Accurate Perception Research Articles

Expressed and Perceived Honesty Benefits Relationships Even When Couples Are Not Accurate

Honesty is a highly valued virtue, yet it can be challenging to enact when it may compromise our most valued relationships. We tested preregistered hypotheses examining the distinct effects of expressed, perceived, and accurate perceptions of honesty on well-being and change. Romantic partners ( Ncouples = 214; Nindividuals = 428) discussed a desired change in the lab and reported on outcomes concurrently and three months later. Honesty was self-reported and rated by observers, with correspondence between these measures observed. Results of multilevel response surface analyses showed that greater expressed and perceived honesty—but not accurate perceptions of honesty—predicted greater well-being, relationship satisfaction, and target motivation to change concurrently, with some benefits emerging over time. The current results indicate that honesty can benefit relationships even when the truth may hurt, with more expressed and perceived honesty fostering better relationships regardless of whether couples share in that perception of honesty.

Can Peer Acceptance and Perceptual Accuracy Impact the Effectiveness of Two Formats of a Preventative Intervention on Functional Subtypes of Aggression in Youth?

Coping Power (CP) is an empirically supported school-based intervention for children at risk for aggression. A child's social status with peers and the extent to which they accurately perceive it are important aspects of preadolescent social development that may influence how intervention format affects disruptive behavior outcomes. Further, reactive (RA) and proactive (PA) functional subtypes of aggression have differential relations with peer acceptance. This study is the first to test whether the effects of group (GCP) and individual (ICP) format of CP on RA and PA differed based on children's actual social status (aim 1) and whether they over- or underestimated their acceptance relative to their actual social status (perceptual accuracy; aim 2). This study involved secondary data analyses using a large-scale randomized controlled trial that assigned 360 children ages 9 to 11 (M = 9.74, SD = .62), predominantly male (n = 234, 65%), and Black (n = 273, 75.8%), with elevated levels of aggression to either ICP or GCP condition. Polynomial regression analyses and three-dimensional response surface plots tested and probed significant (p < .05) interactions between either actual acceptance or perceptual accuracy and intervention format on postintervention reactive and proactive aggression. Actual acceptance moderated the effects of GCP on RA, such that those with higher acceptance showed smaller reductions in RA from either preintervention or postintervention to follow-up. Perceptual accuracy also moderated the effects of ICP on PA, with those underestimating their acceptance showing smaller decreases in PA from postintervention to follow-up. These findings provide valuable insights into how children's actual peer acceptance and perceptual accuracy influence CP outcomes for different functional subtypes of aggression based on intervention format, raising important questions about potential mechanisms.

Perception Development of L2 English and L3 Polish Coda Obstruent by L1 German Adult Multilinguals

Research into L3 phonological acquisition has grown in the past decade, yet perceptual studies remain scarce. Existing studies report complex interactions between the phonetic categories of multilinguals’ L1, L2 and L3, depending on investigated feature and stage of L3 learning. This small-scale study, grounded in Complex Dynamic Systems Theory, examines the development of coda obstruent perception in seven beginner learners of Polish as an L3 (aged 21–39), with German as their L1 and English as their L2. Over ten months of instructed L3 learning, participants were tested four times using a timed forced-choice goodness task in both L2 and L3. Additionally, three participants provided monthly data between the second and fourth testing. Analyses across the sample revealed a competitive relationship between L2 and L3 perception, with L2 accuracy declining as L3 accuracy improved. Individual data, however, indicated more varied patterns: while one learner followed the overall trend, another exhibited decreasing accuracy in both their L2 and L3, and the third maintained accurate L2 perception alongside more accurate yet increasingly variable L3 perception. These findings highlight the value of analyzing both overall trends and individual data to better understand multilingual speech perception development, and suggest that, with growing L3 experience, the newly learnt L3 may influence L2 perception of a phonological process shared in the L1 and marked in the L2.

