Visual Processing Mechanisms Research Articles

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.

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.

It's all in the timing: delayed feedback in autism may weaken predictive mechanisms during contour integration.

Humans rely on predictive and integrative mechanisms during visual processing to efficiently resolve incomplete or ambiguous sensory signals. Although initial low-level sensory data are conveyed by feedforward connections, feedback connections are believed to shape sensory processing through automatic conveyance of statistical probabilities based on prior exposure to stimulus configurations. Individuals with autism spectrum disorder (ASD) show biases in stimulus processing toward parts rather than wholes, suggesting their sensory processing may be less shaped by statistical predictions acquired through prior exposure to global stimulus properties. Investigations of illusory contour (IC) processing in neurotypical (NT) adults have established a well-tested marker of contour integration characterized by a robust modulation of the visually evoked potential (VEP)-the IC-effect-that occurs over lateral occipital scalp during the timeframe of the visual N1 component. Converging evidence strongly supports the notion that this IC-effect indexes a signal with significant feedback contributions. Using high-density VEPs, we compared the IC-effect in 6- to 17-yr-old children with ASD (n = 32) or NT development (n = 53). Both groups of children generated an IC-effect that was equivalent in amplitude. However, the IC-effect notably onset 21 ms later in ASD, even though initial VEP afference was identical across groups. This suggests that feedforward information predominated during perceptual processing for 15% longer in ASD compared with NT children. This delay in the feedback-dependent IC-effect, in the context of known developmental differences between feedforward and feedback fibers, suggests a potential pathophysiological mechanism of visual processing in ASD, whereby ongoing stimulus processing is less shaped by visual feedback.NEW & NOTEWORTHY Children with autism often present with an atypical visual perceptual style that emphasizes parts or details over the whole. Using electroencephalography (EEG), this study identifies delays in the visual feedback from higher-order sensory brain areas to primary sensory regions. Because this type of visual feedback is thought to carry information about prior sensory experiences, individuals with autism may have difficulty efficiently using prior experience or putting together parts into a whole to help make sense of incoming new visual information. This provides empirical neural evidence to support theories of disrupted sensory perception mechanisms in autism.

Organization of corticocortical and thalamocortical top-down inputs in the primary visual cortex.

Unified visual perception requires integration of bottom-up and top-down inputs in the primary visual cortex (V1), yet the organization of top-down inputs in V1 remains unclear. Here, we used optogenetics-assisted circuit mapping to identify how multiple top-down inputs from higher-order cortical and thalamic areas engage V1 excitatory and inhibitory neurons. Top-down inputs overlap in superficial layers yet segregate in deep layers. Inputs from the medial secondary visual cortex (V2M) and anterior cingulate cortex (ACA) converge on L6 Pyrs, whereas ventrolateral orbitofrontal cortex (ORBvl) and lateral posterior thalamic nucleus (LP) inputs are processed in parallel in Pyr-type-specific subnetworks (Pyr←ORBvl and Pyr←LP) and drive mutual inhibition between them via local interneurons. Our study deepens understanding of the top-down modulation mechanisms of visual processing and establishes that V2M and ACA inputs in L6 employ integrated processing distinct from the parallel processing of LP and ORBvl inputs in L5.

An investigation into the correlation between visual performance in simulated complex environments and academic attainment among primary school students

Traditional vision screenings in schools are limited to simple visual tasks, yet students in their daily learning face more complex visual environments. Binocular rivalry tasks can partially simulate the visual challenges of real visual environments and activate advanced visual processing mechanisms that simple visual tasks cannot. Therefore, by superimposing binocular rivalry-state tasks onto simple visual tasks, we have developed an innovative vision screening program to rapidly and extensively assess students’ visual performance in complex environments. This is a cross-sectional study in which we investigated the performance of 1126 grade 1–6 students from a primary school in Wuxi, China, in rivalry-state stereoscopic vision tasks. The correlation between the screening results of 1044 students and their academic achievements was also statistically analyzed. The study results revealed pass rates of 53.5–60.5% across various visual tests. Specifically, for first-grade students, there was a statistically significant difference in standardized Chinese scores between the group that failed and the group that passed the rivalry-state stereoscopic vision test (− 0.49 ± 3.42 vs. 0.22 ± 0.58, t = − 2.081, P = 0.04). This result underscores the importance of focusing on the visual adaptability of first graders in complex environments.Trail registration: Ethics Committee of Affiliated Children’s Hospital of Jiangnan University-Certificate number: WXCH2022-04-027

