Basic Visual Features Research Articles

Microstate Analyses to Study face Processing in Healthy Individuals and Psychiatric Disorders: A Review of ERP Findings.

Microstates represent brief periods of quasi-stable electroencephalography (EEG) scalp topography, offering insights into dynamic fluctuations in event-related potential (ERP) topographies. Despite this, there is a lack of a comprehensive systematic overview of microstate findings concerning cognitive face processing. This review aims to summarize ERP findings on face processing using microstate analyses and assess their effectiveness in characterizing face-related neural representations. A literature search was conducted for microstate ERP studies involving healthy individuals and psychiatric populations, utilizing PubMed, Google Scholar, Web of Science, PsychInfo, and Scopus databases. Twenty-two studies were identified, primarily focusing on healthy individuals (n = 16), with a smaller subset examining psychiatric populations (n = 6). The evidence reviewed in this study suggests that various microstates are consistently associated with distinct ERP stages involved in face processing, encompassing the processing of basic visual facial features to more complex functions such as analytical processing, facial recognition, and semantic representations. Furthermore, these studies shed light on atypical attentional neural mechanisms in Autism Spectrum Disorder (ASD), facial recognition deficits among emotional dysregulation disorders, and encoding and semantic dysfunctions in Post-Traumatic Stress Disorder (PTSD). In conclusion, this review underscores the practical utility of ERP microstate analyses in investigating face processing. Methodologies have evolved towards greater automation and data-driven approaches over time. Future research should aim to forecast clinical outcomes and conduct validation studies to directly demonstrate the efficacy of such analyses in inverse space.

Enhancing image sentiment analysis: A user-centered approach through user emotions and visual features

To bridge the gap between user understanding of emotions and image representation, this paper amalgamated a variety of user features to formulate an image sentiment analysis model. Through controlled user experiments, participants were engaged in discerning sentiment from abstract painting images. User visual features were derived from eye-tracking data collected during the experiment. This paper employed the FFM (Five Factor Model) and POMS (Profile of Mood States) methodologies to gather personality and mood data, which were utilized as user emotional features. Additionally, fundamental components of abstract painting were captured as basic image features. By synergizing features across these three dimensions, the study introduced the "User-Feature-Enhanced-Image-Sentiment-Model" (UFE-ISM) for image sentiment recognition. The research findings underscore the model's superior performance in sentiment classification on abstract painting. Notably, both basic image features and user visual features exhibited significant contributions to the image sentiment classification model.

A novel artificial visual system for motion direction detection in color images

In recent years, convolutional neural networks (CNNs) have dominated the field of computer vision. Compared to traditional methods, these neural network algorithms exhibit strong biomimetic performance advantages in complex visual tasks due to their brain-like structure. However, because some necessary neural characteristics are ignored, these neural network algorithms differ greatly from the computational mechanisms of the brain. This paper starts with extracting basic visual features such as motion direction information from the brain and abstracts, generalizes and models a novel artificial visual system (AVS) for detecting object motion direction in color images based on existing relevant neurophysiological knowledge. We propose a mathematical model and quantification mechanism for each component neuron that generates motion direction selectivity in the visual system using dendritic neuron models, spiking neural network concepts and neurophysiological knowledge of retinal direction-selective ganglion cell pathways. The experiment is based on one million instances of object motion under different environments of noise-free, static and dynamic random noise, dynamic salt-and-pepper noise, dynamic Gaussion noise, and dynamic light changing. In comparison with 4 famous CNNs, LeNet-5, EfficientNetB0, ResNet18, and RegNetX-200MF, we test and verify AVS’s effectiveness, efficiency and strong generalization ability as well as other biomimetic performance advantages including high biological rationality, learning-free capability, interpretability and ease-of-use etc. AVS demonstrates that neuroscience still has important implications for guiding and promoting the development of artificial intelligence technology. Furthermore, AVS firstly provides a successful quantitative reference case study for further understanding motion direction selectivity and other primary cortical encoding characteristics in the brain.

