Attentional Guidance Research Articles

Wing-shaped walls: A directional effect of obstacles on manual avoidance.

Visual information can be used to plan, start, and coordinate manual movements in obstacle avoidance. An intriguing example of visuomotor coordination is the effect of wing-shaped walls, in which walls are oriented away from or toward a moving agent. A historical story from medieval Japan recounts that wing-shaped walls disrupted the agent's movement more when oriented toward the agent than when oriented away from the agent. This study aimed at examining whether the disruptive effect of wing-shaped walls occurs in a schematic situation represented on a 2D plane. In this study, we conducted psychophysical experiments in which participants were asked to move a stylus from a start point to a goal while avoiding multiple line obstacles that were arranged alternately at a course. In the two experiments, we manipulated the orientation and the size of the visible parts of the obstacles systematically. We found that the obstacles oriented toward the agent produced frequent contacts with the agent and attracted manual movements to the endpoints of obstacles. We discussed possible interpretations of the results in the context of attentional guidance.

CFOA: Exploring transferable adversarial examples by content feature optimization with attention guidance

Deep neural networks are known to be highly susceptible to adversarial examples, created by adding small and carefully-designed perturbations on original images. In black-box scenarios, attacking neural networks is challenging since the information about models is not available. Generating transferable adversarial examples on surrogate models is a viable way. However, current mainstream transfer-based attacks seldomly consider the relationship between transferability and content features of the image. To effectively enhance the attack success rate at the feature level, we newly propose an attack method called Content Feature Optimization Attack (CFOA). This novel CFOA approach operates on white-box models, aiming to generate transferable adversarial examples by targeting the content feature representations of images in the feature space. The method involves the extraction and parameterization of content features, followed by the iterative generation of feature-level perturbations guided by attention loss and adversarial loss. Ultimately, the adversarial examples generated by CFOA exhibit strong transferability on other black-box models. Experimental results show that our method produces adversarial examples with higher success rates in transferability compared to state-of-the-art methods, and importantly, they can effectively evade certain defense mechanisms, presenting a challenge for adversarial defense.

Time course of attentional guidance by visual and verbal working memory representations

ABSTRACT Visual and verbal representations in working memory guide visual attention to a memory-matching item. It is well documented that visual cues have an advantage in target detection over verbal cues; however, little research has explored the differences in visual search processes and between attentional guidance by visual and verbal working memory. To address this issue, here we investigated the time course of attentional guidance through visual and verbal representations by comparing the inhibition of return effects. Previous research shows that both types of working memory items produce early facilitation effects and later inhibition effects, but there are lesser facilitation effects for verbal than for visual working memory guidance of attention. Results showed that in all conditions, attention was guided by memory representations, but no inhibition of return was observed. Thus, the robustness of previous findings on the time course of attentional guidance by working memory is low.

High visual salience of alert signals can lead to a counterintuitive increase of reaction times

It is often assumed that rendering an alert signal more salient yields faster responses to this alert. Yet, there might be a trade-off between attracting attention and distracting from task execution. Here we tested this in four behavioral experiments with eye-tracking using an abstract alert-signal paradigm. Participants performed a visual discrimination task (primary task) while occasional alert signals occurred in the visual periphery accompanied by a congruently lateralized tone. Participants had to respond to the alert before proceeding with the primary task. When visual salience (contrast) or auditory salience (tone intensity) of the alert were increased, participants directed their gaze to the alert more quickly. This confirms that more salient alerts attract attention more efficiently. Increasing auditory salience yielded quicker responses for the alert and primary tasks, apparently confirming faster responses altogether. However, increasing visual salience did not yield similar benefits: instead, it increased the time between fixating the alert and responding, as high-salience alerts interfered with alert-task execution. Such task interference by high-salience alert-signals counteracts their more efficient attentional guidance. The design of alert signals must be adapted to a “sweet spot” that optimizes this stimulus-dependent trade-off between maximally rapid attentional orienting and minimal task interference.

