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In the future, automated cars may feature external human–machine interfaces (eHMIs) to communicate relevant information to other road users. However, it is currently unknown where on the car the eHMI should be placed. In this study, 61 participants each viewed 36 animations of cars with eHMIs on either the roof, windscreen, grill, above the wheels, or a projection on the road. The eHMI showed ‘Waiting’ combined with a walking symbol 1.2 s before the car started to slow down, or ‘Driving’ while the car continued driving. Participants had to press and hold the spacebar when they felt it safe to cross. Results showed that, averaged over the period when the car approached and slowed down, the roof, windscreen, and grill eHMIs yielded the best performance (i.e., the highest spacebar press time). The projection and wheels eHMIs scored relatively poorly, yet still better than no eHMI. The wheels eHMI received a relatively high percentage of spacebar presses when the car appeared from a corner, a situation in which the roof, windscreen, and grill eHMIs were out of view. Eye-tracking analyses showed that the projection yielded dispersed eye movements, as participants scanned back and forth between the projection and the car. It is concluded that eHMIs should be presented on multiple sides of the car. A projection on the road is visually effortful for pedestrians, as it causes them to divide their attention between the projection and the car itself.

While deep learning has become the go-to method for image denoising due to its impressive noise removal Retinal blood vessel segmentation presents several challenges, including limited labeled image data, complex multi-scale vessel structures, and susceptibility to interference from lesion areas. To confront these challenges, this work offers a novel technique that integrates attention mechanisms and a cascaded dilated convolution module (CDCM) within a multi-path U-Net architecture. First, a dual-path U-Net is developed to extract both coarse and fine-grained vessel structures through separate texture and structural branches. A CDCM is integrated to gather multi-scale vessel features, enhancing the model’s ability to extract deep semantic features. Second, a boosting algorithm that incorporates probability distribution attention (PDA) within the upscaling blocks is employed. This approach adjusts the probability distribution, increasing the contribution of shallow information, thereby enhancing segmentation performance in complex backgrounds and reducing the risk of overfitting. Finally, the output from the dual-path U-Net is processed through a feature refinement module. This step further refines the vessel segmentation by integrating and extracting relevant features. Results from experiments on three benchmark datasets, including CHASEDB1, DRIVE, and STARE, demonstrate that the proposed method delivers improved segmentation accuracy compared to existing techniques.

Spatial attention is a necessary cognitive process, allowing for the direction of limited capacity resources to varying locations in the visual field for improved visual processing. Thus, understanding how ageing influences these processes is vital. The current study explored the relationship between the spatial spread of attention and healthy ageing using an inhibition of return task to tap visual attention processing. This task allowed us to measure the spatial distribution of inhibition, and thus acted as a marker for attentional spread. Past research has indicated minimal age differences in inhibitory spread. However, these studies used placeholder stimuli, which may have restricted the range over which age differences could be reliably measured. To address this, in Experiment One, we measured the relationship between the spatial spread of inhibition and healthy ageing using a method which did not employ placeholders. In contrast to past research, an age difference in inhibitory spread was observed, where in comparison to younger adults, older adults exhibited a relatively restricted spread of attention. Experiment Two then confirmed these findings, by directly comparing inhibitory spread for placeholder present and placeholder absent conditions, across younger and older adults. Again, it was found that age differences in inhibitory spread emerged, but only in the placeholder absent condition. Possible reasons for the observed age differences in attention are discussed.

Building digital government is an important means for the government to improve the public service ability and optimize the business environment, which directly affects the production and operation activities of micro-enterprises. Based on the panel data of listed enterprises and municipal government portal website performance in China, this paper empirically investigates the impact of digital government development on enterprise total factor productivity (TFP) and the moderating effect of the local government’s attention distribution. The research results showed that digital government development significantly improved the enterprise TFP, and this conclusion remained unchanged after a series of robustness tests using instrumental variables, one-stage lag of explained variables, and debiased machine learning models. We also found that the greater the pressure faced by local governments and the longer the chief officials’ tenure, the more attention local governments paid to building digital government, and the more obvious the role of digital government development in promoting enterprise TFP. Heterogeneity test results showed that the information disclosure, online service, and public participation all had a positive effect on enterprise TFP, while the user experience had no effect on it. Digital government development had a more obvious role in promoting enterprise TFP of central and western regions, non-SOEs, and technology-intensive enterprises. Moreover, reducing enterprise rent-seeking, attracting new enterprise entry, and increasing enterprise R&D investment are important mechanisms for digital government development to improve enterprise TFP.