Long-Tailed Effect Study in Remote Sensing Semantic Segmentation Based on Graph Kernel Principles

The performance of semantic segmentation in remote sensing, based on deep learning models, depends on the training data. A commonly encountered issue is the imbalanced long-tailed distribution of data, where the head classes contain the majority of samples while the tail classes have fewer samples. When training with long-tailed data, the head classes dominate the training process, resulting in poorer performance in the tail classes. To address this issue, various strategies have been proposed, such as resampling, reweighting, and transfer learning. However, common resampling methods suffer from overfitting to the tail classes while underfitting the head classes, and reweighting methods are limited in the extreme imbalanced case. Additionally, transfer learning tends to transfer patterns learned from the head classes to the tail classes without rigorously validating its generalizability. These methods often lack additional information to assist in the recognition of tail class objects, thus limiting performance improvements and constraining generalization ability. To tackle the abovementioned issues, a graph neural network based on the graph kernel principle is proposed for the first time. By leveraging the graph kernel, structural information for tail class objects is obtained, serving as additional contextual information beyond basic visual features. This method partially compensates for the imbalance between tail and head class object information without compromising the recognition accuracy of head classes objects. The experimental results demonstrate that this study effectively addresses the poor recognition performance of small and rare targets, partially alleviates the issue of spectral confusion, and enhances the model’s generalization ability.

Sensorimotor mechanisms selective to numerosity derived from individual differences.

We have previously shown that after few seconds of adaptation by finger-tapping, the perceived numerosity of spatial arrays and temporal sequences of visual objects displayed near the tapping region is increased or decreased, implying the existence of a sensorimotor numerosity system (Anobile et al., 2016). To date, this mechanism has been evidenced only by adaptation. Here, we extend our finding by leveraging on a well-established covariance technique, used to unveil and characterize 'channels' for basic visual features such as colour, motion, contrast, and spatial frequency. Participants were required to press rapidly a key a specific number of times, without counting. We then correlated the precision of reproduction for various target number presses between participants. The results showed high positive correlations for nearby target numbers, scaling down with numerical distance, implying tuning selectivity. Factor analysis identified two factors, one for low and the other for higher numbers. Principal component analysis revealed two bell-shaped covariance channels, peaking at different numerical values. Two control experiments ruled out the role of non-numerical strategies based on tapping frequency and response duration. These results reinforce our previous reports based on adaptation, and further suggest the existence of at least two sensorimotor number channels responsible for translating symbolic numbers into action sequences.

Sensorimotor mechanisms selective to numerosity derived from individual differences

We have previously shown that after few seconds of adaptation by finger-tapping, the perceived numerosity of spatial arrays and temporal sequences of visual objects displayed near the tapping region is increased or decreased, implying the existence of a sensorimotor numerosity system (Anobile et al., 2016). To date, this mechanism has been evidenced only by adaptation. Here, we extend our finding by leveraging on a well-established covariance technique, used to unveil and characterize ‘channels’ for basic visual features such as colour, motion, contrast, and spatial frequency. Participants were required to press rapidly a key a specific number of times, without counting. We then correlated the precision of reproduction for various target number presses between participants. The results showed high positive correlations for nearby target numbers, scaling down with numerical distance, implying tuning selectivity. Factor analysis identified two factors, one for low and the other for higher numbers. Principal component analysis revealed two bell-shaped covariance channels, peaking at different numerical values. Two control experiments ruled out the role of non-numerical strategies based on tapping frequency and response duration. These results reinforce our previous reports based on adaptation, and further suggest the existence of at least two sensorimotor number channels responsible for translating symbolic numbers into action sequences.

Real Face Value: The Processing of Naturalistic Facial Expressions in the Macaque Inferior Temporal Cortex.

For primates, expressions of fear are thought to be powerful social signals. In laboratory settings, faces with fearful expressions have reliably evoked valence effects in inferior temporal cortex. However, because macaques use so called "fear grins" in a variety of different contexts, the deeper question is whether the macaque inferior temporal cortex is tuned to the prototypical fear grin, or to conspecifics signaling fear? In this study, we combined neuroimaging with the results of a behavioral task to investigate how macaques encode a wide variety of fearful facial expressions. In Experiment 1, we identified two sets of macaque face stimuli using different approaches; we selected faces based on the emotional context (i.e., calm vs. fearful), and we selected faces based on the engagement of action units (i.e., neutral vs. fear grins). We also included human faces in Experiment 1. Then, using fMRI, we found that the faces selected based on context elicited a larger valence effect in the inferior temporal cortex than faces selected based on visual appearance. Furthermore, human facial expressions only elicited weak valence effects. These observations were further supported by the results of a two-alternative, forced-choice task (Experiment 2), suggesting that fear grins vary in their perceived pleasantness. Collectively, these findings indicate that the macaque inferior temporal cortex is more involved in social intelligence than commonly assumed, encoding emergent properties in naturalistic face stimuli that transcend basic visual features. These results demand a rethinking of theories surrounding the function and operationalization of primate inferior temporal cortex.

Spoken language processing activates the primary visual cortex.

