Visual Mechanisms Research Articles

A Bio-Inspired Visual Neural Model for Robustly and Steadily Detecting Motion Directions of Translating Objects Against Variable Contrast in the Figure-Ground and Noise Interference

(1) Background: At present, the bio-inspired visual neural models have made significant achievements in detecting the motion direction of the translating object. Variable contrast in the figure-ground and environmental noise interference, however, have a strong influence on the existing model. The responses of the lobula plate tangential cell (LPTC) neurons of Drosophila are robust and stable in the face of variable contrast in the figure-ground and environmental noise interference, which provides an excellent paradigm for addressing these challenges. (2) Methods: To resolve these challenges, we propose a bio-inspired visual neural model, which consists of four stages. Firstly, the photoreceptors (R1–R6) are utilized to perceive the change in luminance. Secondly, the change in luminance is divided into parallel ON and OFF pathways based on the lamina monopolar cell (LMC), and the spatial denoising and the spatio-temporal lateral inhibition (LI) mechanisms can suppress environmental noise and improve motion boundaries, respectively. Thirdly, the non-linear instantaneous feedback mechanism in divisive contrast normalization is adopted to reduce local contrast sensitivity; further, the parallel ON and OFF contrast pathways are activated. Finally, the parallel motion and contrast pathways converge on the LPTC in the lobula complex. (3) Results: By comparing numerous experimental simulations with state-of-the-art (SotA) bio-inspired models, we can draw four conclusions. Firstly, the effectiveness of the contrast neural computation and the spatial denoising mechanism is verified by the ablation study. Secondly, this model can robustly detect the motion direction of the translating object against variable contrast in the figure-ground and environmental noise interference. Specifically, the average detection success rate of the proposed bio-inspired model under the pure and real-world complex noise datasets was increased by 5.38% and 5.30%. Thirdly, this model can effectively reduce the fluctuation in this model response against variable contrast in the figure-ground and environmental noise interference, which shows the stability of this model; specifically, the average inter-quartile range of the coefficient of variation in the proposed bio-inspired model under the pure and real-world complex noise datasets was reduced by 38.77% and 47.84%, respectively. The average decline ratio of the sum of the coefficient of variation in the proposed bio-inspired model under the pure and real-world complex noise datasets was 57.03% and 67.47%, respectively. Finally, the robustness and stability of this model are further verified by comparing other early visual pre-processing mechanisms and engineering denoising methods. (4) Conclusions: This model can robustly and steadily detect the motion direction of the translating object under variable contrast in the figure-ground and environmental noise interference.

Behavioural evidence of spectral opponent processing in the visual system of stomatopod crustaceans.

Stomatopods, commonly known as mantis shrimps, possess an intricate colour vision with up to 12 photoreceptor classes arranged in four specialised ommatidia rows (rows 1-4 in the midband region of the eye) for colour perception. While 2-4 spectral sensitivities suffice for most visual systems, the function and mechanism behind stomatopods' 12-channel colour vision remains unclear. Previous anatomical and behavioural studies have suggested that binning and opponent processing mechanisms may coexist in stomatopods' colour vision. However, direct evidence of colour opponency has been lacking. We hypothesised that if colour opponency exists in stomatopods' vision, they would be able to distinguish colour from grey under coloured illumination. Conversely, if only the binning system is used, they would not. By examining the colour vision of the stomatopod Haptosquilla trispinosa with modified von Frisch grey card experiments, we found that they can differentiate between colour and grey under various coloured illuminations. Our results provide the first direct behavioural evidence of spectral opponency in stomatopods, suggesting that they use a hybrid colour processing system combining opponent and binning mechanisms for colour vision. This study advances our understanding of the complex visual system in stomatopods and highlights the importance of further research into the processing mechanisms, function and evolution of their unique visual system.