The user interface of vehicle interaction systems has become increasingly complex in recent years, which makes these devices important factors that contribute to accidents. Therefore, it is necessary to study the impact of dynamic complexity on the carrying capacity of secondary tasks under different traffic scenarios. First, we selected vehicle speed and vehicle spacing as influencing factors in carrying out secondary tasks. Then, the average single scanning time, total scanning time, and scanning times were selected as evaluation criteria, based on the theories of cognitive psychology. Lastly, we used a driving simulator to conduct an experiment under a car-following scenario and collect data on scanning behavior by an eye tracker, to evaluate the performance of the secondary task. The results show that the relationship between the total scanning time, scanning times, and the vehicle speed can be expressed by an exponential model, the relationship between the above two indicators and the vehicle spacing can be expressed by a logarithmic model, and the relationship with the total number of icons can be expressed by a linear model. Combined with the above relationships and the evaluation criteria for driving secondary tasks, the maximum number of icons at different vehicle speeds and vehicle spacings can be calculated to reduce the likelihood of accidents caused by attention overload.

The distribution of drivers’ visual attention prior to diverting focus from the driving task is critical for safety. The object of this study is to investigate drivers’ attention strategy before they occlude their vision for different durations under different driving scenarios. A total of 3 (scenarios) × 3 (durations) within-subjects design was applied. Twenty-three participants completed three durations of occlusion (0, 1, and 2 s) test drive in a motion-based driving simulator under three scenarios (urban, rural, motorway). Drivers’ occlusion behaviour, driving behaviour, and visual behaviour in 6 s before occlusion was analyzed and compared. The results showed that drivers tended to slow down and increased their attention on driving task to keep safety in occlusion 2 s condition. The distribution of attention differed among different driving scenarios and occlusion durations. More attention was directed to Forward position and Speedometer in occlusion conditions, and a strong shift in attention from Forward position to Road users and Speedometer was found in occlusion 2 s condition. Road users was glanced more frequently in urban road with a higher percentage of attention transitions from Forward position to Road users. While gaze switching to Speedometer with a higher intensity was found on motorway. It suggests that drivers could adapt their visual attention to driving demand and anticipate the development of upcoming situations by sampling enough driving-related information before eyes-off-road. Moreover, the adaptation and anticipation are in accordance with driving situation and expected eyes-off-road duration. Better knowledge about attentional strategies before attention away from road contributes to more efficient and safe interaction with additional tasks.

Background Noise pollution in coal mines is of great concern. Personal injuries directly or indirectly related to noise occur from time to time. Its effects impact the health and safety of coal mine workers. This study aimed to identify if and how the level of noise impacts miners’ safety behavior in underground coal mines. Methods In order to study the influence of noise on miners in the mining industry, we built a coal mine noise simulation experiment system, and set the noise test level at 50 dB ~ 120 dB according to the actual working environment at well. We divided the noise gradient into 8 categories and conducted 93 experiments, in which we aim to test miners’ attention distribution, fatigue, and reaction under each level, and the experimental results were analyzed by SPSS22.0 software. Results The results show that the increase of environmental noise level will have an impact on the attention, reaction, and fatigue. The noise is positively related to the fatigue, the noise is negatively related to the attention and reaction. In the noise environment, the sensitivity of the personnel to optic stimuli is higher than that to acoustic stimuli. The test indicators of attention, fatigue, and reaction will change significantly, when the noise level is greater than 70 ~ 80 dB. Conclusions From the perspective of accident prevention, the noise level can be controlled within the range of less than 70 ~ 80 dB, which can control the occurrence of accidents to a certain extent.