Primary visual cortex (V1) is generally thought of as a low-level sensory area that primarily processes basic visual features. Although there is evidence for multisensory effects on its activity, these are typically found for the processing of simple sounds and their properties, for example spatially or temporally-congruent simple sounds. However, in congenitally blind individuals, V1 is involved in language processing, with no evidence of major changes in anatomical connectivity that could explain this seemingly drastic functional change. This is at odds with current accounts of neural plasticity, which emphasize the role of connectivity and conserved function in determining a neural tissue's role even after atypical early experiences. To reconcile what appears to be unprecedented functional reorganization with known accounts of plasticity limitations, we tested whether V1's multisensory roles include responses to spoken language in sighted individuals. Using fMRI, we found that V1 in normally sighted individuals was indeed activated by comprehensible spoken sentences as compared to an incomprehensible reversed speech control condition, and more strongly so in the left compared to the right hemisphere. Activation in V1 for language was also significant and comparable for abstract and concrete words, suggesting it was not driven by visual imagery. Last, this activation did not stem from increased attention to the auditory onset of words, nor was it correlated with attentional arousal ratings, making general attention accounts an unlikely explanation. Together these findings suggest that V1 responds to spoken language even in sighted individuals, reflecting the binding of multisensory high-level signals, potentially to predict visual input. This capability might be the basis for the strong V1 language activation observed in people born blind, re-affirming the notion that plasticity is guided by pre-existing connectivity and abilities in the typically developed brain.

Prospective search time estimates reveal the strengths and limits of internal models of visual search.

Having an internal model of one's attention can be useful for effectively managing limited perceptual and cognitive resources. While previous work has hinted at the existence of an internal model of attention, it is still unknown how rich and flexible this model is, whether it corresponds to one's own attention or to a generic person-invariant schema, and whether it is specified as a list of facts and rules or alternatively as a probabilistic simulation model. To this end, we tested participants' ability to estimate their own behavior in a visual search task with novel displays. In six online experiments (four pre-registered), prospective search time estimates reflected accurate metacognitive knowledge of key findings in the visual search literature, including the set-size effect, higher efficiency of color over conjunction search, and the asymmetric contributions of target and distractor identities to search difficulty. In contrast, estimates were biased to assume serial search, and demonstrated little to no insight into sizeable effects of search asymmetries for basic visual features, and of target-distractor similarity. Together, our findings reveal a complex picture, where internal models of visual search are sensitive to some, but not all, of the factors that make some searches more difficult than others. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Users' photos of items can reveal their tastes in a recommender system

Recommender Systems (RS) are based on the generalization of the observed interactions of a population of users with a collection of items. Collaborative Filters (CF) give good results, but they degrade when there are few interactions to learn from. The alternative would be to observe some features of the users that could be linked to their tastes. However, specific information on users or items is often not available. In this research work, we explore how to exploit the photos of items taken by users. Our aim is to assign similar meanings to the photos of items with which the same group of users interacted. For this purpose, we define a multi-label classification task from images to sets of users. The classifier uses a general-purpose convolutional neural network to extract the basic visual features, followed by additional layers necessary to accomplish the learning task. To evaluate our proposal we compared it with CFs, using two tourism datasets that include: restaurants of six cities and points of interest of three locations. According to the experimentation carried out, the poor results achieved by CFs are outperformed by our proposal, which takes into account the visual and taste semantics of the available photos.

Decoding of the neural representation of the visual RGB color model.

RGB color is a basic visual feature. Here we use machine learning and visual evoked potential (VEP) of electroencephalogram (EEG) data to investigate the decoding features of the time courses and space location that extract it, and whether they depend on a common brain cortex channel. We show that RGB color information can be decoded from EEG data and, with the task-irrelevant paradigm, features can be decoded across fast changes in VEP stimuli. These results are consistent with the theory of both event-related potential (ERP) and P300 mechanisms. The latency on time course is shorter and more temporally precise for RGB color stimuli than P300, a result that does not depend on a task-relevant paradigm, suggesting that RGB color is an updating signal that separates visual events. Meanwhile, distribution features are evident for the brain cortex of EEG signal, providing a space correlate of RGB color in classification accuracy and channel location. Finally, space decoding of RGB color depends on the channel classification accuracy and location obtained through training and testing EEG data. The result is consistent with channel power value distribution discharged by both VEP and electrophysiological stimuli mechanisms.