Automated Colorectal Polyps Detection from Endoscopic Images using MultiResUNet Framework with Attention Guided Segmentation

The early detection of colorectal polyps is crucial for the reduction of mortality rates. However, manually identifying polyps is time-consuming and expensive, increasing the risk of missing them. Our paper aims to address this issue by presenting an automated segmentation approach for colorectal polyps. This paper proposes a method that combines a skip connection with hybrid attention guidance (AG) using attention guidance (AG) and residual path frameworks to identify salient features. Furthermore, we augment test samples using original, horizontal flip, and vertical flip transformations to enhance model robustness through Test Time Augmentation (TTA). The model was trained with Kvasir-seg samples and evaluated on Kvasir-seg and CVC-ClinicDB datasets to gauge generalizability. A significant accuracy (0.9546), a Dice Similarity Coefficient (DSC) of 0.8557, a Cross-section over Union (IoU) of 0.8824, a Recall (0.8221), a Precision (0.8922), an area under Receiver Operating Characteristics (ROC-AUC) of 0.9454, and an area under Precision-Recall (AUC-PR) of 0.8717 were achieved without TTA. Through TTA integration, accuracy (0.9993), DSC (0.8663), IoU (0.8277), Recall (0.8060), Precision (0.9364), and ROC-AUC (0.9587) have been improved. A comparison of our framework with state-of-the-art models demonstrated its effectiveness and segmentation capabilities. Additionally, the proposed model contains only 0.47 million parameters and a weight size of 6.71 MB, illustrating its potential for clinical diagnostics. A computer-aided diagnosis (CAD) system improves patient outcomes by detecting colorectal polyps early and improving segmentation accuracy.

Differential effects of high-definition transcranial direct current stimulation (HD-tDCS) on attentional guidance by working memory in males with substance use disorder according to memory modality

Information stored in working memory can guide perception selection, and this process is modulated by cognitive control. Although previous studies have demonstrated that neurostimulation over the left dorsolateral prefrontal cortex (lDLPFC) contributes to restore cognitive control among individuals with substance use disorder (SUD), there remains an open question about the potential stimulation effects on memory-driven attention. To address this issue, the present study adopted a combined working memory/attention paradigm while employing high-definition transcranial direct current stimulation (HD-tDCS) to stimulate the lDLPFC. Observers were asked to maintain visual or audiovisual information in memory while executing a search task, while the validity of the memory contents for the subsequent search task could be either invalid or neutral. The results showed a faint memory-driven attentional suppression effect in sham stimulation only under the audiovisual condition. Moreover, anodal HD-tDCS facilitated attentional suppression effect in both the strength and temporal dynamics under the visual-only condition, whereas the effect was impaired or unchanged under the audiovisual condition. Surprisingly, cathodal HD-tDCS selectively improved temporal dynamics of the attentional suppression effect under the audiovisual condition. The present study revealed the differential enhancement of HD-tDCS on cognitive control over visual and audiovisual memory-driven attention among individuals with SUD.

Multi-scale features and attention guided for brain tumor segmentation

Brain tumor segmentation supplies a credible reference for clinical treatment and pathological research and facilitates practitioners to diagnose more accurately. However, since the randomness and complexity of tumor shape and location, automatic brain tumor segmentation remains an extremely challenging assignment. In this study, we build an end-to-end convolutional neural network with a U-shaped structure to implement the segmentation of three lesion regions. We propose a multi-scale context block and an attention guidance block to focus on the spatial information at different scales and the interdependence between feature channels to enhance network representations and boost the learning capability of the model. Specifically, the multi-scale context block draws rich feature information through 3D dilated convolution. The attention guidance block reduces the impact of learned redundant features and eliminates the interference of irrelevant regions in the overall global information. Our recommended approach is evaluated on the brain tumor segmentation 2020 validation data. The Dice scores of the enhancing tumor (ET), whole tumor (WT), and tumor core (TC) are 78.19%, 90.10%, and 83.98%, respectively. In addition, the practice is also carried out in 2019 online validation data, and the Dice scores of ET, WT, and TC are 77.31%, 89.64%, and 82.55%, respectively. Experimental results reveal that the recommended approach gains favorable performance in comparison with representative brain tumor segmentation approaches. Our present study would accurately and efficaciously segment the three brain lesion regions and has clinical practice value.