(Re)Framing the Spectrum of Trauma(s) Experienced During the Sri-Lankan Civil War: A Critical Exploration of the Graphic “Non-fiction-fiction” Vanni

Ethno-graphic narratives have increasingly served as an alternate politico-historical commentary on traumatic events, and Vanni is no exception. By analyzing the iconical language embedded in this graphic ‘non-fiction-fiction’, the article portrays the varied aspects of trauma experienced by individuals and the collective during the prolonged Sri Lankan conflict, thereby documenting the enduring biopsychosocial impacts of the trauma of dwelling, living through, and leaving conflict zones. To elaborate, the paper explores the visual and verbal mechanisms through which the spectrum of trauma, namely intergenerational, collective, and individual trauma, finds representation in this ethnographic text. Firstly, the article explores how Vanni represents the protracted conflict that impacts generations of the Tamil community through the visual-verbal mode of the text, thereby showcasing the mechanisms of understanding intergenerational trauma. Secondly, it analyses the varied aspects through which collective trauma finds representation in the ethnographic narrative, and finally, the study outlines select individual experiences of trauma within the frame of collective suffering. By decoding the lexical and imagistic expressions of trauma in Vanni, the article argues that the graphic narrative form has been able to memorialize the abysmal trauma(s) experienced during the Sri Lankan conflict, thereby serving as a site of memory and countering collective amnesia.

Remote sensing of ship polarization information through sea fog based on retinal visual information processing mechanisms

ABSTRACT It is challenging to observe the polarization information of ships in sea fog weather due to the depolarization phenomenon. Depolarization refers to the phenomenon that the polarization information of incident light is distorted after passing through the scattering medium such as sea fog, which poses a great challenge to polarization remote sensing of ships. Therefore, ship polarization information remote sensing through sea fog is equivalent to recovering the original polarization information of ships. In this paper, a new method is proposed for recovering the original polarization information of ships in sea fog environment. The method is inspired by the visual information processing mechanisms of the retina. In addition, a new type of visual receptive field, called deformable receptive field, is proposed to improve the performance of the polarization information restoration method. The proposed method does not depend on the hazy image formation model, the depth-dependent transmission, and the training on hazy and fog-free image pairs. As a by-product, fog-free images can be recovered using the proposed method. The effectiveness of the proposed method has been verified by an experiment on ship polarization information remote sensing in sea fog weather.

A Method for Detecting Edge Continuity in Art and Design Images Based on Visual Attention Mechanism

To address the issues of low accuracy and significant noise impact in edge continuity detection of art and design images, this paper proposes a visual attention-based edge continuity detection method for art and design images. Determine the edge position points of art and design images using the Laplace operator, and obtain the horizontal and vertical gradients of the image edges by calculating the first-order difference method to determine the amplitude of the image edge gradient; By using the maximum and minimum operations in binary morphology instead of intersection and union operations, the image grayscale is determined, and the HSI color space features are determined to complete the edge feature extraction of art and design images. Using the SUSAN operator to clarify the kernel similarity zone of the edges of art and design images, removing similar edge image pixels, using spatial domain denoising and frequency domain denoising to reduce the edge noise of art and design images, and achieving edge preprocessing of art and design images. Introducing visual attention mechanism to transform the pixel space of edge features in art and design images, performing threshold segmentation on the edges of art and design images, and continuously annotating the segmented edge pixels. Introducing loss function to improve the convergence speed of detection, constructing an art and design image edge continuity detection model based on visual attention mechanism, outputting detection results, and implementing research. The experimental results show that the proposed method has better continuity due to the successful avoidance of noise interference by introducing visual attention mechanisms and other operations. As the number of detected pixels increases, the detection deviation rate of the proposed method remains between 0.02% and 0.03%. The proposed method has a lower detection bias rate and can effectively improve the performance of edge continuity detection in art and design images.

Feature selectivity and invariance in marsupial primary visual cortex.