A Dual-Channel and Frequency-Aware Approach for Lightweight Video Instance Segmentation.

Video instance segmentation, a key technology for intelligent sensing in visual perception, plays a key role in automated surveillance, robotics, and smart cities. These scenarios rely on real-time and efficient target-tracking capabilities for accurate perception and intelligent analysis of dynamic environments. However, traditional video instance segmentation methods face complex models, high computational overheads, and slow segmentation speeds in time-series feature extraction, especially in resource-constrained environments. To address these challenges, a Dual-Channel and Frequency-Aware Approach for Lightweight Video Instance Segmentation (DCFA-LVIS) is proposed in this paper. In feature extraction, a DCEResNet backbone network structure based on a dual-channel feature enhancement mechanism is designed to improve the model's accuracy by enhancing the feature extraction and representation capabilities. In instance tracking, a dual-frequency perceptual enhancement network structure is constructed, which uses an independent instance query mechanism to capture temporal information and combines with a frequency-aware attention mechanism to capture instance features on different attention layers of high and low frequencies, respectively, to effectively reduce the complexity of the model, decrease the number of parameters, and improve the segmentation efficiency. Experiments show that the model proposed in this paper achieves state-of-the-art segmentation performance with few parameters on the YouTube-VIS dataset, demonstrating its efficiency and practicality. This method significantly enhances the application efficiency and adaptability of visual perception intelligent sensing technology in video data acquisition and processing, providing strong support for its widespread deployment.

Decision Tree Model-Based Security Situational Awareness Approach for Wireless Communication Networks

Network security situational awareness is gaining increasing attention due to its capability to globally and dynamically detect potential network security risks. However, traditional security situational awareness models often exhibit poor classification performance, resulting in lower-than-expected acceleration and scalability ratios. In this paper, we propose a novel security situational awareness approach for wireless communication networks based on a decision tree model. First, reconfigure the category division module to categorize the attack data into four different types. Then, using time windows to segment the data flow between the network and the host promotes the design of effective security event detection mechanisms in the model. Finally, a comprehensive network security situational awareness model was constructed at the joint level using decision tree algorithm. The experimental results show that the proposed method can significantly improve the acceleration ratio, and the space occupancy ratio can reach 80, indicating that the proposed method can have a high level of processing capability and accurate perception in network security situations, providing a guarantee for the security of wireless communication networks.

Analyzing Cross-Cultural and Interpersonal Factors Influencing Trolley Dilemma Judgments: Using Morality as Cooperation Theory

Scholars have been constantly interested in studying moral quandaries, including the trolley problem. Despite their widespread adoption, normative ethical theories have challenges in real-world scenarios. This study explores the impact of interpersonal and cross-cultural factors on trolley dilemma judgments through the Morality-as-Cooperation (MAC) model. The study mainly reviews pertinent literature and discusses how the MAC model applies to the trolley dilemma. It demonstrates how interpersonal relationships and cultural norms influence decision-making. Eastern civilizations, for instance, place a high value on communal welfare. Although it has limitations, the MAC model provides a more thorough framework than normative ethics. The MAC model is a prospective alternative, but further research is required to better comprehend moral dilemmas and bridge the disparity between theory and practice in morality research, particularly in enhancing the accuracy of interpersonal perception and methodological considerations.

An autonomous navigation method for orchard mobile robots based on octree 3D point cloud optimization.

