Allocation Of Visual Attention Research Articles

Less Is More: Higher-Skilled Sim Racers Allocate Significantly Less Attention to the Track Relative to the Display Features than Lower-Skilled Sim Racers.

Simulated (sim) racing is an emerging esport that has garnered much interest in recent years and has been a relatively under-researched field in terms of expertise and performance. When examining expertise, visual attention has been of particular interest to researchers, with eye tracking technology commonly used to assess visual attention. In this study, we examined the overt visual attention allocation of high- and low-skilled sim racers during a time trial task using Tobii 3 glasses. In the study, 104 participants were tested on one occasion, with 88 included in the analysis after exclusions. Participants were allocated to either group according to their fastest lap times. Independent t-tests were carried out with sidak corrections to test our hypotheses. Our results indicate that when eye tracking metrics were normalised to the lap time and corner sector time, there was a difference in the relative length of overt attention allocation (fixation behaviour) as lower-skilled racers had significantly greater total fixation durations in laps overall and across corner sectors when normalised (p = 0.013; p = 0.018). Interestingly, high- and low-skilled sim racers differed in where they allocated their attention during the task, with high-skilled sim racers allocating significantly less overt attention to the track relative to other areas of the display (p = 0.003). This would allow for higher-skilled racers to obtain relatively more information from heads-up display elements in-game, all whilst driving at faster speeds. This study provides evidence that high-skilled sim racers appear to need significantly less overt attention throughout a fast lap, and that high- and low-skilled sim racers differ in where they allocate their attention while racing.

Visual Attention to Semantic and Orthographic Associations in Fluent Aphasia: Evidence from Eye-Tracking.

We examined the allocation of visual attention in an association task by both neurotypical participants (n = 11) and adults with fluent aphasia (n = 12). Participants were presented with a picture (e.g., a basket) followed by a semantically related association (i.e., "strawberry") and an orthographically related association (i.e., "b," the first letter of "basket"). An eye tracker recorded their eye movements for three areas of interest (AOI): the picture, the semantic associate, and the orthographic associate, over 1396 observations. Results showed that both neurotypical participants and participants with aphasia looked longer at the semantic associate than at the picture, and this difference was more pronounced for neurotypicals than for people with aphasia (PWA). Neurotypicals also looked longer than the PWA group at the orthographic associate than at the prior picture. Regarding eye fixation counts, both participant groups looked more frequently at the semantic associate than at either the picture or the orthographic associate. Notably, this pattern was more pronounced among neurotypical participants than PWA. Our findings emphasize the importance of semantic associations in fluent aphasia and suggest a potential rehabilitative approach in speech and language therapy.

Alpha oscillations encode Bayesian belief updating underlying attentional allocation in dynamic environments

In a dynamic environment, expectations of the future constantly change based on updated evidence and affect the dynamic allocation of attention. To further investigate the neural mechanisms underlying attentional expectancies, we employed a modified Central Cue Posner Paradigm in which the probability of cues being valid (that is, accurately indicated the upcoming target location) was manipulated. Attentional deployment to the cued location (α), which was governed by precision of predictions on previous trials, was estimated using a hierarchical Bayesian model and was included as a regressor in the analyses of electrophysiological (EEG) data. Our results revealed that before the target appeared, alpha oscillations (8∼13 Hz) for high-predictability cues (88 % valid) were significantly predicted by precision-dependent attention (α). This relationship was not observed under low-predictability conditions (69 % and 50 % valid cues). After the target appeared, precision-dependent attention (α) correlated with alpha band oscillations only in the valid cue condition and not in the invalid condition. Further analysis under conditions of significant attentional modulation by precision suggested a separate effect of cue orientation. These results provide new insights on how trial-by-trial Bayesian belief updating relates to alpha band encoding of environmentally-sensitive allocation of visual spatial attention.

Effects of smartwatch usage on Children’s street-crossing behavior and visual attention allocation: An experimental study on street crossing in a real road environment