A fundamental question in sensory neuroscience revolves around how neurons represent complex visual stimuli. In mammalian primary visual cortex (V1), neurons decode intricate visual features to identify objects, with most being selective for edge orientation, but with half of those also developing invariance to edge position within their receptive fields. Position invariance allows cells to continue to code an edge even when it moves around. Combining feature selectivity and invariance is integral to successful object recognition. Considering the marsupial-eutherian divergence 160million years ago, we explored whether feature selectivity and invariance was similar in marsupials and eutherians. We recovered the spatial filters and non-linear processing characteristics of the receptive fields of neurons in wallaby V1 and compared them with previous results from cat cortex. We stimulated the neurons in V1 with white Gaussian noise and analysed responses using the non-linear input model. Wallabies exhibit the same high percentage of orientation selective neurons as cats. However, in wallabies we observed a notably higher prevalence of neurons with three or more filters compared to cats. We show that having three or more filters substantially increases phase invariance in the V1s of both species, but that wallaby V1 accentuates this feature, suggesting that the species condenses more processing into the earliest cortical stage. These findings suggest that evolution has led to more than one solution to the problem of creating complex visual processing strategies. KEY POINTS: Previous studies have shown that the primary visual cortex (V1) in mammals is essential for processing complex visual stimuli, with neurons displaying selectivity for edge orientation and position. This research explores whether the visual processing mechanisms in marsupials, such as wallabies, are similar to those in eutherian mammals (e.g. cats). The study found that wallabies have a higher prevalence of neurons with multiple spatial filters in V1, indicating more complex visual processing. Using a non-linear input model, we demonstrated that neurons with three or more filters increase phase invariance. These findings suggest that marsupials and eutherian mammals have evolved similar strategies for visual processing, but marsupials have condensed more capacity to build phase invariance into the first step in the cortical pathway.

Attention-Driven Memory Network for Online Visual Tracking.

A memory mechanism has attracted growing popularity in tracking tasks due to the ability of learning long-term-dependent information. However, it is very challenging for existing memory modules to provide the intrinsic attribute information of the target to the tracker in complex scenes. In this article, by considering the biological visual memory mechanisms, we propose the novel online tracking method via an attention-driven memory network, which can mine discriminative memory information and enhance the robustness and reliability of the tracker. First, to reinforce effectiveness of memory content, we design a novel attention-driven memory network. In the network, the long memory module gains property-level memory information by focusing on the state of the target at both the channel and spatial levels. Meanwhile, in reciprocity, we add a short-term memory module to maintain good adaptability when confronting drastic deformation of the target. The attention-driven memory network can adaptively adjust the contribution of short-term and long-term memories to tracking results under the weighted gradient harmonized loss. On this basis, to avoid model performance degradation, an online memory updater (MU) is further proposed. It is designed to mining for target information in tracking results through the Mixer layer and the online head network together. By evaluating the confidence of the tracking results, the memory updater can accurately judge the time of updating the model, which guarantees the effectiveness of online memory updates. Finally, the proposed method performs favorably and has been extensively validated on several benchmark datasets, including object tracking benchmark-50/100 (OTB-50/100), temple color-128 (TC-128), unmanned aerial vehicles-123 (UAV-123), generic object tracking -10k (GOT-10k), visual object tracking-2016 (VOT-2016), and VOT-2018 against several advanced methods.

Unraveling the Differential Efficiency of Dorsal and Ventral Pathways in Visual Semantic Decoding.

Visual semantic decoding aims to extract perceived semantic information from the visual responses of the human brain and convert it into interpretable semantic labels. Although significant progress has been made in semantic decoding across individual visual cortices, studies on the semantic decoding of the ventral and dorsal cortical visual pathways remain limited. This study proposed a graph neural network (GNN)-based semantic decoding model on a natural scene dataset (NSD) to investigate the decoding differences between the dorsal and ventral pathways in process various parts of speech, including verbs, nouns, and adjectives. Our results indicate that the decoding accuracies for verbs and nouns with motion attributes were significantly higher for the dorsal pathway as compared to those for the ventral pathway. Comparative analyses reveal that the dorsal pathway significantly outperformed the ventral pathway in terms of decoding performance for verbs and nouns with motion attributes, with evidence showing that this superiority largely stemmed from higher-level visual cortices rather than lower-level ones. Furthermore, these two pathways appear to converge in their heightened sensitivity toward semantic content related to actions. These findings reveal unique visual neural mechanisms through which the dorsal and ventral cortical pathways segregate and converge when processing stimuli with different semantic categories.