Directed Diffusion: Direct Control of Object Placement through Attention Guidance

Text-guided diffusion models such as DALLE-2, Imagen, and Stable Diffusion are able to generate an effectively endless variety of images given only a short text prompt describing the desired image content. In many cases the images are of very high quality. However, these models often struggle to compose scenes containing several key objects such as characters in specified positional relationships. The missing capability to ``direct'' the placement of characters and objects both within and across images is crucial in storytelling, as recognized in the literature on film and animation theory. In this work, we take a particularly straightforward approach to providing the needed direction. Drawing on the observation that the cross-attention maps for prompt words reflect the spatial layout of objects denoted by those words, we introduce an optimization objective that produces ``activation'' at desired positions in these cross-attention maps. The resulting approach is a step toward generalizing the applicability of text-guided diffusion models beyond single images to collections of related images, as in storybooks. Directed Diffusion provides easy high-level positional control over multiple objects, while making use of an existing pre-trained model and maintaining a coherent blend between the positioned objects and the background. Moreover, it requires only a few lines to implement.

Reducing Spatial Fitting Error in Distillation of Denoising Diffusion Models

Denoising Diffusion models have exhibited remarkable capabilities in image generation. However, generating high-quality samples requires a large number of iterations. Knowledge distillation for diffusion models is an effective method to address this limitation with a shortened sampling process but causes degraded generative quality. Based on our analysis with bias-variance decomposition and experimental observations, we attribute the degradation to the spatial fitting error occurring in the training of both the teacher and student model in the distillation. Accordingly, we propose Spatial Fitting-Error Reduction Distillation model (SFERD). SFERD utilizes attention guidance from the teacher model and a designed semantic gradient predictor to reduce the student's fitting error. Empirically, our proposed model facilitates high-quality sample generation in a few function evaluations. We achieve an FID of 5.31 on CIFAR-10 and 9.39 on ImageNet 64x64 with only one step, outperforming existing diffusion methods. Our study provides a new perspective on diffusion distillation by highlighting the intrinsic denoising ability of models.

Identifying Novel Coronavirus Pneumonia With CT Images: A Deep Learning Approach With Detail Upsampling and Attention Guidance

To construct a deep learning-based target detection method to help radiologists perform rapid diagnosis of lesions in the CT images of patients with novel coronavirus pneumonia (NCP) by restoring detailed information and mining local information. We present a deep learning approach that integrates detail upsampling and attention guidance. A linear upsampling algorithm based on bicubic interpolation algorithm was adopted to improve the restoration of detailed information within feature maps during the upsampling phase. Additionally, a visual attention mechanism based on vertical and horizontal spatial dimensions embedded in the feature extraction module to enhance the capability of the object detection algorithm to represent key information related to NCP lesions. Experimental results on the NCP dataset showed that the detection method based on the detail upsampling algorithm improved the recall rate by 1.07% compared with the baseline model, with the AP50 reaching 85.14%. After embedding the attention mechanism in the feature extraction module, 86.13% AP50, 73.92% recall, and 90.37% accuracy were achieved, which were better than those of the popular object detection models. The feature information mining of CT images based on deep learning can further improve the lesion detection ability. The proposed approach helps radiologists rapidly identify NCP lesions on CT images and provides an important clinical basis for early intervention and high-intensity monitoring of NCP patients.

Can natural scenes cue attention to multiple locations? Evidence from eye-movements in contextual cueing

Humans find visual targets more quickly when the target appears at the same location in a repeated configuration of other stimuli. However, when the target alternates between two locations in the repeated configuration, the benefit for visual search is smaller. This reduction of benefits has been explained as the result of an averaging of a benefit for one location and a cost for the other location. In two experiments, we investigated this two-target-locations effect in real-world scenes using high-resolution eye-tracking. Experiment 1 adapted a study in which subjects searched for a small “T” or “L” superimposed on real-world photographs. Half of the trials showed repeated scenes with one possible target location each; half showed novel scenes. We replicated the pronounced contextual cueing effect in real-world scenes. In Experiment 2, two conditions were added. In one of them, targets appeared in repeated scenes alternating between two possible locations per scene. In the other condition, targets appeared in repeated scenes but at new locations, constrained to one side of the screen. Subjects were faster to search for and identify a target in repeated scenes than in novel scenes, including when the scene was paired with two alternating target locations and (after extensive training) even when the scene only predicted the hemifield. Separate analyses on the two possible target locations resulted in rejection of the suggestion of costs for the additional target location, since the contextual cueing effect was present in the second half of the experiment for both the favored and the less favored target location. The eye-tracking data demonstrated that contextual cueing influences searching fixations, characteristic of attentional guidance, rather than responding fixations, characteristic of facilitation of response processes. Further, these data revealed that adding another possible target location leads to less guidance, rather than impeding response processes. Thus, this study delivers evidence for a flexible and attentional guidance mechanism that is able to prioritize more than one location in natural contexts.