Preparatory attention to visual features primarily relies on non-sensory representation

Prior knowledge of behaviorally relevant information promotes preparatory attention before the appearance of stimuli. A key question is how our brain represents the attended information during preparation. A sensory template hypothesis assumes that preparatory signals evoke neural activity patterns that resembled the perception of the attended stimuli, whereas a non-sensory, abstract template hypothesis assumes that preparatory signals reflect the abstraction of attended stimuli. To test these hypotheses, we used fMRI and multivariate analysis to characterize neural activity patterns when human participants were prepared to attend a feature and then select it from a compound stimulus. In an fMRI experiment using basic visual feature (motion direction), we observed reliable decoding of the to-be-attended feature from the preparatory activity in both visual and frontoparietal areas. However, while the neural patterns constructed by a single feature from a baseline task generalized to the activity patterns during stimulus selection, they could not generalize to the activity patterns during preparation. Our findings thus suggest that neural signals during attentional preparation are predominantly non-sensory in nature that may reflect an abstraction of the attended feature. Such a representation could provide efficient and stable guidance of attention.

Crossmodal correspondences between basic tastes and visual design features: A narrative historical review.

People tend to associate abstract visual features with basic taste qualities. This narrative historical review critically evaluates the literature on these associations, often referred to as crossmodal correspondences, between basic tastes and visual design features such as color hue and shape curvilinearity. The patterns, discrepancies, and evolution in the development of the research are highlighted while the mappings that have been reported to date are summarized. The review also reflects on issues of cross-cultural validity and deviations in the matching patterns that are observed when correspondences are assessed with actual tastants versus with verbal stimuli. The various theories that have been proposed to account for different classes of crossmodal correspondence are discussed, among which the statistical and affective (or emotional-mediation) accounts currently appear most promising. Several critical research questions for the future are presented to address the gaps that have been identified in the literature and help validate the popular theories on the origin and operations of visual-taste correspondences.

Visual features as carriers of abstract quantitative information.

Four experiments investigated the extent to which abstract quantitative information can be conveyed by basic visual features. This was done by asking observers to estimate and discriminate Pearson correlation in graphical representations where the first data dimension of each element was encoded by its horizontal position, and the second by the value of one of its visual features; perceiving correlation then requires combining the information in the two encodings via a common abstract representation. Four visual features were examined: luminance, color, orientation, and size. All were able to support the perception of correlation. Indeed, despite the strikingly different appearances of the associated stimuli, all gave rise to performance that was much the same: Just noticeable difference was a linear function of distance from complete correlation, and estimated correlation a logarithmic function of this distance. Performance differed only with regard to the level of noise in the feature, with these values compatible with estimates of channel capacity encountered in classic experiments on absolute perceptual magnitudes. These results suggest that quantitative information can be conveyed by visual features that are abstracted at relatively low levels of visual processing, with little representation of the original sensory property. It is proposed that this is achieved via an abstract parameter space in which the values in each perceptual dimension are normalized to have the same means and variances, with perceived correlation based on the shape of the joint probability density function of the resultant elements. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Sub-cortical areas process physical size but not numerical value.

A robust finding in the numerical cognition literature is that the physical size and the numerical value of two to-be-compared digits interact, resulting in a size congruity effect (SiCE). The current study focuses on the possible role of prestriate areas in a digit comparison Stroop-like task. In the visual pathway, prestriate areas refer to regions from the retina up to the primary visual cortex (V1). I hypothesized that processing of physical size, but not numerical value, begins already in prestriate areas. This is because physical size is a basic visual feature while the numerical value of a symbol is a learned convention that should be retrieved from long-term memory. Adult participants compared the size or the numerical value of two digits. Without participants' awareness, I projected the digits either to the same eye, or each digit to a different eye. The latter type of presentation prevents prestriate areas from taking part in comparing the digits. Therefore, slower aresponse time under this condition hints atthe involvement of prestriate areas. Evidence confirmed the initial hypothesis, demonstrating slower performance when the stimuli are segregated between the eyes but only for physical size comparisons. This finding suggests that at least the initial processing of physical size, when relevant, is done before, and by different neural substrate than numerical value. The implications of the study and future directions are discussed.