Emoji multimodal microblog sentiment analysis based on mutual attention mechanism.

Emojis, utilizing visual means, mimic human facial expressions and postures to convey emotions and opinions. They are widely used in social media platforms such as Sina Weibo, and have become a crucial feature for sentiment analysis. However, existing approaches often treat emojis as special symbols or convert them into text labels, thereby neglecting the rich visual information of emojis. We propose a novel multimodal information integration model for emoji microblog sentiment analysis. To effectively leverage the emoji visual information, the model employs a text-emoji visual mutual attention mechanism. Experiments on a manually annotated microblog dataset show that compared to the baseline models without incorporating emoji visual information, the proposed model achieves improvements of 1.37% in macro F1 score and 2.30% in accuracy, respectively. To facilitate the related research, our corpus will be publicly available at https://github.com/yx100/Emojis/blob/main/weibo-emojis-annotation .

Acoustic cavitation assisted synthesis of PCDs@PVA composite film for UV shielding, intelligent pH detection, information encryption and food packing applications

Excessive exposure to UV and high-energy blue light (HEBL) can severely damage the skin and eyes. Therefore, it is essential to protect our bodies from UV and HEBL radiation. We created PEG-derived carbon dots (PCDs) using the acoustic cavitation approach in order to completely block UV and HEBL. The influence of different experimental parameters such as sonication time, amplitude, and temperature on the PCDs is examined. The optimized PCDs are found to have an average diameter ranging from 5 to 9 nm, contingent upon the preparation circumstances, and to have a quantum yield (QY) of 14–16 %. The PCDs vivid colour and simultaneous UV and HEBL absorption properties allowed the PVA film containing 0.120 wt% of PCDs to demonstrate outstanding blocking efficiency, with 99.9 % blocking in HEBL and 100 % blocking in UV-C, UV-B, and UV-A. The photoluminescence (PL) behaviour of PCDs is greatly reduced by Fe3+ ions because of strong excited state electron transfer. This fluorescent “turn-off” behaviour towards Fe3+ is highly selective and sensitive with a limit of detection (LoD) of 0.366 μM. Paper-based sensors incorporating PCDs are developed for the rapid and accurate detection of Fe3+ in surface water, wastewater treatment plant effluent, and tap water, leveraging their excellent sensitivity and selectivity towards Fe3+. The characteristics of ink-free patterned substrates are displayed by PCDs scattered across a PVA matrix, which makes them useful for the non-destructive acquisition and recognition of latent fingerprints (LFPs). A flexible, transparent film is produced when a LFP is exposed to a PCDs@PVA solution. In this film, a steady luminous fingerprint with specific ridge characteristics that aid in personal recognition is seen. This method shows promise for lifting and identifying long-exposed LFPs from various surfaces without causing damage. It is believed that the interfacial segregation of PCDs inside the PVA matrix during the film production process serves as a mechanism for LFP collection and visualization. The YOLOv8x program, which utilizes deep convolutional neural networks, is employed to analyze the discernible features present in fingerprints (FPs). Notably, their exceptional flexibility, foldability, and sustainability create new opportunities for use in the anti-counterfeiting (AC) field. Because of its remarkable fluorescence stability, it may be a viable substitute for conventional fluorescent inks. Fresh green apples are observed over time after being coated with 0.120 wt% PCDs@PVA and PVA. Virtual assessments indicated that the 0.120 wt% PCDs@PVA film coating significantly reduced weight and moisture loss, effectively inhibiting fungal growth and spoilage for over 25 days at room temperature (RT). Overall, the results clearly show that the PCDs@PVA film is easy to make, flexible, safe for the environment, and resistant to photodegradation. Hemolysis, and blood clotting exhibited favorable biocompatibility and nontoxicity.

