Visual Information Processing Research Articles

Alterations in center-surround contrast suppression in patients with major depressive disorder.

Previous pattern electroretinogram studies indicate reduced retinal contrast gain in patients with a major depressive disorder (MDD) which may contribute to alterations in visual perception. In line, psychophysical investigations reported elevated contrast thresholds in MDD. This study aims to gain insights into higher-level processing of visual information in MDD by evaluating contrast suppression. We examined contrast suppression of 21 MDD patients and 23 healthy controls (HC) using four different stimulus conditions (spatial frequencies 6.3 and 12.6cpd at 30% and 60% background contrast) in a psychophysical test. Participants were instructed to adjust perceived contrasts between two vertical target patches, embedded in a horizontally or vertically oriented grid background. With finer stimulus gratings, MDD patients exhibited less contrast suppression compared to HC, particularly when the stimulus contrast was high (p = 0.006; MDD vs. HC = - 45%). Contrast suppression in the HC group was significantly reduced for the coarse compared to the fine grating, while contrast suppression scores in MDD did not change with the spatial properties of the stimulus. The reduced contrast suppression in patients with MDD supports the hypothesis of altered dopaminergic neurotransmission and could be attributed to alterations in the retinal receptive fields or in dysfunctional adaptation mechanisms in depression.

An experimental demonstration of neuromorphic sensing of chemical species using electro-optical reservoir computing.

A chemical discrimination system based on photonic reservoir computing is demonstrated experimentally for the first time. The system is inspired by the way humans perceive and process visual sensory information. The electro-optical reservoir computing system is a photonic analogue of the human nervous system with the read-out layer acting as the 'brain', and the sensor that of the human eye. A task-specific optimisation of the system is implemented, and the performance of the system for the discrimination between three chemicals is presented. The results are compared to the previously published numerical simulation (Anufriev et al. in Opt Mater Express 12:1767-1783, 2022, 10.1364/OME.449036). This publication provides a feasibility assessment and a demonstration of a practical realisation of photonic reservoir computing for a new neuromorphic sensing system - the next generation sensor with a built-in 'intelligence' which can be trained to 'understand' and to make a real time sensing decision based on the training data.

The Evolution of Data Visualization Tools and Their Impact on Business Decision-Making : A Historical Analysis

This article examines the transformative evolution of data visualization tools and their profound impact on business decision-making processes. Through a comprehensive analysis of historical developments, theoretical frameworks, and technological advancements, the article explores how visualization tools have progressed from basic manual charts to sophisticated AI-powered analytical platforms. The investigation encompasses the cognitive foundations of visual information processing, the emergence of first-generation Business Intelligence tools, and the current landscape of modern visualization platforms. Particular attention is given to the integration of artificial intelligence in data visualization, including automated generation, pattern recognition, and predictive analytics capabilities. The article presents both quantitative and qualitative assessments of these tools' impact on organizational decision-making, highlighting significant improvements in decision speed, accuracy, and user engagement. The article reveal substantial benefits in terms of ROI, decision-making efficiency, and organizational adoption patterns. The article concludes by examining emerging trends, including the potential of extended reality technologies and edge computing, while acknowledging current limitations and providing recommendations for future research directions in this rapidly evolving field.

Functional localization of the human auditory and visual thalamus using a thalamic localizer functional magnetic resonance imaging task

