Sight Research Articles

Top-down and bottom-up effects on visual perception during data-based scientific reasoning in the context of population dynamics

ABSTRACT This study investigates the role of visual perception in data-based scientific reasoning within population dynamics. Visual perception involves representational characteristics of stimuli and context-based conceptions, influenced by top-down (contextual assumptions) and bottom-up (data representation) effects. In population dynamics, top-down effects relate to assumptions like natural equilibrium, while bottom-up effects concern how data are represented, typically through line graphs. We assessed pre-service biology teachers’ (N = 20) initial conceptions on population dynamics, categorizing them as either the Balance of Nature (BoN) or Flux of Nature (FoN) metaphors. Participants viewed line graphs depicting population dynamics while eye-tracking technology recorded their visual focus and think-aloud protocols provided verbal data. ANOVA results showed significant differences in eye-tracking metrics between AOI groups of BoN and FoN graphs, but no significant effects between initial conceptions and eye-tracking measures. Heat map analyses revealed that participants focused on visually prominent graph features, especially when interpretation required conceptual knowledge and pattern identification.

SIFusion: Lightweight infrared and visible image fusion based on semantic injection.

The objective of image fusion is to integrate complementary features from source images to better cater to the needs of human and machine vision. However, existing image fusion algorithms predominantly focus on enhancing the visual appeal of the fused image for human perception, often neglecting their impact on subsequent high-level visual tasks, particularly the processing of semantic information. Moreover, these fusion methods that incorporate downstream tasks tend to be overly complex and computationally intensive, which is not conducive to practical applications. To address these issues, a lightweight infrared and visible light image fusion method known as SIFusion, which is based on semantic injection, is proposed in this paper. This method employs a semantic-aware branch to extract semantic feature information, and then integrates these features into the fused features through a Semantic Injection Module (SIM) to meet the semantic requirements of high-level visual tasks. Furthermore, to simplify the complexity of the fusion network, this method introduces an Edge Convolution Module (ECB) based on structural reparameterization technology to enhance the representational capacity of the encoder and decoder. Extensive experimental comparisons demonstrate that the proposed method performs excellently in terms of visual appeal and advanced semantics, providing satisfactory fusion results for subsequent high-level visual tasks even in challenging scenarios.

Wide-angle simulated artificial vision enhances spatial navigation and object interaction in a naturalistic environment

Objective. Vision restoration approaches, such as prosthetics and optogenetics, provide visual perception to blind individuals in clinical settings. Yet their effectiveness in daily life remains a challenge. Stereotyped quantitative tests used in clinical trials often fail to translate into practical, everyday applications. On the one hand, assessing real-life benefits during clinical trials is complicated by environmental complexity, reproducibility issues, and safety concerns. On the other hand, predicting behavioral benefits of restorative therapies in naturalistic environments may be a crucial step before starting clinical trials to minimize patient discomfort and unmet expectations.Approach. To address this, we leverage advancements in virtual reality technology to conduct a fully immersive and ecologically valid task within a physical artificial street environment. As a case study, we assess the impact of the visual field size in simulated artificial vision for common outdoor tasks.Main results. We show that a wide visual angle (45°) enhances participants' ability to navigate and solve tasks more effectively, safely, and efficiently. Moreover, it promotes their learning and generalization capability. Concurrently, it changes the visual exploration behavior and facilitates a more accurate mental representation of the environment. Further increasing the visual angle beyond this value does not yield significant additional improvements in most metrics.Significance. We present a methodology combining augmented reality with a naturalistic environment, enabling participants to perceive the world as patients with retinal implants would and to interact physically with it. Combining augmented reality in naturalistic environments is a valuable framework for low vision and vision restoration research.