Functional characterization of optic photoreception in Lymnaea stagnalis.

Optic photoreception is a critical function for animal survival. Across the evolutionary spectrum, diverse animal models have been used to investigate visual system function and potential mechanisms under physiological or pathophysiological states. However less is known on photoreceptive behaviors and retinal processing in invertebrates, especially molluscs. This study focuses on the freshwater pond snail, Lymnaea stagnalis (L. stagnalis), to explore its visual function and underlying mechanisms. Using anatomical and histological approaches we characterized the L. stagnalis eye structure and demonstrated structural connections and retinal rhodopsin-positive sensory cells potentially critical for phototransduction. To assess the snail phototactic responses, we developed a new neurobehavioral protocol and employed DeepLabCut to track and quantify animal locomotion. We demonstrated that L. stagnalis exhibits a positive locomotory response to intense focal light and has diverse photo-locomotory responses. Further, we conducted phylogenetic and protein structure analyses and demonstrated that L. stagnalis has a unique repertoire of both vertebrate and invertebrate phototransduction genes. Further characterization of a rhodopsin-like gene identified unique characteristics compared to other mollusks and vertebrates, suggesting different mechanisms of phototransduction. Taken together, our work establishes L. stagnalis as a model organism for studying optic photoreception, offering new insights into the evolution and diversity of visual function across animal species.

Design of automatic sorting system based on capsule neural network

Abstract. This paper proposes a vegetable disease classification system based on Capsule Neural Network (CapsNet), which uses the visual recognition mechanism of deep learning algorithms to improve the efficiency and accuracy of agricultural disease recognition. The CapsNet algorithm outperforms traditional Convolutional Neural Network (CNN) in few-shot learning and generalization due to its unique capsule structure and dynamic routing mechanism. Experimental results show that the proposed system performs well in the accuracy, robustness, and model interpretation of image recognition, and can accurately identify diseases even in the case of poor image quality or noise and occlusion. In addition, the embedded system of this experiment includes an image acquisition module, an algorithm core module, and a logic control module, which realizes the automation of the whole process from image acquisition to recognition result display. This study also discusses the scalability and adaptability of the system, as well as the future application prospects in agricultural intelligence and automation. It is expected that CapsNet will play a more critical role in artificial intelligence, especially in agrarian automation and smart agriculture.

Prey detection by a stepwise visual template matching mechanism.

Predators can improve prey capture using a search image, and recent prey provide a visual template with which subsequent prey are compared. Considering trout feeding responses to mayfly prey of different sizes and phenological availability across years, we tested if changing relative abundances (ratios) of prey of the same species, but different body sizes, shifted trout feeding behaviour. For example, we hypothesized that a feeding switch from larger to smaller prey required continuous exposure to the novel smaller prey. The hypothesis that continuous exposure to novel small prey results in their acceptance was not supported. Rather, we discovered that trout identify novel prey using a dynamic stepwise visual neural template prey matching process, which involves the formation of focal prey template based on size or type, rejection of novel prey that do not match the size or type templates and modification of the existing or development of multiple prey templates that eventually enabled recognition of novel, small prey. We also discovered trout store multiple visual prey templates in memory. These results have implications for predator and prey dynamics, optimal foraging, the persistence of rare prey, prey species coexistence and predator selection on prey phenology.