The use of smartwatches is facilitating the lives of children. However, smartwatch usage while crossing a street may lead to distractions and traffic safety hazards. The purpose of this study was to investigate the effects of smartwatch usage on the street-crossing behavior and visual strategies of children. Twenty-two children were recruited, and 220 street-crossing experiments were performed in real road environments, including at signalized and unsignalized intersections. ANOVA and Kruskal–Wallis H test calibrations were used to analyze the street-crossing behavior between the control group (who performed a task without smartwatch distraction) and the experimental group (who performed four different tasks with smartwatch distraction), and to visualize and compare the visual behavior data of children crossing the street. The results showed that children wearing smartwatches crossed the street more slowly and had a relatively narrow range of visual search, focusing on only a small area of the zebra crossing around them and the crosswalk area within their sight, whereas children who did not wear smartwatches additionally focused on areas with dangerous oncoming traffic on the left and right sides. Children wearing smartwatches were under various mental workloads and distraction levels as they performed the various distracting tasks, with listening to stories being the least distracting and least intensive, followed by answering phone calls and viewing images; a simple arithmetic operation was the most distracting and intensive task. Children while performing the distracting tasks showed a lower street-crossing speed and scanned left and right fewer times; viewing images and performing the simple arithmetic task also increased their fixation time on the smartwatches, further affecting their street-crossing behavior and thus increasing the risk of collision. At signalized intersections, children paid more attention to hazardous oncoming traffic areas and exhibited cautious crossing behaviors compared with that at unsignalized intersections. This study elucidated the characteristics of the effects of smartwatch usage on the street-crossing behavior of children and the influence pattern of the visual attention allocation and revealed the hazards of smartwatch usage on the street-crossing safety. It can contribute to improving the safety cognition of children distracted by smartwatch usage and reducing the occurrence of distracting behaviors while crossing streets. The results can provide a theoretical basis for scientific interventions and safety improvement measures.

Event-related brain potentials reveal enhancing and compensatory mechanisms during dual neurocognitive and cycling tasks

BackgroundVarious neurocognitive tests have shown that cycling enhances cognitive performance compared to resting. Event-related potentials (ERPs) elicited by an oddball or flanker task have clarified the impact of dual-task cycling on perception and attention. In this study, we investigate the effect of cycling on cognitive recruitment during tasks that involve not only stimulus identification but also semantic processing and memory retention.MethodsWe recruited 24 healthy young adults (12 males, 12 females; mean age = 22.71, SD = 1.97 years) to perform three neurocognitive tasks (namely color-word matching, arithmetic calculation, and spatial working memory) at rest and while cycling, employing a within-subject design with rest/cycling counterbalancing.Results The reaction time on the spatial working memory task was faster while cycling than at rest at a level approaching statistical significance. The commission error percentage on the color–word matching task was significantly lower at rest than while cycling. Dual-task cycling while responding to neurocognitive tests elicited the following results: (a) a greater ERP P1 amplitude, delayed P3a latency, less negative N4, and less positivity in the late slow wave (LSW) during color-word matching; (b) a greater P1 amplitude during memory encoding and smaller posterior negativity during memory retention on the spatial working memory task; and (c) a smaller P3 amplitude, followed by a more negative N4 and less LSW positivity during arithmetic calculation.ConclusionThe encoding of color-word and spatial information while cycling may have resulted in compensatory visual processing and attention allocation to cope with the additional cycling task load. The dual-task cycling and cognitive performance reduced the demands of semantic processing for color-word matching and the cognitive load associated with temporarily suspending spatial information. While dual-tasking may have required enhanced semantic processing to initiate mental arithmetic, a compensatory decrement was noted during arithmetic calculation. These significant neurocognitive findings demonstrate the effect of cycling on semantic-demand and memory retention-demand tasks.

Operator visual attention allocation prediction in a robotic arm teleoperation interface

AbstractIn digital interactive interfaces with high visual workloads, it is important for operators to allocate their limited attentional resources appropriately to ensure efficient information collection. The salience, effort, expectancy, value (SEEV) model, which combines top‐down and bottom‐up attention mechanisms for predicting attention allocation, has been validated in research areas such as piloting, driving, and surgical operations. However, the validity of the SEEV model in the field of robotic arm teleoperation has not yet been thoroughly studied. The primary purpose of this study was to confirm the feasibility of the SEEV model for operator visual attention allocation prediction in a robotic arm teleoperation scenario. The improved ITTI algorithm, distance‐measuring tool, Delphi method, and lowest ordinal algorithm were adopted to qualify the four factors of the SEEV model, which also contributed to salience and expectancy quantification methods. Accordingly, an attention allocation prediction model in a robotic arm teleoperation scene was constructed. To verify the validity of the prediction model, 20 participants were recruited to control the robotic arm using V‐REP simulation software, and their fixation durations were recorded using an eye tracker as an attention allocation indicator. Participants controlled the robotic arm according to the experimental requirements and operational tasks, such as grasping and placing the target. The results demonstrated that the theoretical data based on the SEEV prediction model are significantly related to the proportion of fixation durations. The experiment verifies the suitability of the SEEV prediction model, and it is anticipated to be utilized in the optimization of interactive interfaces for robotic arm teleoperation.

