and the Visual Framing of Political Activism

Media coverage of protest, particularly its visual framing, is crucial to the legitimacy and impact of protest movements. Typical patterns in media coverage of protests, which account for discrepancies between how protests are portrayed, are the protest paradigm and WUNC (worthy, united, numbers, commitment). In order to investigate how specific visual items and features of media images showing political protest elicit positive or negative perceptions and annotations by an audience, we study two questions: Which visual features in media images of protest elicit positive or negative perceptions and annotations by an audience? How do these perceptions correspond with the protest paradigm and WUNC, respectively? We answer these questions by conducting a qualitative focus group study with students from a mid-size German university.

User Preference-Based Video Synopsis Using Person Appearance and Motion Descriptions.

During the last decade, surveillance cameras have spread quickly; their spread is predicted to increase rapidly in the following years. Therefore, browsing and analyzing these vast amounts of created surveillance videos effectively is vital in surveillance applications. Recently, a video synopsis approach was proposed to reduce the surveillance video duration by rearranging the objects to present them in a portion of time. However, performing a synopsis for all the persons in the video is not efficacious for crowded videos. Different clustering and user-defined query methods are introduced to generate the video synopsis according to general descriptions such as color, size, class, and motion. This work presents a user-defined query synopsis video based on motion descriptions and specific visual appearance features such as gender, age, carrying something, having a baby buggy, and upper and lower clothing color. The proposed method assists the camera monitor in retrieving people who meet certain appearance constraints and people who enter a predefined area or move in a specific direction to generate the video, including a suspected person with specific features. After retrieving the persons, a whale optimization algorithm is applied to arrange these persons reserving chronological order, reducing collisions, and assuring a short synopsis video. The evaluation of the proposed work for the retrieval process in terms of precision, recall, and F1 score ranges from 83% to 100%, while for the video synopsis process, the synopsis video length compared to the original video is decreased by 68% to 93.2%, and the interacting tube pairs are preserved in the synopsis video by 78.6% to 100%.

Prejavy agresivity vo výtvarnom prejave žiakov mladšieho školského veku

The paper is a theoretical study that characterizes the basic features of the artistic representations of young school-age pupil and the aggressive manifestations that can be found and diagnosed in the artistic representations at this age. In addition to general characteristics, we also present specific visual features that may be suggestive of the artist's expressions of aggression. The knowledge of possible manifestations of aggression in artistic expression is of pedagogical value.

Foxd1-dependent induction of a temporal retinal character is required for visual function.

Appropriate patterning of the retina during embryonic development is assumed to underlie the establishment of spatially localised specialisations that mediate the perception of specific visual features. For example, in zebrafish, an area involved in high acuity vision (HAA) is thought to be present in the ventro-temporal retina. Here, we show that the interplay of the transcription factor Rx3 with Fibroblast Growth Factor and Hedgehog signals initiates and restricts foxd1 expression to the prospective temporal retina, initiating naso-temporal regionalisation of the retina. Abrogation of Foxd1 results in the loss of temporal and expansion of nasal retinal character, and consequent absence of the HAA. These structural defects correlate with severe visual defects, as assessed in optokinetic and optomotor response assays. In contrast, optokinetic responses are unaffected in the opposite condition, in which nasal retinal character is lost at the expense of expanded temporal character. Our study indicates that the establishment of temporal retinal character during early retinal development is required for the specification of the HAA, and suggests a prominent role of the temporal retina in controlling specific visual functions.

Endogenous attention biases transformational apparent motion based on high-level shape representations.

When two pre-existing, separated squares are connected by the sudden onset of a bar between them, viewers do not perceive the bar to appear all at once. Instead, they see an illusory morphing of the original squares over time. The direction of this transformational apparent motion (TAM) can be influenced by endogenous attention deployed before the appearance of the connecting bar. Here, we investigated whether the influence of endogenous attention on TAM results from operations over high-level feature-independent shape representations, or instead over lower level shape representations defined by specific visual features. To do so, we tested the influence of endogenous attention on TAM in first- and second-order displays, which shared common shapes but had different shape-defining attributes (luminance and texture contrast, respectively). In terms of both the magnitude of directional bias and timing, we found that endogenous attention exerted a similar influence on both first- and second-order objects. These results imply that endogenous attention biases the perceived direction of TAM by operating on high-level shape representations that are invariant to the low-level visual features that define them. Our results support a four-stage model of TAM, where a feature encoding stage passes a features-specific layout to a parsing stage that forms discrete, high-level meta-featural shapes, which are then matched and visually interpolated over time.

Atypical gaze patterns in autistic adults are heterogeneous across but reliable within individuals

BackgroundAcross behavioral studies, autistic individuals show greater variability than typically developing individuals. However, it remains unknown to what extent this variability arises from heterogeneity across individuals, or from unreliability within individuals. Here, we focus on eye tracking, which provides rich dependent measures that have been used extensively in studies of autism. Autistic individuals have an atypical gaze onto both static visual images and dynamic videos that could be leveraged for diagnostic purposes if the above open question could be addressed.MethodsWe tested three competing hypotheses: (1) that gaze patterns of autistic individuals are less reliable or noisier than those of controls, (2) that atypical gaze patterns are individually reliable but heterogeneous across autistic individuals, or (3) that atypical gaze patterns are individually reliable and also homogeneous among autistic individuals. We collected desktop-based eye tracking data from two different full-length television sitcom episodes, at two independent sites (Caltech and Indiana University), in a total of over 150 adult participants (N = 48 autistic individuals with IQ in the normal range, 105 controls) and quantified gaze onto features of the videos using automated computer vision-based feature extraction.ResultsWe found support for the second of these hypotheses. Autistic people and controls showed equivalently reliable gaze onto specific features of videos, such as faces, so much so that individuals could be identified significantly above chance using a fingerprinting approach from video epochs as short as 2 min. However, classification of participants into diagnostic groups based on their eye tracking data failed to produce clear group classifications, due to heterogeneity in the autistic group.LimitationsThree limitations are the relatively small sample size, assessment across only two videos (from the same television series), and the absence of other dependent measures (e.g., neuroimaging or genetics) that might have revealed individual-level variability that was not evident with eye tracking. Future studies should expand to larger samples across longer longitudinal epochs, an aim that is now becoming feasible with Internet- and phone-based eye tracking.ConclusionsThese findings pave the way for the investigation of autism subtypes, and for elucidating the specific visual features that best discriminate gaze patterns—directions that will also combine with and inform neuroimaging and genetic studies of this complex disorder.

Periodicity counting in videos with unsupervised learning of cyclic embeddings

• We introduce a new method to measure periodicity on any video, even out of the “daily-life” domain. • Our framework can create a periodic latent representation for any video (conventional or 4D videos) without supervision. • Our method can convert periodic videos into simple periodic signals. • We introduce a robust peak detection and counting algorithm for periodic signals. We introduce a context-agnostic unsupervised method to count periodicity in videos. Current methods estimate periodicity for a specific type of application (e.g. some repetitive human motion). We propose a novel method that provides a powerful generalisation ability since it is not biased towards specific visual features. It is thus applicable to a range of diverse domains that require no adaptation, by relying on a deep neural network that is trained completely unsupervised. More specifically, it is trained to transform the periodic temporal data into some lower-dimensional latent encoding in such a way that it forms a cyclic path in this latent space. We also introduce a novel algorithm that is able to reliably detect and count periods in complex time series. Despite being unsupervised and facing supervised methods with complex architectures, our experimental results demonstrate that our approach is able to reach state-of-the-art performance for periodicity counting on the challenging QUVA video benchmark.