High-precision positioning system for composite robots based on visual feedback

Abstract Composite robots, which integrate robotic arms with Automated Guided Vehicles (AGVs), offer flexibility but face challenges due to insufficient navigation precision. To address this issue, this paper proposes a high-precision positioning system for composite robots based on visual feedback. First, the system constructs an environment map and performs path planning through LiDAR to realize preliminary navigation. Due to the limited localization accuracy of LiDAR, the system introduces a visual feedback mechanism after the robot arrives at the target point, acquires the position information of markers in real-time through the camera, and precisely adjusts the end-effector so that it actually achieves the absolute positioning accuracy of the robotic arm. This method is equal to the end positioning accuracy of the robotic arm after compensation when the absolute positioning accuracy of the robotic arm is 0.2mm. This system holds significant potential in industrial automation, particularly in scenarios requiring high-precision operations, and provides critical support for enhancing production efficiency and product quality.

Inspiration from Visual Ecology for Advancing Multifunctional Robotic Vision Systems: Bio-inspired Electronic Eyes and Neuromorphic Image Sensors.

In robotics, particularly for autonomous navigation and human-robot collaboration, the significance of unconventional imaging techniques and efficient data processing capabilities is paramount. The unstructured environments encountered by robots, coupled with complex missions assigned to them, present numerous challenges necessitating diverse visual functionalities, and consequently, the development of multifunctional robotic vision systems has become indispensable. Meanwhile, rich diversity inherent in animal vision systems, honed over evolutionary epochs to meet their survival demands across varied habitats, serves as a profound source of inspirations. Here, recent advancements in multifunctional robotic vision systems drawing inspiration from natural ocular structures and their visual perception mechanisms are delineated. First, unique imaging functionalities of natural eyes across terrestrial, aerial, and aquatic habitats and visual signal processing mechanism of humans are explored. Then, designs and functionalities of bio-inspired electronic eyes are explored, engineered to mimic key components and underlying optical principles of natural eyes. Furthermore, neuromorphic image sensors are discussed, emulating functional properties of synapses, neurons, and retinas and thereby enhancing accuracy and efficiency of robotic vision tasks. Next, integration examples of electronic eyes with mobile robotic/biological systems are introduced. Finally, a forward-looking outlook on the development of bio-inspired electronic eyes and neuromorphic image sensors is provided.

Camouflaged Object Segmentation Based on Matching-Recognition-Refinement Network.

In the biosphere, camouflaged objects take the advantage of visional wholeness by keeping the color and texture of the objects highly consistent with the background, thereby confusing the visual mechanism of other creatures and achieving a concealed effect. This is also the main reason why the task of camouflaged object detection is challenging. In this article, we break the visual wholeness and see through the camouflage from the perspective of matching the appropriate field of view. We propose a matching-recognition-refinement network (MRR-Net), which consists of two key modules, i.e., the visual field matching and recognition module (VFMRM) and the stepwise refinement module (SWRM). In the VFMRM, various feature receptive fields are used to match candidate areas of camouflaged objects of different sizes and shapes and adaptively activate and recognize the approximate area of the real camouflaged object. The SWRM then uses the features extracted by the backbone to gradually refine the camouflaged region obtained by VFMRM, thus yielding the complete camouflaged object. In addition, a more efficient deep supervision method is exploited, making the features from the backbone input into the SWRM more critical and not redundant. Extensive experimental results demonstrate that our MRR-Net runs in real-time (82.6 frames/s) and significantly outperforms 30 state-of-the-art models on three challenging datasets under three standard metrics. Furthermore, MRR-Net is applied to four downstream tasks of camouflaged object segmentation (COS), and the results validate its practical application value. Our code is publicly available at: https://github.com/XinyuYanTJU/MRR-Net.