Survey on the Impact of Historical Museum Exhibition Forms on Visitors’ Perceptions Based on Eye-Tracking

Exhibition design in museum space is of great significance in enhancing cultural attraction, visitor experience, and heritage conservation. With the growth of spiritual and cultural needs, the construction of museums has increased, but the quality of exhibitions has been improved to a limited extent. In order to explore visitors’ perceptual experience in terms of display forms, this paper combines eye-tracking technology and subjective evaluation to select three display forms, namely, stand-alone display form, combined picture and object form, and combined 3D object and physical form, for the research. Combining field research, eye-tracking experiments, and subjective questionnaire evaluations, this study explores the relationship between visual perception and emotional experience in different display formats. The results showed that (1) the effects of different display formats differed between visual perception and emotional experience, and (2) there is some correlation between visual perception and emotional experience. Specifically, the number of fixations sessions was negatively correlated with fun, richness, comfort, and attractiveness and positively correlated with authenticity, while the average fixation time was positively correlated with the former and negatively correlated with authenticity. Combining the two dimensions, the study concluded that visitors’ experience satisfaction and preference are high in response to the form combining 3D objects and physical objects; the exhibition form of combining pictures and physical objects produces a higher degree of cognition in a short period of time; and the stand-alone display form has the lowest degree of preference, with poor information retention among visitors. It is hoped that this study will provide some reference for the use of exhibition forms in historical museums.

Enhanced visual contrast suppression during peak psilocybin effects: Psychophysical results from a pilot randomized controlled trial.

In visual perception, an effect known as surround suppression occurs wherein the apparent contrast of a center stimulus is reduced when it is presented within a higher-contrast surrounding stimulus. Many key aspects of visual perception involve surround suppression, yet the neuromodulatory processes involved remain unclear. Psilocybin is a serotonergic psychedelic compound known for its robust effects on visual perception, particularly texture, color, object, and motion perception. We asked whether surround suppression is altered under peak effects of psilocybin. Using a contrast-matching task with different center-surround stimulus configurations, we measured surround suppression after 25mg of psilocybin compared with placebo (100mg niacin). Data on harms were collected, and no serious adverse events were reported. After taking psilocybin, participants (n = 6) reported stronger surround suppression of perceived contrast compared to placebo. Furthermore, we found that the intensity of subjective psychedelic visuals induced by psilocybin correlated positively with the magnitude of surround suppression. We note the potential relevance of our findings for the field of psychiatry, given that studies have demonstrated weakened visual surround suppression in both major depressive disorder and schizophrenia. Our findings are thus relevant to understanding the visual effects of psilocybin, and the potential mechanisms of visual disruption in mental health disorders.

Multi-channel neural audio decorrelation using generative adversarial networks

The degree of correlation between the sounds received by the ears significantly influences the spatial perception of a sound image. Audio signal decorrelation is, therefore, a commonly used tool in various spatial audio rendering applications. In this paper, we propose a multi-channel extension of a previously proposed decorrelation method based on generative adversarial networks. A separate generator network is employed for each output channel. All generator networks are optimized jointly to obtain a multi-channel output signal with the desired properties. The training objective includes a number of individual loss terms to control both the input-output and the inter-channel correlation as well as the quality of the individual output channels. The proposed approach is trained on music signals and evaluated both objectively and through formal listening tests. Thereby, a comparison with two classical signal processing-based multi-channel decorrelators is performed. Additionally, the influence of the number of output channels, the individual loss term weightings, and the employed training data on the proposed method’s performance is investigated.

Predictably manipulating photoreceptor light responses to reveal their role in downstream visual responses.

Computation in neural circuits relies on the judicious use of nonlinear circuit components. In many cases, multiple nonlinear components work collectively to control circuit outputs. Separating the contributions of these different components is difficult, and this limits our understanding of the mechanistic basis of many important computations. Here, we introduce a tool that permits the design of light stimuli that predictably alter rod and cone phototransduction currents - including stimuli that compensate for nonlinear properties such as light adaptation. This tool, based on well-established models for the rod and cone phototransduction cascade, permits the separation of nonlinearities in phototransduction from those in downstream circuits. This will allow, for example, direct tests of how adaptation in rod and cone phototransduction affects downstream visual signals and perception.

Predictably manipulating photoreceptor light responses to reveal their role in downstream visual responses

Computation in neural circuits relies on the judicious use of nonlinear circuit components. In many cases, multiple nonlinear components work collectively to control circuit outputs. Separating the contributions of these different components is difficult, and this limits our understanding of the mechanistic basis of many important computations. Here, we introduce a tool that permits the design of light stimuli that predictably alter rod and cone phototransduction currents – including stimuli that compensate for nonlinear properties such as light adaptation. This tool, based on well-established models for the rod and cone phototransduction cascade, permits the separation of nonlinearities in phototransduction from those in downstream circuits. This will allow, for example, direct tests of how adaptation in rod and cone phototransduction affects downstream visual signals and perception.