External cues improve visual working memory encoding in the presence of salient distractors in schizophrenia.

People with schizophrenia (PSZ) are impaired in attentional prioritization of non-salient but relevant stimuli over salient distractors during visual working memory (VWM) encoding. Conversely, guidance of top-down attention by external predictive cues is intact. Yet, it is unknown whether this preserved ability can help PSZ encode more information in the presence of salient distractors. We employed a visuospatial change-detection task using four Gabor patches with differing orientations in 66 PSZ and 74 healthy controls (HCS). Two Gabor patches flickered which were designated either as targets or distractors and either a predictive or a non-predictive cue was displayed to manipulate top-down attention, resulting in four conditions. We observed significant effects of group, salience and cue as well as significant interactions of salience by cue, group by salience and group by cue. Across all conditions, PSZ stored significantly less information in VWM than HCS. PSZ stored significantly less non-flickering than flickering information with a non-predictive cue. However, PSZ stored significantly more flickering and non-flickering information with a predictive cue. Our findings indicate that control of attentional selection is impaired in schizophrenia. We demonstrate that additional top-down information significantly improves performance in PSZ. The observed deficit in attentional control suggests a disturbance of GABAergic inhibition in early visual areas. Moreover, our findings are indicative of a mechanism for enhancing attentional control in PSZ, which could be utilized by pro-cognitive interventions. Thus, the current paradigm is suitable to reveal both preserved and compromised cognitive component processes in schizophrenia.

The attentional guidance and facilitating effects of group behavioral cues on individual college pedestrians’ jaywalking decisions

Objective Jaywalking is an important cause of pedestrian-related automobile accidents. Exploring the factors that influence jaywalking behavior and suggesting appropriate improvement measures are critical for reducing automobile accidents involving pedestrians. Methods This study divided traffic situations into high-risk and low-risk situations. Each situation contained three visual attention cues: vehicle, traffic light, and group behavior. Based on this, the role of visual cues in guiding pedestrians’ attention and influencing their decisions during jaywalking was examined. Sixty participants, with an average age of 19, were recruited. They were shown 84 crosswalk videos randomly while their crossing decisions and eye movement data were recorded. Results In low-risk situations, pedestrians spent more attention on group behavioral cues when making jaywalking decisions. The rate of jaywalking increased with the number of other jaywalking pedestrians. In high-risk situations, the pedestrians’ total fixation duration at vehicle hazard cues was longer when making jaywalking decisions, and the jaywalking rate decreased. Conclusions The results indicate that pedestrians’ jaywalking decisions were based on other pedestrians’ illegal crossing cues and automatic associative processes in low-risk situations. The higher the number of people crossing the street, the higher the number of pedestrians illegally crossing the road. In high-risk situations, pedestrians paid more attention to vehicle hazard cues before making jaywalking decisions, and fewer illegal crossings. The jaywalking decisions were based on a risk assessment, a controlled analytical process. The results verify the effect of visual cues on pedestrians’ attentional guidance and decision-making in different traffic situations, as well as the effectiveness of visual attention in predicting decision intention. The findings provide a theoretical basis and data reference for pedestrian safety education and constructing an intelligent driving pedestrian trajectory prediction model.