The present study examined how task priority influences operators’ scanning patterns and trust ratings toward imperfect automation. Previous research demonstrated that participants display lower trust and fixate less frequently toward a visual display for the secondary task assisted with imperfect automation when the primary task demanded more attention. One account for this phenomenon is that the increased primary task demand induced the participants to prioritize the primary task than the secondary task. The present study asked participants to perform a tracking task, system monitoring task, and resource management task simultaneously using the Multi-Attribute Task Battery (MATB) II. Automation assisted the system monitoring task with 70% reliability. Task load was manipulated via difficulty of the tracking task. Participants were explicitly instructed to either prioritize the tracking task over all other tasks (tracking priority condition) or reduce tracking performance (equal priority condition). The results demonstrate the effects of task load on attention distribution, task performance and trust ratings. Furthermore, participants under the equal priority condition reported lower performance-based trust when the tracking task required more frequent manual input (tracking condition), while no effect of task load was observed under the tracking priority condition. Task priority can modulate automation trust by eliminating the adverse effect of task load in a dynamic multitasking environment.

Cross-neurotype differences in social communication patterns contribute to high unemployment rates among adults with autism. Adults with autism can be unsuccessful in job searches or terminated from employment due to mismatches between their social attention behaviors and society's expectations on workplace communication. We propose a behavioral intervention concerning distribution of attention in triadic (three-way) conversations. Specifically, the objective is to determine whether providing personalized feedback to each individual with autism based on an analysis of their attention distribution behavior during an initial conversation session would cause them to modify their orientation behavior in a subsequent conversation session. Our system uses an unobtrusive head orientation estimation model to track the focus of attention of each individual. Head orientation sequences from a conversation session are analyzed based on five statistical domains (eg, maximum exclusion duration and average contact duration) representing different types of attention distribution behavior. An intervention is provided to a participant if they exceeded the nonautistic average for that behavior by at least 2 SDs. The intervention uses data analysis and video modeling along with a constructive discussion about the targeted behaviors. Twenty-four individuals with autism with no intellectual disabilities participated in the study. The participants were divided into test and control groups of 12 participants each. Based on their attention distribution behavior in the initial conversation session, 11 of the 12 participants in the test group received an intervention in at least one domain. Of the 11 participants who received the intervention, 10 showed improvement in at least one domain on which they received feedback. Independent t tests for larger test groups (df>15) confirmed that the group improvements are statistically significant compared with the corresponding controls (P<.05). Crawford-Howell t tests confirmed that 78% of the interventions resulted in significant improvements when compared individually against corresponding controls (P<.05). Additional t tests comparing the first conversation sessions of the test and control groups and comparing the first and second conversation sessions of the control group resulted in nonsignificant differences, pointing to the intervention being the main effect behind the behavioral changes displayed by the test group, as opposed to confounding effects or group differences. Our proposed behavioral intervention offers a useful framework for practicing social attention behavior in multiparty conversations that are common in social and professional settings.

A fast evacuation from buildings to emergency shelters is necessary and important after the occurrence of a disaster. We investigated the variations in physical behaviors and cognition processes while finding emergency shelter. The on-site emergency-shelter-finding experiments were conducted in Beijing, China. Participants performed the task by using a wearable eye-tracking device. We aimed to assess three eye metrics: fixation counts, mean fixation duration, and visual attention index, to perform cognitive searching analysis for the environmental elements. The results showed that most people spend more fixation time on digital maps (297.77 ± 195.90 ms) and road conditions (239.43 ± 114.91 ms) than signs (150.90 ± 81.70 ms), buildings (153.44 ± 41.15 ms), and plants (170.11 ± 47.60 ms). Furthermore, most participants exhibit hesitation and retracing behaviors throughout the wayfinding process. The participants with relatively rich disaster experience and a proactive personality exhibit better performance in the shelter-finding task, such as a shorter retracing distance (p = 0.007) and nearer destination (p = 0.037). Eye metrics, together with the questionnaire, can mirror the complexity and heterogeneity of evacuation behavior during emergency shelter-finding. In addition, this also provides insights for the optimization of guidance sign systems and improvements in emergency management.