Three-dimensional (3D) LiDAR is crucial for the autonomous navigation of orchard mobile robots, offering comprehensive and accurate environmental perception. However, the increased richness of information provided by 3D LiDAR also leads to a higher computational burden for point cloud data processing, posing challenges to real-time navigation. To address these issues, this paper proposes a 3D point cloud optimization method based on the octree data structure for autonomous navigation of orchard mobile robots. This approach includes two key components: 1) In terms of orchard mapping, the spatial indexing and segmentation features of the octree data structure are introduced. According to the sparsity and density of the point cloud, the 3D orchard map is adaptively divided and the key information of the orchard is retained. 2) In terms of path planning, by using octree nodes as the unit nodes for RRT* random tree expansion, an improved RRT* algorithm based on octree is proposed. Field experiments were conducted in a pear orchard based on this method. The experimental results show that: 1) The overall number of point cloud data points in the map was reduced by approximately 76.32%, while important features, including tree morphology, trellis structure, and road surface information, were fully preserved. 2) When different octree node resolutions were applied, the improved RRT* algorithm demonstrated significant improvements in path generation time, sampling point utilization, path length, and curvature. The lateral tracking error increased as the resolution of octree nodes decreased. At a resolution of 0.20m, the maximum average lateral tracking error was 0.079m, indicating strong path trackability. This method exhibits tremendous potential for processing large-scale 3D point cloud data and enhancing path planning efficiency, providing a valuable technical reference for the real-time autonomous navigation of mobile robots in complex orchard environments.

ASTHMAXcel perception: a mobile health application for training in the perception of airflow limitation

Objective ASTHMAXcel Perception is an expansion of prior ASTHMAXcel mobile health applications for children with asthma. ASTHMAXcel Perception was evaluated for its ability to improve asthma control and perception of airflow limitation. Methods Patients with asthma ages 15–21 were randomized to receive ASTHMAXcel Perception with peak expiratory flow (PEF) feedback or usual care (UC). A baseline visit was followed by 3 intervention sessions across 6 wk in the intervention group. Assessments in both groups were designed to occur at baseline, 6-week (primary time point), and 3-month follow-up. Self-reported asthma control (primary outcome), asthma quality of life, asthma knowledge, medication adherence, healthcare utilization, app usage, and perception of airflow limitation (measured by comparing self-reported PEF guess to actual PEF) were assessed. Results 45 patients were enrolled (PEF n = 22, UC n = 23). At 6 wk, the PEF group reported better asthma control than the UC group (18.7 vs 16.7, p < .05). In the PEF group, accurate perception increased from baseline to 6 wk (48% vs 77%, p = 0.01) and from baseline to 3 months (48% vs 74%, p = 0.009). Over-perception decreased from baseline to 6 wk (26% vs 5%, p = 0.041). The number of logins was associated with greater accuracy in perception of airflow limitation from baseline to 2 wk (r = 0.91, p = 0.002) and 2 wk to 4 wk (r = 0.77, p < .05). No significant differences were found in asthma quality of life, asthma knowledge, medication adherence, or healthcare utilization between the two groups. Conclusion ASTHMAXcel Perception improved asthma control and was associated with greater accuracy in perception of airflow limitation.

Eye Movements during Measurements of Visual Vertical in the Poststroke Subacute Phase.

The subjective visual vertical (VV), the visually estimated direction of gravity, is essential for assessing vestibular function and visuospatial cognition. In this study, we aimed to investigate the mechanisms underlying altered VV perception in stroke participants with unilateral spatial neglect (USN), specifically by examining their eye movement patterns during VV judgment tasks. Participants with USN demonstrated limited eye movement scanning along a rotating bar, often fixating on prominent ends, such as the top or bottom. This suggests a reflexive response to visually salient areas, potentially interfering with accurate VV perception. In contrast, participants without USN showed broader scanning around the center of the bar. Notably, participants with USN without frontal lobe lesions occasionally exhibited extended scanning that included the bar's center, which was associated with accurate VV judgments. These findings suggest that (1) a tendency to fixate on peripheral, prominent areas and (2) frontal lobe involvement in disengaging and redirecting spatial attention may influence VV perception in USN. Based on these results, targeted rehabilitation strategies that encourage individuals with USN to extend their visual scanning beyond prominent endpoints and include central areas could improve VV accuracy. This study highlights the specific eye movement behaviors contributing to VV misperception, emphasizing the importance of training that broadens scanning to improve VV perception effectively.