Abstract Functional magnetic resonance imaging (fMRI) of the auditory and visual sensory systems of the human brain is an active area of investigation in the study of human health and disease. The medial geniculate nucleus (MGN) and lateral geniculate nucleus (LGN) are key thalamic nuclei involved in the processing and relay of auditory and visual information, respectively, and are the subject of blood-oxygen-level-dependent (BOLD) fMRI studies of neural activation and functional connectivity in human participants. However, localization of BOLD fMRI signal originating from neural activity in MGN and LGN remains a technical challenge, due, in part, to the poor definition of boundaries of these thalamic nuclei in standard T1-weighted and T2-weighted magnetic resonance imaging sequences. Here, we report the development and evaluation of an auditory and visual sensory thalamic localizer (TL) fMRI task that produces participant-specific functionally-defined regions of interest (fROIs) of both MGN and LGN, using 3 Tesla multiband fMRI and a clustered-sparse temporal acquisition sequence, in less than 16 minutes of scan time. We demonstrate the use of MGN and LGN fROIs obtained from the TL fMRI task in standard resting-state functional connectivity (RSFC) fMRI analyses in the same participants. In RSFC analyses, we validated the specificity of MGN and LGN fROIs for signals obtained from primary auditory and visual cortex, respectively, and benchmarked their performance against alternative atlas- and segmentation-based localization methods. The TL fMRI task and analysis code (written in Presentation and MATLAB, respectively) have been made freely available to the wider research community.

Automated Fiber Quantification Analysis Identifies Tract-specific Microstructural Alterations in Brain in Intermittent exotropia

BackgroundGrowing evidence of neuroimaging has indicated brain microstructural abnormalities in comitant strabismus. Nonetheless, few studies have investigated neuropathological alterations in patients with intermittent exotropia (IXT). This study aimed at examining the characteristics of brain microstructure along major fiber tracts in IXT patients using an automated fiber quantification analysis. MethodsA total of 25 patients with IXT as well as 25 healthy participants matched for age and gender finished the diffusion tensor imaging scanning and the ophthalmic examination. Automated fiber quantification analysis of 20 major fiber tracts was carried out for IXT patients and healthy subjects, respectively. Diffusion metrics of 100 equidistant nodes resampled along each tract were measured for every subject and compared between two groups. Effect size analysis was performed to identify the most affected fiber tracts in IXT. ResultsWidely declined mean diffusivity was noted in IXT along major tracts containing bilateral thalamic radiations, bilateral corticospinal fasciculi, bilateral cingulum cingulate, left inferior fronto-occipital fasciculus, right arcuate fasciculus and superior longitudinal fasciculus. Local reduction in fractional anisotropy was observed in IXT along left cingulum hippocampus, right inferior longitudinal fasciculus and right uncinate fasciculus, in contrast to the regionally increased fractional anisotropy along bilateral thalamic radiation, left corticospinal tract and left arcuate fasciculus. Among the tracts with significantly changed diffusion metrics in IXT, right inferior longitudinal fasciculus was the most affected one in fractional anisotropy while left thalamic radiation was the most influenced one in mean diffusivity. ConclusionsAbnormalities in microstructural properties along visual-related fiber tracts are likely to contribute to difficulties in visual information processing in IXT patients, which could serve as the neural basis of underlying pathological mechanism of IXT.

Human-centric Image Rendering for Natural and Comfortable Viewing--Image Optimization Based on Human Visual Information Processing Models

Human-centric Image Rendering for Natural and Comfortable Viewing--Image Optimization Based on Human Visual Information Processing Models

Genetic and environmental contributions to individual differences in visual attention and oculomotor control in early infancy.

Infants differ in their level of eye movement control, which at the extreme could be linked to autism. We assessed eye movements in 450 twins (225 pairs, 57% monozygotic, 46% female, aged 5-6 months) using the gap-overlap eye-tracking task. Shorter latency in the gap condition was associated with having more parent-rated autistic traits at 2 years. Latency across the task's three conditions was primarily explained by one highly heritable latent factor likely representing individual differences in basic oculomotor efficiency and/or in visual information processing. Additionally, disengagement of attention was linked to unique genetic factors, suggesting that genetic factors involved in visual attention are different from those involved in basic visual information processing and oculomotor efficiency.

Modeling saccade reaction time in marmosets: the contribution of earlier visual response and variable inhibition.

Marmosets are expected to serve as a valuable model for studying the primate visuomotor system due to their similar oculomotor behaviors to humans and macaques. Despite these similarities, differences exist; challenges in training marmosets on tasks requiring suppression of unwanted saccades, having consistently shorter, yet more variable saccade reaction times (SRT) compared to humans and macaques. This study investigates whether the short and variable SRT in marmosets is related to differences in visual signal transduction and variability in inhibitory control. We refined a computational SRT model, adjusting parameters to better capture the marmoset SRT distribution in a gap saccade task. Our findings indicate that visual information processing is faster in marmosets, and that saccadic inhibition is more variable compared to other species.