Bidirectional Feature Aggregation Network for Stereo Image Quality Assessment Considering Parallax Attention-Based Binocular Fusion

Inspired by the two-path visual information processing mechanism (i.e., a bottom-up path and a top-down path), we propose a bidirectional binocular feature aggregation based stereo image quality assessment (SIQA) network, which considers a two-path visual mechanism and realizes the binocular fusion based on parallax information. To better aggregate binocular features from different levels, a two-path feature aggregation structure, which simulates the bottom-up and top-down mechanism in human visual system (HVS), is proposed. It not only realizes the supplement of low-level detail information to high-level semantic in the bottom-up path, but also realizes the supplement of high-level semantic information to low-level detail in the top-down path. Simultaneously, because feature misalignment exists in binocular features of adjacent levels, a feature alignment module (FAM) based on deformable convolution is designed to integrate the binocular fusion features of adjacent levels. In addition, considering the importance role of parallax in guiding binocular fusion, a binocular fusion module (BFM) based on parallax attention mechanism, which is different with existing binocular fusion methods, is explicitly proposed to achieve the binocular fusion between the left and right view features. Extensive experiments are conducted on LIVE I, LIVE II, WIVC I and WIVC II databases to demonstrate the effectiveness of the proposed method.

A stimulus exposure of 50 ms elicits the uncanny valley effect

The uncanny valley (UV) effect captures the observation that artificial entities with near-human appearances tend to create feelings of eeriness. Researchers have proposed many hypotheses to explain the UV effect, but the visual processing mechanisms of the UV have yet to be fully understood. In the present study, we examined if the UV effect is as accessible in brief stimulus exposures compared to long stimulus exposures (Experiment 1). Forty-one participants, aged 21–31, rated each human-robot face presented for either a brief (50 ms) or long duration (3 s) in terms of attractiveness, eeriness, and humanness (UV indices) in a 7-point Likert scale. We found that brief and long exposures to stimuli generated a similar UV effect. This suggests that the UV effect is accessible at early visual processing. We then examined the effect of exposure duration on the categorisation of visual stimuli in Experiment 2. Thirty-three participants, aged 21–31, categorised faces as either human or robot in a two-alternative forced choice task. Their response accuracy and variance were recorded. We found that brief stimulus exposures generated significantly higher response variation and errors than the long exposure condition. This indicated that participants were more uncertain in categorising faces in the brief exposure condition due to insufficient time. Further comparisons between Experiment 1 and 2 revealed that the eeriest faces were not the hardest to categorise. Overall, these findings indicate (1) that both the UV effect and categorical uncertainty can be elicited through brief stimulus exposure, but (2) that categorical uncertainty is unlikely to cause the UV effect. These findings provide insights towards the perception of robotic faces and implications for the design of robots, androids, avatars, and artificial intelligence agents.

Attentional modulation of eye blinking is altered by sex, age, and task structure.