Distracted by Previous Experience: Integrating Selection History, Current Task Demands and Saliency in an Algorithmic Model

AbstractAttention can be biased by previous learning and experience. We present an algorithmic-level model of this selection history bias in visual attention that predicts quantitatively how stimulus-driven processes, goal-driven control and selection history compete to control attention. In the model, the output of saliency maps as stimulus-driven guidance interacts with a history map that encodes learning effects and a goal-driven task control to prioritize visual features. The model works on coded features rather than image pixels which is common in many traditional saliency models. We test the model on a reaction time (RT) data from a psychophysical experiment. The model accurately predicts parameters of reaction time distributions from an integrated priority map that is comprised of an optimal, weighted combination of separate maps. Analysis of the weights confirms selection history effects on attention guidance. The model is able to capture individual differences between participants’ RTs and response probabilities per group. Moreover, we demonstrate that a model with a reduced set of maps performs worse, indicating that integrating history, saliency and task information are required for a quantitative description of human attention. Besides, we show that adding intertrial effect to the model (as another lingering bias) improves the model’s predictive performance.

A Multi-Path Semantic Segmentation Network Based on Convolutional Attention Guidance

Due to the efficiency of self-attention mechanisms in encoding spatial information, Transformer-based models have recently taken a dominant position among semantic segmentation methods. However, Transformer-based models have the disadvantages of requiring a large amount of computation and lacking attention to detail, so we look back to the CNN model. In this paper, we propose a multi-path semantic segmentation network with convolutional attention guidance (dubbed MCAG). It has a multi-path architecture, and feature guidance from the main path is used in other paths, which forces the model to focus on the object’s boundaries and details. It also explores multi-scale convolutional features through spatial attention. Finally, it captures both local and global contexts in spatial and channel dimensions in an adaptive manner. Extensive experiments were conducted on popular benchmarks, and it was found that MCAG surpasses other SOTA methods by achieving 47.7%, 82.51% and 43.6% mIoU on ADE20K, Cityscapes and COCO-Stuff, respectively. Specifically, the experimental results prove that the proposed model has high segmentation precision for small objects, which demonstrates the effectiveness of convolutional attention mechanisms and multi-path strategies. The results show that the CNN model can achieve good segmentation effects with a lower amount of calculation.

Semantically congruent bimodal presentation modulates cognitive control over attentional guidance by working memory.

Although previous studies have well established that audiovisual enhancement has a promoting effect on working memory and selective attention, there remains an open question about the influence of audiovisual enhancement on attentional guidance by working memory. To address this issue, the present study adopted a dual-task paradigm that combines a working memory task and a visual search task, in which the content of working memory was presented in audiovisual or visual modalities. Given the importance of search speed in memory-driven attentional suppression, we divided participants into two groups based on their reaction time (RT) in neutral trials and examined whether audiovisual enhancement in attentional suppression was modulated by search speed. The results showed that the slow search group exhibited a robust memory-driven attentional suppression effect, and the suppression effect started earlier and its magnitude was greater in the audiovisual condition than in the visual-only condition. However, among the faster search group, the suppression effect only occurred in the trials with longer RTs in the visual-only condition, and its temporal dynamics were selectively improved in the audiovisual condition. Furthermore, audiovisual enhancement of memory-driven attention evolved over time. These findings suggest that semantically congruent bimodal presentation can progressively facilitate the strength and temporal dynamics of memory-driven attentional suppression, and that search speed plays an important role in this process. This may be due to a synergistic effect between multisensory working memory representation and top-down suppression mechanism. The present study demonstrates the flexible role of audiovisual enhancement on cognitive control over memory-driven attention.

Attentional guidance by target-location probability cueing is largely inflexible, long-lasting, and distinct from inter-trial priming.

There is growing consensus that selection history strongly guides spatial attention and is distinct from current goals and physical salience. Here, we focused on target-location probability cueing: when the target is more likely to appear in one region, search performance gradually improves for targets appearing in that region. Probability cueing is thought to reflect a long-lasting, inflexible and implicit attentional bias. However, strong evidence for these claims is lacking. We re-examined them in four experiments. The target was more likely to appear in one than in the other regions during the learning phase, whereas all regions were equiprobable during the extinction phase. We manipulated set size in all experiments. Probability cueing reduced search slopes during learning and extinction, suggesting that the bias is attentional and long-lasting. Although intertrial priming from several previous trials had an influence, it did not account for all the effects. We also found the bias to be largely inflexible: informing participants that the probability imbalance during learning would be discontinued during extinction did not reduce the bias. Moreover, the acquired bias remained the default determinant of attentional priority when goal-directed guidance failed (i.e., when a cue instructing participants to start their search in a given region during the extinction phase was omitted or invalid). Finally, many more participants than predicted by chance showed awareness of the probability manipulation, although we could not establish whether awareness was associated with the bias. We conclude that probability cueing is a long-lasting and inflexible attentional bias, distinct from intertrial priming. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

A Weakly Supervised Segmentation Network Embedding Cross-Scale Attention Guidance and Noise-Sensitive Constraint for Detecting Tertiary Lymphoid Structures of Pancreatic Tumors.