Driver's visual fixation attention prediction in dynamic scenes using hybrid neural networks

The traffic driving scenario is dynamically varying and experienced drivers can allocate visual attention effectively in brief time intervals, focusing on prominent targets and areas in advance, thus ensuring safe driving. Therefore, it is significant to research the behavior of driver's visual attention allocation in the development of driver assistance systems and autonomous vehicles. Recently, inspired by top-down and bottom-up attention mechanisms, most visual attention models based on neural networks have been proposed. However, these models tend to yield highly distributed predictions for the specific driving scenario, and have relatively low prediction accuracy, making them unfeasible for being used in practice. Hence, to overcome these challenges, we propose a driver's visual attention model (DVAM) built on the encoder-decoder architecture with the utilization of the convolutional neural network (CNN) and the recurrent neural networks (RNN). Specifically, the model leverages the temporal and spatial dimension information to better mimic realistic dynamic driving, and extract abundant feature information, thus ensuring that the final predictions are authentic and effective. Then, extensive experiments demonstrate that our suggested DVAM achieves better robustness and precision in predicting driver attention to areas or targets compared to state-of-the-art methods on the DR(eye)VE dataset. Finally, our model is deployed on the TDV dataset to validate its generalization capabilities, which prove the potential for field adaptation.

A Meta-Analytic Approach to Investigating the Relationship Between Human-Automation Trust and Attention Allocation

Trust and attention allocation are pivotal determinants in human-automation interaction. However, there are scarce empirical findings regarding the relationship between trust and attention allocation. Observations from our previous work suggested there may be a negative correlation between trust in automation and eye movement towards automation, though no formal analysis of these data had been conducted to quantify this relationship. The present meta-analysis examined the relationship between three dimensions of trust in automation (performance, process, and purpose) and visual attention allocation to the automation. Specifically, we applied Cumming’s (2014) meta-analysis technique to combine evidence across three experiments. Results indicated a negative correlation between trust in automation and visual sampling of the automated system monitoring task for performance-based trust, but not for process- or purpose-based trust. These findings suggest that operators scanned the automation’s behavior less frequently when indicating higher performance-based trust towards the automation.

Towards Attention–aware Foveated Rendering

Foveated graphics is a promising approach to solving the bandwidth challenges of immersive virtual and augmented reality displays by exploiting the falloff in spatial acuity in the periphery of the visual field. However, the perceptual models used in these applications neglect the effects of higherlevel cognitive processing, namely the allocation of visual attention, and are thus overestimating sensitivity in the periphery in many scenarios. Here, we introduce the first attention-aware model of contrast sensitivity. We conduct user studies to measure contrast sensitivity under different attention distributions and show that sensitivity in the periphery drops significantly when the user is required to allocate attention to the fovea. We motivate the development of future foveation models with another user study and demonstrate that tolerance for foveation in the periphery is significantly higher when the user is concentrating on a task in the fovea. Analysis of our model predicts significant bandwidth savings over those afforded by current models. As such, our work forms the foundation for attention-aware foveated graphics techniques.

Does the Game Matter? Analyzing Sponsorship Effectiveness and Message Personalization in Sport Live Broadcasts

This study aims to increase the effective use of in-stadium sponsor message placement by analyzing the influence of various run-of-play characteristics on television viewers’ visual attention allocation. Sports broadcasts constitute one potential platform for sponsors to place personalized messages. However, literature still questions the effectiveness of in-stadium sponsor messages, and the influence of game-related factors on viewers’ visual attention has received little consideration in this context. In addition, researchers call for more reliable and realistic measures concerning the effective evaluation of sponsorship-linked marketing. Therefore, this study uses real-time adaptions (eye-tracking, in-play betting odds, etc.) utilizing live soccer broadcasts as one of the first. Data were analyzed second by second (n = 100,298) using generalized linear mixed models. Results indicate significant associations of several run-of-play characteristics with viewers’ visual attention to sponsor messages depending on the characteristic, the games’ degree of suspense, and playing time. Findings provide hands-on advice for practitioners to enhance sponsor message placement during live broadcasts.

CLERA: A Unified Model for Joint Cognitive Load and Eye Region Analysis in the Wild

Non-intrusive, real-time analysis of the dynamics of the eye region allows us to monitor humans’ visual attention allocation and estimate their mental state during the performance of real-world tasks, which can potentially benefit a wide range of human-computer interaction (HCI) applications. While commercial eye-tracking devices have been frequently employed, the difficulty of customizing these devices places unnecessary constraints on the exploration of more efficient, end-to-end models of eye dynamics. In this work, we propose CLERA, a unified model for Cognitive Load and Eye Region Analysis, which achieves precise keypoint detection and spatiotemporal tracking in a joint-learning framework. Our method demonstrates significant efficiency and outperforms prior work on tasks including cognitive load estimation, eye landmark detection, and blink estimation. We also introduce a large-scale dataset of 30k human faces with joint pupil, eye-openness, and landmark annotation, which aims to support future HCI research on human factors and eye-related analysis.