Playing stereoscopic video games enhances the precision but not the accuracy of depth perception

Playing stereoscopic video games enhances the precision but not the accuracy of depth perception

Using a YOLO Deep Learning Algorithm to Improve the Accuracy of 3D Object Detection by Autonomous Vehicles

This study presents an adaptation of the YOLOv4 deep learning algorithm for 3D object detection, addressing a critical challenge in autonomous vehicle (AV) systems: accurate real-time perception of the surrounding environment in three dimensions. Traditional 2D detection methods, while efficient, fall short in providing the depth and spatial information necessary for safe navigation. This research modifies the YOLOv4 architecture to predict 3D bounding boxes, object depth, and orientation. Key contributions include introducing a multi-task loss function that optimizes 2D and 3D predictions and integrating sensor fusion techniques that combine RGB camera data with LIDAR point clouds for improved depth estimation. The adapted model, tested on real-world datasets, demonstrates a significant increase in 3D detection accuracy, achieving a mean average precision (mAP) of 85%, intersection over union (IoU) of 78%, and near real-time performance at 93–97% for detecting vehicles and 75–91% for detecting people. This approach balances high detection accuracy and real-time processing, making it highly suitable for AV applications. This study advances the field by showing how an efficient 2D detector can be extended to meet the complex demands of 3D object detection in real-world driving scenarios without sacrificing computational efficiency.

Thermoelectric Gel Enabling Self-Powered Facial Perception for Expression Recognition and Health Monitoring.

By analyzing facial features to perform expression recognition and health monitoring, facial perception plays a pivotal role in noninvasive, real-time disease diagnosis and prevention. Current perception routes are limited by structural complexity and the necessity of a power supply, making timely and accurate monitoring difficult. Herein, a self-powered poly(vinyl alcohol)-gellan gum-glycerol thermogalvanic gel patch enabling facial perception is developed for monitoring emotions and atypical pathological states. Due to the high thermopower of 1.89 mV/K as well as excellent stretchability of 680%, the on-face-conformed thermoelectric gel can operate upon facial thermoelectric variation resulting from different interfacial contact statuses between the gel and face induced by facial muscle activities. With the aid of machine learning, the patch array delivers accurate perception of facial activities of 11 muscles in real time, achieving active expression recognition and health monitoring with the accuracy of 98 and 96%, respectively. This work provides a promising strategy of actively monitoring multisite physiological activities, advancing the development of intelligent wearable bioelectronics for physical or mental monitoring.

The Performance of Teachers Perceptions in Interpersonal Communication in Elementary School Students in Medan City

Perception plays a crucial role in interpersonal communication, acting as the foundation for how individuals interpret and understand the messages they receive from others. Accurate perception enables clear communication, fostering mutual understanding and strengthening relationships, as it allows individuals to respond appropriately to the messages conveyed. On the contrary, inaccurate perceptions can result in misunderstandings, misinterpretations, and conflicts, often undermining the quality of communication. Various factors, such as personal experiences, cultural background, social context, and individual values, significantly influence how we perceive others and the situations we encounter. These influences can shape our attitudes and interpretations, sometimes leading to biased or distorted perceptions. For instance, our past experiences may lead us to make assumptions about others based on limited information, while cultural differences can affect how we interpret non-verbal cues or tone of voice. Thus, it is essential to develop self-awareness, which involves recognizing and reflecting on our own biases and perspectives. Active listening, which involves paying close attention to verbal and non-verbal cues and asking clarifying questions when needed, is also key in ensuring that we accurately perceive the messages others are trying to convey. Additionally, respecting and appreciating different points of view helps promote empathy and openness in communication, enabling individuals to understand and engage with others more effectively. By honing these skills, individuals can improve the accuracy of their perceptions, ultimately enhancing the quality of their interpersonal communication and fostering more positive and productive relationships.