Spontaneous eye blinking is gaining popularity as a proxy for higher cognitive functions, as it is readily modulated by both environmental demands and internal processes. Prior studies were impoverished in sample size, sex representation and age distribution, making it difficult to establish a complete picture of the behavior. Here we present eye-tracking data from a large cohort of normative participants (n=604, 393 F, aged 5-93 years) performing two tasks: one with structured, discrete trials (interleaved pro/anti-saccade task; IPAST) and one with a less structured, continuous organization in which participants watch movies (free-viewing; FV). Sex- and age-based analyses revealed that females had higher blink rates between the ages of 22 and 58 years in the IPAST, and 22 and 34 years in FV. We derived a continuous measure of blink probability to reveal behavioral changes driven by stimulus appearance in both paradigms. In the IPAST, blinks were suppressed near stimulus appearance, particularly on correct anti-saccade trials, which we attribute to the stronger inhibitory control required for anti-saccades compared to pro-saccades. In FV, blink suppression occurred immediately after scene changes, and the effect was sustained on scenes where gaze clustered among participants (indicating engagement of attention). Females were more likely than males to blink during appearance of novel stimuli in both tasks, but only within the age bin of 18-44 years. The consistency of blink patterns in each paradigm endorses blinking as a sensitive index for changes in visual processing and attention, while sex and age differences drive interindividual variability.Significance Statement Eye-tracking is becoming useful as a non-invasive tool for detecting preclinical markers of neurological and psychiatric disease. Blinks are understudied despite being an important supplement to saccade and pupil eye-tracking metrics. The present study is a crucial step in developing a healthy baseline for blink behavior to compare to clinical groups. While many prior blink studies suffered from small sample sizes with relatively low age- and sex-diversity (review by Jongkees & Colzato, 2016), our large cohort of healthy participants has permitted a more detailed analysis of sex and age effects in blink behavior. Furthermore, our analysis techniques are robust to temporal changes in blink probability, greatly clarifying the relationship between blinking, visual processing, and inhibitory control mechanisms on visual tasks.

IdeNet: Making Neural Network Identify Camouflaged Objects Like Creatures.

Camouflaged objects often blend in with their surroundings, making the perception of a camouflaged object a more complex procedure. However, most neural-network-based methods that simulate the visual information processing pathway of creatures only roughly define the general process, which deficiently reproduces the process of identifying camouflaged objects. How to make modeled neural networks perceive camouflaged objects as effectively as creatures is a significant topic that deserves further consideration. After meticulous analysis of biological visual information processing, we propose an end-to-end prudent and comprehensive neural network, termed IdeNet, to model the critical information processing. Specifically, IdeNet divides the entire perception process into five stages: information collection, information augmentation, information filtering, information localization, and information correction and object identification. In addition, we design tailored visual information processing mechanisms for each stage, including the information augmentation module (IAM), the information filtering module (IFM), the information localization module (ILM), and the information correction module (ICM), to model the critical visual information processing and establish the inextricable association of biological behavior and visual information processing. The extensive experiments show that IdeNet outperforms state-of-the-art methods in all benchmarks, demonstrating the effectiveness of the five-stage partitioning of visual information processing pathway and the tailored visual information processing mechanisms for camouflaged object detection. Our code is publicly available at: https://github.com/whyandbecause/IdeNet.

The mechanism of visual processing for nonsalient stimuli in perceptual learning

The mechanism of visual processing for nonsalient stimuli in perceptual learning

Optimizing the algorithm for assessing the efficacy of treatment for amblyopia in astigmats through the investigation of the features of meridional acuity characteristics as vector quantities

Background: Refractive asymmetry associated with astigmatism may cause a special form of amblyopia, meridional amblyopia (MA). MA manifests itself as alterations in selective mechanisms of visual stimulus processing during recognition of contours of a certain orientation. Current routine examination of amblyopes does not include meridional acuity assessment and thus does not allow determining whether MA is present or not, as well as performing a more detailed evaluation of the efficacy of treatment for amblyopia in astigmats. Purpose: To optimize the algorithm for assessing the efficacy of stand-alone and complex methods of treatment for amblyopia in astigmats through the determination of the features of changes in visual acuities in orthogonal retinal meridians as vector quantities. Material and Methods: Twenty-four hyperopic astigmats with amblyopia (48 eyes) aged 5 to 12 years were involved in the study. Patients were treated by accommodative facility training only and, in 3 months, by complex therapy (accommodative facility training plus the use of device-based methods). Treatment course duration was 10 days. The results were assessed by changes in best-corrected visual acuity (Sivtsev Chart) and meridional separable visual acuity (MSVA) determined with the software which generates Landolt ring optotypes. Results: The study sample was found to be heterogeneous regarding the features of asymmetries in MSVA. Separable visual acuity in the horizontal meridian was equal in magnitude to that in the vertical meridian in 16.65% of patients. Asymmetries in separable visual acuity that may be considered MA were found in dominant and non-dominant (fellow) eyes in 83.35% of astigmats with amblyopia. Two different clusters were determined in the group with asymmetries in MSVA. Separable visual acuity in the horizontal meridian was higher than that in the vertical meridian in 35.45% of eyes in cluster 1, and lower than that in the vertical meridian in 47.85% of eyes in cluster 2. After treatment, mean separable visual acuity value in the vertical meridian was practically similar to that in the horizontal meridian in both groups for the dominant and fellow eyes, which allowed concluding that both methods were equally effective in treating the disease. However, the number of eyes with the same MSVA in orthogonal meridians increased by 10.41% after MA treatment with accommodative facility training only versus 18.75% after complex treatment. Conclusion: Assessing the features of changes in visual acuities in orthogonal retinal meridians as vector quantities enables obtaining principally new information on the performance of sensory functions in patients with amblyopia and assessing more reliably the efficacy of stand-alone and complex pleoptic methods as methods of treatment for MA.