Asymmetrical cross-modal influence on neural encoding of auditory and visual features in natural scenes

Natural scenes contain multi-modal information, which is integrated to form a coherent perception. Previous studies have demonstrated that cross-modal information can modulate neural encoding of low-level sensory features. These studies, however, mostly focus on the processing of single sensory events or rhythmic sensory sequences. Here, we investigate how the neural encoding of basic auditory and visual features is modulated by cross-modal information when the participants watch movie clips primarily composed of non-rhythmic events. We presented audiovisual congruent and audiovisual incongruent movie clips, and since attention can modulate cross-modal interactions, we separately analyzed high- and low-arousal movie clips. We recorded neural responses using electroencephalography (EEG), and employed the temporal response function (TRF) to quantify the neural encoding of auditory and visual features. The neural encoding of sound envelope is enhanced in the audiovisual congruent condition than the incongruent condition, but this effect is only significant for high-arousal movie clips. In contrast, audiovisual congruency does not significantly modulate the neural encoding of visual features, e.g., luminance or visual motion. In summary, our findings demonstrate asymmetrical cross-modal interactions during the processing of natural scenes that lack rhythmicity: Congruent visual information enhances low-level auditory processing, while congruent auditory information does not significantly modulate low-level visual processing.

Temporal dynamics of the neural representation of hue and luminance polarity

Hue and luminance contrast are basic visual features. Here we use multivariate analyses of magnetoencephalography data to investigate the timing of the neural computations that extract them, and whether they depend on common neural circuits. We show that hue and luminance-contrast polarity can be decoded from MEG data and, with lower accuracy, both features can be decoded across changes in the other feature. These results are consistent with the existence of both common and separable neural mechanisms. The decoding time course is earlier and more temporally precise for luminance polarity than hue, a result that does not depend on task, suggesting that luminance contrast is an updating signal that separates visual events. Meanwhile, cross-temporal generalization is slightly greater for representations of hue compared to luminance polarity, providing a neural correlate of the preeminence of hue in perceptual grouping and memory. Finally, decoding of luminance polarity varies depending on the hues used to obtain training and testing data. The pattern of results is consistent with observations that luminance contrast is mediated by both L-M and S cone sub-cortical mechanisms.

Rapid detection of social interactions is the result of domain general attentional processes.

Using visual search displays of interacting and non-interacting pairs, it has been demonstrated that detection of social interactions is facilitated. For example, two people facing each other are found faster than two people with their backs turned: an effect that may reflect social binding. However, recent work has shown the same effects with non-social arrow stimuli, where towards facing arrows are detected faster than away facing arrows. This latter work suggests a primary mechanism is an attention orienting process driven by basic low-level direction cues. However, evidence for lower level attentional processes does not preclude a potential additional role of higher-level social processes. Therefore, in this series of experiments we test this idea further by directly comparing basic visual features that orient attention with representations of socially interacting individuals. Results confirm the potency of orienting of attention via low-level visual features in the detection of interacting objects. In contrast, there is little evidence for the representation of social interactions influencing initial search performance.

Analysis of visual impression based on Bra's basic components

PurposeThe purpose of this paper is to discuss the relationship between bra's visual impression and bra parts, and then to explore the application of visual impression in bra design.Design/methodology/approachFirstly, 82 female undergraduates are asked to answered this questionnaire online, which is about the importance of parts in bra design. In the part of data analysis, the method of principal part analysis (PCA) are used to get the relationship between bra's parts, and reduce dimension of factors that influence bra design. After that, those group of features are further discussed from the perspective of visual design. Finally, design an application based on conclusion.FindingsTo get the influence features of bra appearance and improve the visual design effect, this paper matches the bra parts with visual features (color, texture, shape and space) and presents four main features of bra design: “color,” “visual texture,” “design shape” and “spatial expression” together with corresponding bra parts and technique of expression. Moreover, user interface in bra cloud customization is designed.Practical implicationsThe conclusion, which shows the corresponding relationship between bra visual effect and its basic parts, has an important role in bra visual design. First, it can be useful for design idea with different technique of expression, which may supply a theoretical basis for design. Secondly, the combination of bra parts and visual features can be used to evaluate the appearance.Originality/valueDiscussing the bra visual impression based on bra's basic parts and visual features provides a theoretical method for bra design and its appearance evaluation.

Visual stimulus features that elicit activity in object-vector cells

Object-vector (OV) cells are cells in the medial entorhinal cortex (MEC) that track an animal’s distance and direction to objects in the environment. Their firing fields are defined by vectorial relationships to free-standing 3-dimensional (3D) objects of a variety of identities and shapes. However, the natural world contains a panorama of objects, ranging from discrete 3D items to flat two-dimensional (2D) surfaces, and it remains unclear what are the most fundamental features of objects that drive vectorial responses. Here we address this question by systematically changing features of experimental objects. Using an algorithm that robustly identifies OV firing fields, we show that the cells respond to a variety of 2D surfaces, with visual contrast as the most basic visual feature to elicit neural responses. The findings suggest that OV cells use plain visual features as vectorial anchoring points, allowing vector-guided navigation to proceed in environments with few free-standing landmarks.