The relation between social anxiety and perceptions of likeability and friendship in adolescents

Background This study investigated how different social anxiety symptoms (i.e., worrying about negative evaluation versus avoidance tendencies) in adolescents are related to the perception accuracy of likeability by peers and friendships with peers. Method A community sample of 263 adolescents between 12 and 15 years old reported on their social anxiety symptoms. In addition, they estimated how much their peers liked them, indicated how much they liked their peers, and who their friends were in their classroom. Results Results showed that socially anxious adolescents who mainly worried about negative evaluations, underestimated their likeability by peers. Adolescents with strong social avoidance tendencies had a more accurate perception of their likeability and friendships; they were less liked by their peers and had fewer friends. Conclusion The results emphasize the importance of treating avoidance behavior in social anxiety since avoidance tendencies may not only maintain the social anxiety symptoms but are also related to a more negative judgment by others.

The algorithm for foggy weather target detection based on YOLOv5 in complex scenes

With the rapid development of urbanization and global climate warming, complex environments and adverse weather conditions pose significant challenges to the accuracy of object detection and driving safety in autonomous driving systems. Addressing the challenges of severe occlusion and noise interference in target detection within complex foggy scenes and considering the YOLOv5 model’s small size and fast processing capabilities, which meet the real-time processing demands in complex environments, it is particularly suited for resource-constrained vehicular systems. Consequently, this paper introduces the YOLOv5-RCBiW model tailored for vehicular vision perception aimed at enhancing feature extraction and recognition. Initially, the Receptive Field Block (RFB) is integrated with the Coordinate Attention (CA) mechanism to form the RFCA module, which emphasizes the importance of different features and optimizes receptive field spatial features. Furthermore, the Re-BiFPN module is constructed to enhance feature perception accuracy through bidirectional cross-scale connections and feature fusion, while the detection head at the P5 layer is replaced to improve recognition capabilities. Finally, a gradient gain loss function is introduced to reduce feature information loss and prevent model performance degradation, ensuring robustness and accuracy in complex environments. The comparative experimental results on the RTTS and Foggy Driving datasets indicate that the YOLOv5-RCBiW model significantly outperforms existing models in object detection accuracy under foggy and complex scenes. Additionally, in-vehicle experiments validate the model’s effectiveness and real-time performance in challenging environments.

Improving the Perception of Objects Under Daylight Foggy Conditions in the Surrounding Environment

Autonomous driving (AD) technology has seen significant advancements in recent years; however, challenges remain, particularly in achieving reliable performance under adverse weather conditions such as heavy fog. In response, we propose a multi-class fog density classification approach to enhance the AD system performance. By categorizing fog density into multiple levels (25%, 50%, 75%, and 100%) and generating separate datasets for each class using the CARLA simulator, we improve the perception accuracy for each specific fog density level and analyze the effects of varying fog intensities. This targeted approach offers benefits such as improved object detection, specialized training for each fog class, and increased generalizability. Our results demonstrate enhanced perception of various objects, including cars, buses, trucks, vans, pedestrians, and traffic lights, across all fog densities. This multi-class fog density method is a promising advancement toward achieving reliable AD performance in challenging weather, improving both the precision and recall of object detection algorithms under diverse fog conditions.