The presence of tertiary lymphoid structures (TLSs) on pancreatic pathological images is an important prognostic indicator of pancreatic tumors. Therefore, TLSs detection on pancreatic pathological images plays a crucial role in diagnosis and treatment for patients with pancreatic tumors. However, fully supervised detection algorithms based on deep learning usually require a large number of manual annotations, which is time-consuming and labor-intensive. In this paper, we aim to detect the TLSs in a manner of few-shot learning by proposing a weakly supervised segmentation network. We firstly obtain the lymphocyte density maps by combining a pretrained model for nuclei segmentation and a domain adversarial network for lymphocyte nuclei recognition. Then, we establish a cross-scale attention guidance mechanism by jointly learning the coarse-scale features from the original histopathology images and fine-scale features from our designed lymphocyte density attention. A noise-sensitive constraint is introduced by an embedding signed distance function loss in the training procedure to reduce tiny prediction errors. Experimental results on two collected datasets demonstrate that our proposed method significantly outperforms the state-of-the-art segmentation-based algorithms in terms of TLSs detection accuracy. Additionally, we apply our method to study the congruent relationship between the density of TLSs and peripancreatic vascular invasion and obtain some clinically statistical results.

Predicting attentional allocation in real-world environments: The need to investigate crossmodal semantic guidance.

Real-world environments are multisensory, meaningful, and highly complex. To parse these environments in a highly efficient manner, a subset of this information must be selected both within and across modalities. However, the bulk of attention research has been conducted within sensory modalities, with a particular focus on vision. Visual attention research has made great strides, with over a century of research methodically identifying the underlying mechanisms that allow us to select critical visual information. Spatial attention, attention to features, and object-based attention have all been studied extensively. More recently, research has established semantics (meaning) as a key component to allocating attention in real-world scenes, with the meaning of an item or environment affecting visual attentional selection. However, a full understanding of how semantic information modulates real-world attention requires studying more than vision in isolation. The world provides semantic information across all senses, but with this extra information comes greater complexity. Here, we summarize visual attention (including semantic-based visual attention), crossmodal attention, and argue for the importance of studying crossmodal semantic guidance of attention. This article is categorized under: Psychology > Attention Psychology > Perception and Psychophysics.

The Preparatory Activation of Guidance Templates for Visual Search and of Target Templates in Non-Search Tasks.

Representations of task-relevant object attributes (attentional templates) control the adaptive selectivity of visual processing. Previous studies have demonstrated that templates involved in the guidance of attention during visual search are activated in a preparatory fashion prior to the arrival of visual search displays. The current study investigated whether such proactive mechanisms are also triggered in non-search tasks, where attentional templates do not mediate the guidance of attention towards targets amongst distractors but are still necessary for subsequent target recognition processes. Participants either searched for colour-defined targets among multiple distractors or performed two other non-search tasks where imperative stimuli appeared without competing distractors (a colour-based Go/NoGo task, and a shape discrimination task where target colour was constant and could thus be ignored). Preparatory activation of colour-selective templates was tracked by measuring N2pc components (markers of attention allocation) to task-irrelevant colour singleton probes flashed every 200 ms during the interval between target displays. As expected, N2pcs were triggered by target-coloured probes in the search task, indicating that a corresponding guidance template was triggered proactively. Critically, clear probe N2pcs were also observed in the Go/NoGo task, and even in the shape discrimination task in an attenuated fashion. These findings demonstrate that the preparatory activation of feature-selective attentional task settings is not uniquely associated with the guidance of visual search but is also present in other types of visual selection tasks where guidance is not required.