A following model considering multiple vehicles from the driver's front and rear perspectives

In the current traffic environment dominated by manual driving, existing models of drivers' rear-view perceptions are inadequate. Existing car-following models that incorporate rear-view information are primarily focused on the Internet of Vehicles (IoV) and automated driving environments. However, they fail to realistically reflect the visual processing mechanisms of human drivers, limiting their effectiveness in realistic traffic scenarios. Therefore, we propose a new following model, the multi-vehicle influence from front and rear perspectives (MVFR), that considers the influence of multiple vehicles. The MVFR model combines information from both front and rear vehicles, integrating views from the front, side front and rear. It provides an in-depth analysis of the effects of relative state differences between a vehicle and its surrounding vehicles on speed, including the effects of perspectives in both the lateral and longitudinal directions. Linear stability analysis and numerical simulation demonstrate that considering the perspectives of rear-following vehicles and lateral offset angles can improve traffic flow stability to a certain extent. Furthermore, properly considering the lateral offset distance and the number of vehicles ahead also positively affects traffic flow stability. This study reveals that observing following vehicles and considering information from multiple front vehicles enhances system stability, especially when there is no or minimal lateral offset. In contrast, focusing on fewer front vehicles is more effective for traffic flow stability when there is a large lateral offset. Experimental results using the CKQ4up dataset show that the MVFR model achieves higher accuracy than the conventional FVD model, the front-view-only improved FVD model (MFVD-RV), and the MFRHVAD-RV and MFRHVAD-AV models. Compared with models relying solely on front-view or non-visual perception, the MVFR model demonstrates a better fit, validating the advantages of this full-view perception model in manual driving environments. This innovation addresses the shortcomings of existing research, thereby enhancing the reliability of models under manual driving conditions on highways.

The early visual processing of faces in a basic classification task: An ERP study of spatial frequency, other-race and other species effect

ABSTRACT This study investigates the neural processing of other-race and other-species faces, using spatial frequencies (SFs) to probe into early visual processing mechanisms. We conducted a basic classification task to reveal fundamental aspects of facial processing without the complexity of higher-order recognition tasks. This approach utilized SFs set at low (LSF, 0.53 cycles per degree [cpd]), broadband (BB), and high (HSF, 2.1 cpd) SFs to examine their impact on the both P100 and N170 components of event-related potentials. Our findings revealed a significant latency difference in the N170 component under HSF conditions, indicating race-specific efficiency in processing fine details between Asian and Caucasian faces. Furthermore, N170 amplitudes were consistently larger for human than monkey faces, with this difference more pronounced in LSF and BB conditions. This finding indicates that the other-species effect in early visual processing is more pronounced in holistic processing (LSF and BB conditions) rather than in fine-detail processing.

Can playing video games enhance reading skills through more efficient serial visual search mechanisms? Insights from an eye tracking study

ABSTRACT Reading disorders are associated with atypical top-down visual attention (VA) processes like reduced VA span and slower serial visual search (SVS). In contrast, expert action video game (AVG) players, known for their efficient top-down VA, exhibit improved reading abilities. It is unclear whether these benefits stem solely from AVGs or apply to other gaming experiences. To explore this, AVG players (AVGPs), players of genres excluding AVGs (VGPs), and non-players were evaluated on their VA span, and behavioural and oculomotor performance in SVS. VGPs, but not AVGPs, demonstrated enhanced performance and oculomotor behaviour in SVS compared to non-players, while both player groups showed a trend towards better VA span skills. Notably, reading-related skills were enhanced in the two player groups, but particularly more so in VGPs. These findings support the existence of potential benefits of playing video games different from classical AVGs for the development of top-down VA and reading-related abilities.

A neural mechanism for optic flow parsing in macaque visual cortex

For the brain to compute object motion in the world during self-motion, it must discount the global patterns of image motion (optic flow) caused by self-motion. Optic flow parsing is a proposed visual mechanism for computing object motion in the world, and studies in both humans and monkeys have demonstrated perceptual biases consistent with the operation of a flow-parsing mechanism. However, the neural basis of flow parsing remains unknown. We demonstrate, at both the individual unit and population levels, that neural activity in macaque middle temporal (MT) area is biased by peripheral optic flow in a manner that can at least partially account for perceptual biases induced by flow parsing. These effects cannot be explained by conventional surround suppression mechanisms or choice-related activity and have substantial neural latency. Together, our findings establish the first neural basis for the computation of scene-relative object motion based on flow parsing.