Infrared UAV Target Detection Based on Continuous-Coupled Neural Network.

The task of the detection of unmanned aerial vehicles (UAVs) is of great significance to social communication security. Infrared detection technology has the advantage of not being interfered with by environmental and other factors and can detect UAVs in complex environments. Since infrared detection equipment is expensive and data collection is difficult, there are few existing UAV-based infrared images, making it difficult to train deep neural networks; in addition, there are background clutter and noise in infrared images, such as heavy clouds, buildings, etc. The signal-to-clutter ratio is low, and the signal-to-noise ratio is low. Therefore, it is difficult to achieve the UAV detection task using traditional methods. The above challenges make infrared UAV detection a difficult task. In order to solve the above problems, this work drew upon the visual processing mechanism of the human brain to propose an effective framework for UAV detection in infrared images. The framework first determines the relevant parameters of the continuous-coupled neural network (CCNN) through the image's standard deviation, mean, etc. Then, it inputs the image into the CCNN, groups the pixels through iteration, then obtains the segmentation result through expansion and erosion, and finally, obtains the final result through the minimum circumscribed rectangle. The experimental results showed that, compared with the existing most-advanced brain-inspired image-understanding methods, this framework has the best intersection over union (IoU) (the intersection over union is the overlapping area between the predicted segmentation and the label divided by the joint area between the predicted segmentation and the label) in UAV infrared images, with an average of 74.79% (up to 97.01%), and can effectively realize the task of UAV detection.

Manipulating the Fourier spectra of stimuli comprising a two-frame kinematogram to study early visual motion-detecting mechanisms: Perception versus short latency ocular-following responses.

Two-frame kinematograms have been extensively used to study motion perception in human vision. Measurements of the direction-discrimination performance limits (Dmax) have been the primary subject of such studies, whereas surprisingly little research has asked how the variability in the spatial frequency content of individual frames affects motion processing. Here, we used two-frame one-dimensional vertical pink noise kinematograms, in which images in both frames were bandpass filtered, with the central spatial frequency of the filter manipulated independently for each image. To avoid spatial aliasing, there was no actual leftward-rightward shift of the image: instead, the phases of all Fourier components of the second image were shifted by ±¼ wavelength with respect to those of the first. We recorded ocular-following responses (OFRs) and perceptual direction discrimination in human subjects. OFRs were in the direction of the Fourier components' shift and showed a smooth decline in amplitude, well fit by Gaussian functions, as the difference between the central spatial frequencies of the first and second images increased. In sharp contrast, 100% correct perceptual direction-discrimination performance was observed when the difference between the central spatial frequencies of the first and second images was small, deteriorating rapidly to chance when increased further. Perceptual dependencies moved closer to the OFR ones when subjects were allowed to grade the strength of perceived motion. Response asymmetries common for perceptual judgments and the OFRs suggest that they rely on the same early visual processing mechanisms. The OFR data were quantitatively well described by a model which combined two factors: (1) an excitatory drive determined by a power law sum of stimulus Fourier components' contributions, scaled by (2) a contrast normalization mechanism. Thus, in addition to traditional studies relying on perceptual reports, the OFRs represent a valuable behavioral tool for studying early motion processing on a fine scale.