Time courses of brain plasticity underpinning visual motion perceptual learning

Visual perceptual learning (VPL) refers to a long-term improvement of visual task performance through training or experience, reflecting brain plasticity even in adults. In human subjects, VPL has been mostly studied using functional magnetic resonance imaging (fMRI). However, due to the low temporal resolution of fMRI, how VPL affects the time course of visual information processing is largely unknown. To address this issue, we trained human subjects to perform a visual motion direction discrimination task. Their behavioral performance and magnetoencephalography (MEG) signals responding to the motion stimuli were measured before, immediately after, and two weeks after training. Training induced a long-lasting behavioral improvement for the trained direction. Based on the MEG signals from occipital sensors, we found that, for the trained motion direction, VPL increased the motion direction decoding accuracy, reduced the motion direction decoding latency, enhanced the direction-selective channel response, and narrowed the tuning profile. Following the MEG source reconstruction, we showed that VPL enhanced the cortical response in early visual cortex (EVC) and strengthened the feedforward connection from EVC to V3A. These VPL-induced neural changes co-occurred in 160–230 ms after stimulus onset. Complementary to previous fMRI findings on VPL, this study provides a comprehensive description on the neural mechanisms of visual motion perceptual learning from a temporal perspective and reveals how VPL shapes the time course of visual motion processing in the adult human brain.

Efficiency of the method of forming special knowledge and skills in the process of physical training of cadets

The article theoretically substantiates, develops and experimentally tests the method of formation of special knowledge and skills of future officers in the process of physical education. It is determined that Military specialization has a different content of combat activity and, thus, different requirements for the training of future specialists, according to the specifics of the activity, the structure of work movements, and the involvement of the body's systems and functions.Analysis of the dynamics of psychophysiological preparation of future officers in the conditions of a pedagogical experiment showed the presence of reliable positive changes in the development of movement frequency, accuracy of reproduction of half and a quarter of muscle effort, accuracy of reproduction of short and long time intervals, accuracy of distance reproduction, speed of reaction to a moving object, speed of perception and processing of visual information, efficiency of psychomotor activity, mental stability, volume, distribution and switching of attention in young cadets. In female cadets, a significant increase is observed in the indicators of the frequency of movements, the accuracy of reproduction of half and a quarter of muscle efforts, the accuracy of reproduction of short and long time intervals, the strength of nervous processes, the speed of perception and processing of visual information, the efficiency of mental activity, mental stability, volume, distribution and switching of attention. In addition, it has been proven that the implementation of the method of formation of special knowledge and skills of future officers in the process of physical education significantly increased the effectiveness of professional training of cadets of military higher educational institutions.

The Psychological Nature of Female Gait Attractiveness.

Walking, a basic physical movement of the human body, is a resource for observers in forming interpersonal impressions. We have previously investigated the expression and perception of the attractiveness of female gaits. In this paper, drawing on our previous research, additional analysis, and reviewing previous studies, we seek to deepen our understanding of the function of gait attractiveness. First, we review previous research on gait as nonverbal information. Then, we show that fashion models' gaits reflect sociocultural genderlessness, while nonmodels express reproductive-related biological attractiveness. Next, we discuss the functions of gait attractiveness based on statistical models that link gait parameters and attractiveness scores. Finally, we focus on observers' perception of attractiveness, constructing a model of the visual information processing with respect to gait attractiveness. Overall, our results suggest that there are not only biological but also sociocultural criteria for gait attractiveness, and men and women place greater importance on the former and latter criteria, respectively, when assessing female gait attractiveness. This paper forms a major step forward in neuroaesthetics to understand the beauty of the human body and the generation of biological motions.