A Study on the Visual Perception of Cultural Value Characteristics of Traditional Southern Fujian Architecture Based on Eye Tracking

The traditional Southern Fujian architecture serves as a reflection of the region’s deeply rooted cultural values. However, research on the architectural and cultural heritage of Southern Fujian remains scarce. Accordingly, this study employs eye-tracking technology to experiment with 20 individuals with local cultural backgrounds and 20 without it to investigate the differences in the perception of the architectural heritage of Southern Fujian by individuals with disparate cultural backgrounds. The findings indicate that distinct groups evince disparate visual perceptions of the architectural and cultural heritage of Southern Fujian. The participants’ attention was divided into two categories: patterns and shapes and calligraphy and text. Non-locals were primarily drawn to patterns and shapes, whereas locals demonstrated a greater interest in the calligraphy and text category. The extraction of cultural information from calligraphic characters requires greater understanding and more time spent within the culture. Knowledge of local cultural backgrounds is helpful to better understand such information. This highlights the potential impact of Southern Fujian architecture on the local population. These findings provide a compelling rationale for the incorporation of multicultural elements in contemporary architectural design while also advocating for the preservation and advancement of Southern Fujian architectural heritage.

EXPRESS: Racial Inequity in Donation-based Crowdfunding Platforms: the Role of Facial Emotional Expressiveness

Donation-based crowdfunding platforms often claim to pursue equitable outcomes for all beneficiaries, yet many face criticism for failing to do so across different demographic profiles. In response, platform managers are eager to understand how these inequities emerge and explore solutions to address them. In this research, we show that the degree of facial emotional expressiveness of beneficiaries in uploaded images can differentially impact donation amounts for White vs Black beneficiaries. Drawing on social vision theory, we propose that facial emotional expressiveness in images combined with the race of the faces activates racial stereotypes of emotion expression that result in differential donation amounts to Black and White individuals. Analyzing a sample of 4,153 campaigns from GoFundMe between June 2021 and September 2022, along with a follow-up experiment, we find that higher facial emotional expressiveness is associated with significantly lower donation amounts for Black compared to White beneficiaries. Further exploring our moderating constructs reveals that the use of call-to-action cues, affective messaging, and race-gender homophily cues can attenuate the activation of stereotypes and therefore reduce differences in donation amounts between racial groups. Based on these findings, we offer targeted recommendations for platform managers to help reduce racial inequities in crowdfunding outcomes.

CdSe quantum dots photoelectric memristors for simulating biological visual system behavior

The visual system is the most important in the biological neurosensory system, so simulation of the biological visual system is the key for developing artificial nervous system. This work incorporates a CdSe quantum dot layer into silk fibroin based memristor device, endowing the devices with light-responsive capability. Furthermore, interesting phenomena were observed when the device operated as artificial synapses: excessive voltage stimulation led to a reduction in synaptic weight compared to the responses observed under normal electrical stimulation. This behavior mirrors sensations associated with pain, neuroprotection, and potential injuries to the neural system. At the end, we designed a visual perception system, simulating processes of the biological light intensity perception and the visual degradation response under intense light stimulation. Our research demonstrates the feasibility of constructing an artificial visual nervous system using a hardware system based on memristors.

Enhancing Visual Perception in Immersive VR and AR Environments: AI-Driven Color and Clarity Adjustments Under Dynamic Lighting Conditions

The visual fidelity of virtual reality (VR) and augmented reality (AR) environments is essential for user immersion and comfort. Dynamic lighting often leads to chromatic distortions and reduced clarity, causing discomfort and disrupting user experience. This paper introduces an AI-driven chromatic adjustment system based on a modified U-Net architecture, optimized for real-time applications in VR/AR. This system adapts to dynamic lighting conditions, addressing the shortcomings of traditional methods like histogram equalization and gamma correction, which struggle with rapid lighting changes and real-time user interactions. We compared our approach with state-of-the-art color constancy algorithms, including Barron’s Convolutional Color Constancy and STAR, demonstrating superior performance. Experimental results from 60 participants show significant improvements, with up to 41% better color accuracy and 39% enhanced clarity under dynamic lighting conditions. The study also included eye-tracking data, which confirmed increased user engagement with AI-enhanced images. Our system provides a practical solution for developers aiming to improve image quality, reduce visual discomfort, and enhance overall user satisfaction in immersive environments. Future work will focus on extending the model’s capability to handle more complex lighting scenarios.