Canonical circuit computations for computer vision.

Advanced computer vision mechanisms have been inspired by neuroscientific findings. However, with the focus on improving benchmark achievements, technical solutions have been shaped by application and engineering constraints. This includes the training of neural networks which led to the development of feature detectors optimally suited to the application domain. However, the limitations of such approaches motivate the need to identify computational principles, or motifs, in biological vision that can enable further foundational advances in machine vision. We propose to utilize structural and functional principles of neural systems that have been largely overlooked. They potentially provide new inspirations for computer vision mechanisms and models. Recurrent feedforward, lateral, and feedback interactions characterize general principles underlying processing in mammals. We derive a formal specification of core computational motifs that utilize these principles. These are combined to define model mechanisms for visual shape and motion processing. We demonstrate how such a framework can be adopted to run on neuromorphic brain-inspired hardware platforms and can be extended to automatically adapt to environment statistics. We argue that the identified principles and their formalization inspires sophisticated computational mechanisms with improved explanatory scope. These and other elaborated, biologically inspired models can be employed to design computer vision solutions for different tasks and they can be used to advance neural network architectures of learning.

Investigating app icon recognition with event-related potentials.

In modern society, visual symbols such as logos, icons, and letters have become essential for communication and cognition, playing a crucial role in daily life. This study focuses on app icons, a frequently encountered type of symbol, and aims to investigate the neural mechanisms involved in their recognition. Specifically, our objective is to identify the timing and location of brain activity associated with this process. We presented participants with familiar and unfamiliar app icons and asked them to perform a repetition detection task while recording the event-related potentials (ERPs) elicited by these stimuli. Statistical analysis revealed a significant difference in the ERPs between familiar and unfamiliar icons, occurring around 220 ms in the parietooccipital scalp region. The source analysis indicated that this ERP difference originated in the ventral occipitotemporal cortex, specifically the fusiform gyrus. These findings suggest that the recognition of familiar app icons results in the activation of the ventral occipitotemporal cortex approximately 220 ms after exposure. Additionally, our findings, in conjunction with previous research on visual word recognition, suggest that the lexical orthographic processing of visual words is dependent on general visual processing mechanisms that are also involved in the recognition of familiar app icons. In essence, the ventral occipitotemporal cortex likely plays a crucial role in memorizing and recognizing visual symbols and objects, including familiar visual words.

Conscious and unconscious processing of ensemble statistics oppositely modulate perceptual decision-making.

Our visual system possesses a remarkable ability to extract summary statistical information from groups of similar objects, known as ensemble perception. It remains elusive whether the processing of ensemble statistics exerts influences on our perceptual decision-making and what roles consciousness and attention play in this process. In a series of experiments, we demonstrated that the processing of ensemble statistics can exert significant modulation effects on our perceptual decision-making, which is independent of consciousness but relies on attentional resources. More intriguingly, the conscious and unconscious ensemble representations respectively induce repulsive and attractive modulation effects, with the unconscious effect susceptible to the temporal separation and the distinction between the inducers and the targets. These results not only suggest that the conscious and unconscious ensemble representations engage different visual processing mechanisms but also highlight the distinct roles of consciousness and attention in ensemble perception. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Colour and melanopsin mediated responses in the murine retina.