INDICATORS OF OCULOMOTOR ACTIVITY AND CEREBRAL HEMODYNAMICS IN STUDENTS WITH DIFFERENT TYPES OF AUTONOMIC NERVOUS SYSTEM REACTIVITY IN COGNITIVE TASK PERFORMANCE

Background. The current stage of human development is characterized by high dynamics of knowledge accumulation, acceleration of social and technical development. The need to study psychophysiological characteristics of cognitive activity with time limitation increases under these conditions. Time limit can make activity more efficient or less effective. Purpose. To study indicators of oculomotor activity and cerebral hemodynamics of students with different types of autonomic nervous system reactivity during cognitive activity. Materials and methods. The study involved 110 students of the Northern (Arctic) Federal University named after M.V. Lomonosov took part in the study (average age of students 19.0±0.5 years). To identify specific features of ensuring cognitive activity in different time conditions, the type of reactivity of the autonomic nervous system (ANS) was determined in the subjects using the VNS-Spectrum computer complex (Neurosoft). Students performed cognitive tasks without and with time limits. At the same time, eye movements were recorded using the stationary binocular eye tracking system iView X™ RED, 500 Hz, and cerebral hemodynamic parameters were recorded using the Reo-Spectrum 2 hardware and software complex (Neurosoft). The efficiency of cognitive activity of the participants was also assessed. Statistical analysis was performed using the application package IBM SPSS Statistics 22.0. Results. Students with different types of reactivity of the autonomic nervous system had different effectiveness of cognitive activity without time limitation and with time limitation. Students with a sympathotonic type of autonomic reactivity had a higher result in processing visual information under time pressure. These students had more stable statistical relationships between the parameters of oculomotor activity and cerebral hemodynamics. Representatives with a vagotonic type of reactivity of the autonomic nervous system had the lowest rates of success in cognitive activity. These students showed less stable structure of statistical relationships between oculomotor activity and cerebral hemodynamics Conclusion. General and specific changes in cerebral hemodynamics and oculomotor activity, features of the effectiveness of cognitive activity under time constraints in young people with different types of autonomic nervous system reactivity were identified.

Unravelling the object-based nature of visual working memory: insight from pointers.

Visual working memory (VWM) plays a crucial role in temporarily storing and processing visual information, but the nature of stored representations and their interaction with new inputs has long been unclear. The pointer system refers to how VWM links new sensory inputs to stored information using specific cues. This study aimed to investigate whether the pointer system is based on spatial, feature-based, or object-based cues by employing the repetition benefit effect, where memory performance improves with repeated memory items. Across three experiments, we manipulated spatial positions, shapes, and colors as pointer cues to determine how these features affect VWM consolidation and updating. The results showed that while spatial location serves as a strong pointer cue, shape and color features can also effectively reestablish object correspondence in VWM. These findings support the view that the pointer system in VWM is flexible and object-based, utilizing various feature cues to maintain memory continuity. This study provides new insights into how VWM connects new inputs with stored information through the pointer system.

Unveiling Visual Physiology and Steady-State Evoked Potentials using Low-Cost and Transferable Electroencephalography for Evaluating Neuronal Activation

Purpose: The ability to see and process images depends on the function of the eyes and the processing of visual information by neurons in the cerebral cortex, something that could be measured through electroencephalography (EEG). Although the EEG is used to evaluate visual pathways in children and demyelination diseases, the limited utilization of brain recording techniques in other applications like therapy is primarily due to budget constraints. The goal of this paper is to demonstrate results from studying brain aspects of vision, utilizing measurements based on oscillatory activity analysis, low-cost, portable equipment, and a processing pipeline relying on Python’s open-source libraries. These studies involve healthy subjects who wear glasses to assess changes in visual perception. Methods: First, electroencephalographic signals were recorded while the subjects observed a visually standardized stimulus. The signals were processed and filtered to reduce artifacts, and the power spectral density (PSD) was calculated to observe the presence of steady-state visual potentials (VEP) to confirm the capture of neuronal activation to the visual stimulus. Results: It was possible to establish a difference between subjects wearing and not wearing their glasses, allowing validation that the information acquired with the transferable equipment is adequate for the analysis of neuronal activity related to visual processing, opening the possibility to be used in future studies in therapy. Conclusion: This study contributes to the development of cost-effective and portable EEG solutions for visual system analysis. It demonstrates the potential for applying transferable EEG devices in clinical settings and highlights the importance of tailored visual stimuli for reliable neural activation.