An Environment Recognition Algorithm for Staircase Climbing Robots

For deformed wheel-based staircase-climbing robots, the accuracy of staircase step geometry perception and scene mapping are critical factors in determining whether the robot can successfully ascend the stairs and continue its task. Currently, while there are LiDAR-based algorithms that focus either on step geometry detection or scene mapping, few comprehensive algorithms exist that address both step geometry perception and scene mapping for staircases. Moreover, significant errors in step geometry estimation and low mapping accuracy can hinder the ability of deformed wheel-based mobile robots to climb stairs, negatively impacting the efficiency and success rate of task execution. To solve the above problems, we propose an effective LiDAR-Inertial-based point cloud detection method for staircases. Firstly, we preprocess the staircase point cloud, mainly using the Statistical Outlier Removal algorithm to effectively remove the outliers in the staircase scene and combine the vertical angular resolution and spatial geometric relationship of LiDAR to realize the ground segmentation in the staircase scene. Then, we perform post-processing based on the point cloud map obtained from LiDAR SLAM, extract the staircase point cloud and project and fit the staircase point cloud by Ceres optimizer, and solve the dimensional information such as depth and height of the staircase by combining with the mean filtering method. Finally, we fully validate the effectiveness of the method proposed in this paper by conducting multiple sets of SLAM and size detection experiments in real different staircase scenarios.

Burnout among medical residents: key drivers and practical mitigating strategies.

Burnout is a prevalent phenomenon in medicine, affecting >50% of physicians and up to 60% of medical residents. This has negative consequences for both doctors' mental health and job satisfaction as well as patient care quality. While numerous studies have explored the causes, psychological effects, and workplace solutions, we aim to practicalize the issue from the perspectives of residents by discussing three key drivers of burnout and offering actionable, multipronged strategies that may be able to tackle these root causes effectively. From review of relevant literature and personal reflections/experiences, we identified three key factors that contribute to resident burnout, namely, (i) inherent physician attributes, (ii) mismatched expectations of the medical profession, and (iii) stressful nature of clinical work and residency training for junior physicians. We offer practical strategies that can be implemented by various stakeholders in a concerted manner to target these three areas, namely, to inculcate and foster accurate perception of the medical profession at the outset, develop psychological strength/resilience among medical residents, and make practical improvements to working and training environments.

Right middle occipital gyrus is associated with egocentric spatial orientation during body tilt: Evidence from a repetitive transcranial magnetic stimulation study.

Accurate perception of the orientation of external objects relative to the body, known as egocentric spatial orientation, is fundamental to performing action. Previously, we found via behavioural and magnetic resonance imaging voxel-based morphometry studies that egocentric spatial orientation is strongly distorted when the whole body is tilted with respect to gravity, and that the magnitude of this perceptual distortion is correlated with the grey matter volume of the right middle occipital gyrus (rMOG). In the present study, we further validated the association between the neural processing in the rMOG and the perceptual distortion by transiently suppressing neural activity in this region using low-frequency repetitive transcranial magnetic stimulation (rTMS) and evaluating the consequent effect on perceptual distortion. Our results showed that rTMS over the rMOG significantly reduced perceptual distortions when the body was tilted in the frontal plane, while it did not affect egocentric spatial orientation in the upright position. No significant changes in perceptual distortion were observed when rTMS was applied to another cortical candidate (the right temporo-parietal junction). These results provide evidence that neural processing in the rMOG is associated with body tilt-related perceptual distortion, suggesting that the rMOG may be engaged in egocentric spatial orientation related to gravitational information.

Adaptive curiosity about metacognitive ability.

Metacognition provides control and oversight to the process of acquiring and using knowledge. Efficient metacognition is essential to many aspects of daily life, from health care to finance and education. Across three experiments, we found a specific form of curiosity in humans about the quality of their own metacognition, using a novel approach that dissociates perceptual from metacognitive information searches. Observers displayed a strategic balance in their curiosity, alternating between a focus on perceptual accuracy and metacognitive performance. Depending on the context, this metacognitive curiosity was modulated by an internal evaluation of metacognition, leading to increased feedback requests when metacognition was likely to be inaccurate. Using an ideal observer model, we describe how this curiosity trade-off can arise naturally from a recursive evaluation and transformation of decisions' evidence. These results show that individuals are inherently curious about their metacognitive abilities and can compare perceptual and metacognitive precision to fine-tune performance monitoring. We propose that this form of curiosity may reflect humans' drive to refine their self-model. (PsycInfo Database Record (c) 2024 APA, all rights reserved).