Peak Alpha Frequency, Visual Perception, and Cognition in Schizophrenia

Peak Alpha Frequency, Visual Perception, and Cognition in Schizophrenia

A novel infrared and visible image fusion network based on cross-modality reinforcement and multi-attention fusion strategy

Infrared and visible image fusion focuses on the integration of complementary information, whose fused results with abundant texture details and salient targets can cater for human visual observation well. However, the loss of some important details is still a challenge in the image fusion process. In this paper, a novel method based on cross-modality reinforcement module and multi-attention fusion strategy is proposed to boost the end-to-end CNN backbone. Specifically, a cross-modality architecture is applied to compensate the spectral differences from heterogeneous images; and the multi-scale strip pooling is utilized as a further feature representation tool to model long-rang independencies precisely; then, a detail injection block is devised to fulfill the enhancement requirements of texture-contrast and target intensity; sequentially, a multi-attention fusion module is proposed to integrate features progressively. Extensive comparative experiments are conducted on several datasets to demonstrate the superiority of the proposed method in both quantitative metrics and visual perception, such as the average of visual information fidelity metric based on all the experiment samples reach to 0.964.

Eye-movement-prompted large image captioning model

Pretrained large vision-language models have shown outstanding performance on the task of image captioning. However, owing to the insufficient decoding of image features, existing large models sometimes lose important information, such as objects, scenes, and their relationships. In addition, the complex ”black-box” nature of these models makes their mechanisms difficult to explain. Research shows that humans learn richer representations than machines do, which inspires us to improve the accuracy and interpretability of large image captioning models by combining human observation patterns. We built a new dataset, called saliency in image captioning (SIC), to explore relationships between human vision and language representation. One thousand images with rich context information were selected as image data of SIC. Each image was annotated with five caption labels and five eye-movement labels. Through analysis of the eye-movement data, we found that humans efficiently captured comprehensive information for image captioning during their observations. Therefore, we propose an eye-movement-prompted large image captioning model, which is embedded with two carefully designed modules: the eye-movement simulation module (EMS) and the eye-movement analyzing module (EMA). EMS combines the human observation pattern to simulate eye-movement features, including the positions and scan paths of eye fixations. EMA is a graph neural network (GNN) based module, which decodes graphical eye-movement data and abstracts image features as a directed graph. More accurate descriptions can be predicted by decoding the generated graph. Extensive experiments were conducted on the MS-COCO and NoCaps datasets to validate our model. The experimental results showed that our network was interpretable, and could achieve superior results compared with state-of-the-art methods, i.e., 84.2% BLEU-4 and 145.1% CIDEr-D on MS-COCO Karpathy test split, indicating its strong potential for use in image captioning.

Phototherapy as the tool of emotional identification development of adolescents

The results of the empirical stage statistically proved that phototherapy is one of the effective tools of psycho-pedagogical assistance to adolescents who experience difficulties associated with emotional identification. We diagnosed the level of formation of this phenomenon within the sample of 240 respondents who are university and school students aged 17 to 20 years female and male. A number of psycho-diagnostic methods were used in the study: the method 'Toron to alexithymia scale (TAS-26)” and the questionnaire “self-attitude.” Summarizing the results, we state that most of the studied sample is in a state of emotional well-being but emotional discomfort occurs among some respondents with certain levels of propensity for alexithymia (22.5%). The research proves the positive impact of phototherapy on adolescents' emotional identification development through photographing and creating visual pictures or direct perception and interpretation of images.

Discussion on effect of observation circumstances for image in digital radiography