Aberrant dynamic functional and effective connectivity changes of the primary visual cortex in patients with retinal detachment via machine learning.

Previous neuroimaging studies have identified significant alterations in brain functional activity in retinal detachment (RD) patients, these investigations predominantly concentrated on local functional activity changes. The potential directional alterations in functional connectivity within the primary visual cortex (V1) in RD patients remain to be elucidated. In this study, we employed seed-based functional connectivity analysis along with Granger causality analysis to examine the directional alterations in dynamic functional connectivity (dFC) within the V1 region of patients diagnosed with RD. Finally, a support vector machine algorithm was utilized to classify patients with RD and healthy controls (HCs). RD patients exhibited heightened dynamic functional connectivity (dFC) and dynamic effective connectivity (dEC) between the Visual Network (VN) and default mode network (DMN), as well as within the VN, compared to HCs. Conversely, dFC between VN and auditory network (AN) decreased, and dEC between VN and sensorimotor network (SMN) significantly reduced. In state 4, RD patients had higher frequency. Notably, variations in dFC originating from the left V1 region proved diagnostically effective, achieving an AUC of 0.786. This study reveals significant alterations in the connectivity between the VN and the default mode network in patients with RD. These changes may disrupt visual information processing and higher cognitive integration in RD patients. Additionally, alterations in the left V1 region and whole-brain dFC show promising potential in aiding the diagnosis of RD. These findings offer valuable insights into the neural mechanisms underlying visual and cognitive impairments associated with RD.

Local stimulation of pyramidal neurons in deep cortical layers of anesthetized rats enhances cortical visual information processing

In the primary visual cortex area V1 activation of inhibitory interneurons, which provide negative feedback for excitatory pyramidal neurons, can improve visual response reliability and orientation selectivity. Moreover, optogenetic activation of one class of interneurons, parvalbumin (PV) positive cells, reduces the receptive field (RF) width. These data suggest that in V1 the negative feedback improves visual information processing. However, according to information theory, noise can limit information content in a signal, and to the best of our knowledge, in V1 signal-to-noise ratio (SNR) has never been estimated following either pyramidal or inhibitory neuron activation. Therefore, we optogenetically activated pyramidal or PV neurons in the deep layers of cortical area V1 and measured the SNR and RF area in nearby pyramidal neurons. Activation of pyramidal or PV neurons increased the SNR by 267% and 318%, respectively, and reduced the RF area to 60.1% and 77.5%, respectively, of that of the control. A simple integrate-and-fire neuron model demonstrated that an improved SNR and a reduced RF area can increase the amount of information encoded by neurons. We conclude that in V1 activation of pyramidal neurons improves visual information processing since the location of the visual stimulus can be pinpointed more accurately (via a reduced RF area), and more information is encoded by neurons (due to increased SNR).

Text-image multimodal fusion model for enhanced fake news detection.

In the era of rapid internet expansion and technological progress, discerning real from fake news poses a growing challenge, exposing users to potential misinformation. The existing literature primarily focuses on analyzing individual features in fake news, overlooking multimodal feature fusion recognition. Compared to single-modal approaches, multimodal fusion allows for a more comprehensive and enriched capture of information from different data modalities (such as text and images), thereby improving the performance and effectiveness of the model. This study proposes a model using multimodal fusion to identify fake news, aiming to curb misinformation. The framework integrates textual and visual information, using early fusion, joint fusion and late fusion strategies to combine them. The proposed framework processes textual and visual information through data cleaning and feature extraction before classification. Fake news classification is accomplished through a model, achieving accuracy of 85% and 90% in the Gossipcop and Fakeddit datasets, with F1-scores of 90% and 88%, showcasing its performance. The study presents outcomes across different training periods, demonstrating the effectiveness of multimodal fusion in combining text and image recognition for combating fake news. This research contributes significantly to addressing the critical issue of misinformation, emphasizing a comprehensive approach for detection accuracy enhancement.