Introduction: Intrinsically photosensitive retinal ganglion cells (ipRGCs) integrate melanopsin and rod/cone-mediated inputs to signal to the brain. Whilst originally identified as a cell type specialised for encoding ambient illumination, several lines of evidence indicate a strong association between colour discrimination and ipRGC-driven responses. Thus, cone-mediated colour opponent responses have been widely found across ipRGC target regions in the mouse brain and influence a key ipRGC-dependent function, circadian photoentrainment. Although ipRGCs exhibiting spectrally opponent responses have also been identified, the prevalence of such properties have not been systematically evaluated across the mouse retina or yet been found in ipRGC subtypes known to influence the circadian system. Indeed, there is still uncertainty around the overall prevalence of cone-dependent colour opponency across the mouse retina, given the strong retinal gradient in S and M-cone opsin (co)-expression and overlapping spectral sensitivities of most mouse opsins. Methods: To address this, we use photoreceptor isolating stimuli in multielectrode recordings from human red cone opsin knock-in mouse (Opn1mwR) retinas to systematically survey cone mediated responses and the occurrence of colour opponency across ganglion cell layer (GCL) neurons and identify ipRGCs based on spectral comparisons and/or the persistence of light responses under synaptic blockade. Results: Despite detecting robust cone-mediated responses across the retina, we find cone opponency is rare, especially outside of the central retina (overall ~3% of GCL neurons). In keeping with previous suggestions we also see some evidence of rod-cone opponency (albeit even more rare under our experimental conditions), but find no evidence for any enrichment of cone (or rod) opponent responses among functionally identified ipRGCs. Conclusion: In summary, these data suggest the widespread appearance of cone-opponency across the mouse early visual system and ipRGC-related responses may be an emergent feature of central visual processing mechanisms.

Temporal dynamics of cognitive flexibility in adolescents with anorexia nervosa: A high-density EEG study.

Impairment in cognitive flexibility is a core symptom of anorexia nervosa (AN) and is associated with treatment resistance. Nevertheless, studies on the neural basis of cognitive flexibility in adolescent AN are rare. This study aimed to investigate brain networks underlying cognitive flexibility in adolescents with AN. To address this aim, participants performed a Dimensional Change Card Sorting task during high-density electroencephalography (EEG) recording. Anxiety was measured with the State-Trait Anxiety Inventory. Data were collected on 22 girls with AN and 23 controls. Evoked responses were investigated using global-spatial analysis. Adolescents with AN showed greater overall accuracy, fewer switch trial errors and reduced inverse efficiency switch cost relative to controls, although these effects disappeared after adjusting for trait and state anxiety. EEG results indicated augmented early visual orienting processing (P100) and subsequent impaired attentional mechanisms to task switching (P300b) in subjects with AN. During task switching, diminished activations in subjects with AN were identified in the posterior cingulate, calcarine sulcus and cerebellum, and task repetitions induced diminished activations in a network involving the medial prefrontal cortex, and several posterior regions, compared with controls. No significant associations were found between measures of cognitive flexibility and anxiety in the AN group. Findings of this study suggest atypical neural mechanisms underlying cognitive flexibility in adolescents with AN. More importantly, our findings suggest that different behavioural profiles in AN could relate to differences in anxiety levels. Future research should investigate the efficacy of cognitive training to rebalance brain networks of cognitive flexibility in AN.

Coarse-to-Fine Feedback Guidance Based Stereo Image Quality Assessment Considering Dominant Eye Fusion

Considering that the human brain always follows a coarse-to-fine (low-to-high spatial frequency) visual processing and fusion mechanism, we propose a coarse-to-fine feedback guidance based stereo image quality assessment (SIQA) network which considers a coarse-to-fine feedback guidance and adaptive dominant eye mechanism. The proposed network consists of two main sub-network streams, each of which has three branches to extract low, middle and high spatial frequency information in parallel. To better realize the guidance of the high-level features in the low spatial frequency branch to the low-level features in the high spatial frequency branch, an information feedback guidance module (IFGM) is proposed, which realizes a top-down guidance mechanism in each sub-network stream. Simultaneously, according to the theory of ocular dominance in human visual system (HVS), we design an adaptive bi-directional parallax-based binocular fusion module (BPBFM), which synthesizes two types of fusion feature by taking the left and right view features as dominant eye input. Furthermore, in order to obtain the better perceptual quality of stereo images, we design a weighted fusion strategy to weigh the quality scores from the two types of fusion features obtained by using an ensemble model with two multi-layer perceptrons (MLPs). The experimental results on four public stereo image datasets show that the proposed method is superior to the mainstream metrics and achieves an excellent performance.