The discriminability of the IQI in the film radiography is affected by the observation circumstance and the luminance of film viewer. ISO17636-2 states that the images of digital radiograph shall be examined in a dimmed room. However, it does not describe requirements for the observation environment in digital radiography, despite the fact that the environment is similar to that of a normal professional PC and the display of the images can be easily adjusted. In this report, the influence of the observation environment in digital radiography was compared as the parameters the wire diameter and the WL value of a display adjustment value when the minimum wire diameter of the image quality indicator (IQI) was percepted, using images of welded steel at t25mm and t50mm. For welded steel of t25mm, differences due to SNRN values and differences due to the observation environment of light and dark room were compared. For t50mm, differences due to the presence or absence of the observation mask for the single wire of wire IQI which is places in front of the display, differences due to the resolution and brightness of the PC monitor, and differences when the WL was adjusted arbitrarily or fixed were compared. For the Digital Radiographic testing (RT-D), the basic spatial resolutions (SRb) were obtained by measuring the profile and applying of visual observation with the duplex wire type IQI. On the other hand, when observing the image of wire type IQI, the observation ways that one was obtain the perceptible image for all wires and another was to get the perceptible single wire image by using the observation mask for watching each wire were applied. Moreover, the influence of window width (WW) and window level (WL) adjustment on the discrimination of wire-type IQI was examined by quantifying it as WW/WL. As a result, it was confirmed that the discriminality in the dimmed room and the adjustment of WW and WL were effective. In addition, using the observation a mask showed that the perception of the adjacent wire image affected observer cognition in IQI discrimination during image observation. When using viewer software profiling, SRb measurements were not affected by the observation environment, but the visual observation was affected by the environment, and when observing images in the dimmed room with RT-D, it was found that adjusting WW and WL had a greater effect on discrimination for images with low SNRN and low contrast.

Ordinal information, but not metric information, matters in binding feature with depth location in three-dimensional contexts.

A basic function of human visual perception is the ability to recognize and locate objects in the environment. It has been shown that two-dimensional (2D) location can reliably bias judgments on object identity (spatial congruency bias; Golomb et al., 2014), suggesting that 2D location information is automatically bound with object features to induce such a bias. Although the binding problem of feature and location has been vigorously studied under various 2D settings, it remains unclear how depth location can be bound with object features in a three-dimensional (3D) setting. Here we conducted five experiments in various 3D contexts using the congruency bias paradigm, and found that changes of object's depth location could influence perceptual judgments on object features differently depending on whether its relative depth order with respect to others changed or not. Experiments 1 and 2 showed that the judgments on an object's color could be affected by changes in its ordinal depth, but not by changes in its absolute metric depth. Experiment 3 showed that the bias was asymmetric-changes in an object's color did bias the judgments on metric-depth location, but not if its depth order had changed. Experiments 4 and 5 investigated whether these findings could be generalized to a peripersonal near space and a large-scale far space, respectively, using more ecological virtual environments. Our findings suggest that ordinal depth plays a special role in feature-location binding: an object may be automatically bound with its relative depth relation, but not with its absolute metric-depth location. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Visual Perceptual Confidence: Exploring Discrepancies Between Self-reported and Actual Distance Perception In Virtual Reality.

Virtual Reality (VR) systems are widely used, and it is essential to know if spatial perception in virtual environments (VEs) is similar to reality. Research indicates that users tend to underestimate distances in VR. Prior work suggests that actual distance judgments in VR may not always match the users self-reported preference of where they think they most accurately estimated distances. However, no explicit investigation evaluated whether user preferences match actual performance in a spatial judgment task. We used blind walking to explore potential dissimilarities between actual distance estimates and user-selected preferences of visual complexities, VE conditions, and targets. Our findings show a gap between user preferences and actual performance when visual complexities were varied, which has implications for better visual perception understanding, VR applications design, and research in spatial perception, indicating the need to calibrate and align user preferences and true spatial perception abilities in VR.

VPRF: Visual Perceptual Radiance Fields for Foveated Image Synthesis.

Neural radiance fields (NeRF) has achieved revolutionary breakthrough in the novel view synthesis task for complex 3D scenes. However, this new paradigm struggles to meet the requirements for real-time rendering and high perceptual quality in virtual reality. In this paper, we propose VPRF, a novel visual perceptual based radiance fields representation method, which for the first time integrates the visual acuity and contrast sensitivity models of human visual system (HVS) into the radiance field rendering framework. Initially, we encode both the appearance and visual sensitivity information of the scene into our radiance field representation. Then, we propose a visual perceptual sampling strategy, allocating computational resources according to the HVS sensitivity of different regions. Finally, we propose a sampling weight-constrained training scheme to ensure the effectiveness of our sampling strategy and improve the representation of the radiance field based on the scene content. Experimental results demonstrate that our method renders more efficiently, with higher PSNR and SSIM in the foveal and salient regions compared to the state-of-the-art FoV-NeRF. The results of the user study confirm that our rendering results exhibit high-fidelity visual perception.