Using Geovisualizations to Educate the Public About Environmental Health Hazards: What Works and Why.

Informing the public about environmental risks to health is crucial for raising awareness around hazards, and promoting actions that minimize exposures. Geographic visualizations-geovisualizations-have become an increasingly common way to disseminate web-based information about environmental hazards, displaying spatial variations in exposures and health outcomes using a map. Unfortunately, ineffective geovisualizations can result in inaccurate inferences about a hazard, leading to misguided actions or policies. In this narrative review, we discuss key considerations for the use of geovisualizations to promote environmental health literacy. Many conventional geovisualizations used for hazard education and risk communication fail to consider how people process visual information. Design choices that prompt viewers to think and feel, leveraging processes such as individual attention, memory, and emotion, could promote improved comprehension and decision making around environmental health risks using geovisualizations. Based on the studies reviewed, we recommend six strategies for designing effective, evidence-based geovisualizations, synthesizing evidence from the cognitive sciences, cartography, and environmental health. These strategies include: Displaying only key data, tailoring and testing geovisualizations with the desired audience, using salient cues, leveraging emotion, aiding pattern recognition, and limiting visual distractions. Geovisualizations offer a promising avenue for advancing public awareness and fostering proactive measures in addressing complex environmental health challenges. This review highlights how incorporating evidence-based design principles into geovisualizations could promote environmental health literacy. More experimental research evaluating geovisualizations, using interdisciplinary approaches, is needed.

On the lasting impact of mild traumatic brain injury on working memory: Behavioural and electrophysiological evidence

Despite increasing recognition of the significance of mild traumatic brain injury (mTBI), the long-term cognitive consequences of the injury remain unclear. More sensitive measures that can detect subtle cognitive changes and consideration of individual variability are needed to properly characterise cognitive outcomes following mTBI. Here, we used complex behavioural tasks, individual differences approaches, and electrophysiology to investigate the long-term cognitive effects of a history of mTBI. In Experiment 1, participants with self-reported mTBI history (n=82) showed poorer verbal working memory performance on the operation span task compared to control participants (n=88), but there were no group differences in visual working memory, multitasking, cognitive flexibility, attentional control, visuospatial ability, or information processing speed. Individual differences analyses revealed that time since injury and presence of memory loss predicted visual working memory capacity and visuospatial ability, respectively, in those with mTBI history. In Experiment 2, participants with mTBI history (n=20) again demonstrated poorer verbal working memory on the operation span task compared to control participants (n=38), but no group differences were revealed on a visuospatial complex span task or simpler visual working memory measures. We also explored the electrophysiological indices of visual working memory using EEG during a change detection task. No differences were observed in early sensory event-related potentials (P1, N1) or the later negative slow wave associated with visual working memory capacity. Together, these findings suggest that mTBI history may be associated with a lasting, isolated disruption in the subsystem underlying verbal working memory storage. The results emphasise the importance of sensitive cognitive measures and accounting for individual variability in injury characteristics when assessing mTBI outcomes.

Bio-inspired deep neural local acuity and focus learning for visual image recognition

In the field of computer vision and image recognition, enabling the computer to discern target features while filtering out irrelevant ones poses a challenge. Drawing insights from studies in biological vision, we find that there is a local visual acuity mechanism and a visual focus mechanism in the initial conversion and processing of visual information, ensuring that the visual system can give ear to salient features of the target in the early visual observation phase. Inspired by this, we build a novel image recognition network to focus on the target features while ignoring other irrelevant features in the image, and further focus on the focus features based on the target features. Meanwhile, in order to comply with the output characteristics when similar features exist in different categories, we design a softer image label operation for similar features in different categories, which solves the correlation of labels between categories. Relevant experimental findings underscore the efficacy of our proposed method, revealing discernible advantages in comparison to alternative approaches. Visualization results further attest to the method’s capability to selectively focus on pertinent target features within the image, sidelining extraneous information.