Valence moderates the effect of stimulus-hand proximity on conflict processing and gaze-cueing.

An effective interaction with the environment requires adaptation of one's own behaviour to environmental demands. We do so by using cues from our environment and relating these cues to our body to predict the outcomes of events. The recent literature on embodied cognition suggests that task-relevant stimuli, presented near the hands, receive more attentional capacity and are processed differently than stimuli, presented spatially more distant to our body. It has also been proposed that near-hand processing is beneficial to conflict resolution. In the current study, we tested the assumption of an attentional bias towards the near hand space in the context of our previous work by combining a cueing paradigm (allocation of visual attention) with a conflict processing paradigm (Simon task) in the near vs far hand space. In addition, the relevance of processing was manipulated by using affective (angry vs neutral smileys) gaze cues (i.e., varying the valence of the cues). Our results indicate that (a) the interaction of valence × cue congruency × hand proximity was significant, indicating that the cueing effect was larger for negative valence in the proximal condition. (b) The interaction of valence × Simon compatibility × stimulus-hand proximity interaction was significant, indicating that for negative valence processing, the Simon effect was smaller in the proximal than in the distal stimulus-hand condition. This effect was at least numerically but not significantly reversed in the neutral valence condition. (c) Overall, cue congruency, indicating the correct vs incorrect attention allocation to the target stimulus onset, did not reveal any effect on Simon compatibility × stimulus-hand proximity. Our results suggest that valence, the allocation of attention, and conflict, seem to be decisive factors determining the direction and strength of hand proximity effects.

Visual search and the inhibitions of return

In the early 1980's independent research streams were launched by two of the 20th century's leading attention researchers. Anne Treisman's research program is best-known for distinguishing empirically between serial and pop-out search and for proposing feature integration theory and the idea of an attentional operator, that sequentially inspects items or groups of items when search is difficult. Among his many contributions to psychological science, Michael Posner is well-known for pioneering simple model tasks that made the allocation of visual attention in space amenable to scientific inquiry. When one version of the Posner cuing paradigm was used to explore visuospatial orienting it was serendipitously discovered that an “inhibitory” effect occurred in the aftermath of events that captured visuospatial attention involuntarily. This “inhibitory” phenomenon became known as Inhibition of Return (IOR), and, as implied by its name, the underlying mechanisms were thought to bias attention away from previously explored places. These two research programs were linked in 1988 when Raymond Klein exploited the distinction between pop-out and serial search to test and verify Posner's proposal that this inhibition might be a novelty seeking mechanism that could improve search efficiency. Subsequent research has identified at least two different inhibitory mechanisms that bias attention toward novelty. We present evidence using several diagnostics (central vs. peripheral targets, joint consideration of speed and accuracy, and the locus of slack logic embedded in the psychological refractory period effect) to illustrate the dual natures of IOR. The input form operates on a salience map that influences what will capture our attention, while the output form operates on a priority map that influences what behaviors (including orienting) are likely to be executed. The input form is generated when the reflexive oculomotor system is suppressed while the output form is generated when this system is not suppressed. We believe that both forms of IOR can serve the novelty seeking (and search facilitating) function proposed by Posner and others. Yet, many questions remain unanswered. Some of the many remaining puzzles will be described and we hope that our review will stimulate research to solve them.

Novice and Expert Observer Accuracy of the Threshold Wheelchair Skill: A Pilot Eye-Tracking Study

Background: Moving a wheelchair over a low threshold is an entry-level mobility skill. Observation is critical to the assessment and training of this skill. The primary objective of this exploratory pilot study was to determine if a difference between novice and expert visual attention allocation pattern was linked to the accuracy of rating skill performance and decision confidence.

On the forces of driver distraction: Explainable predictions for the visual demand of in-vehicle touchscreen interactions

With modern infotainment systems, drivers are increasingly tempted to engage in secondary tasks while driving. Since distracted driving is already one of the main causes of fatal accidents, in-vehicle touchscreens must be as little distracting as possible. To ensure that these systems are safe to use, they undergo elaborate and expensive empirical testing, requiring fully functional prototypes. Thus, early-stage methods informing designers about the implication their design may have on driver distraction are of great value. This paper presents a machine learning method that, based on anticipated usage scenarios, predicts the visual demand of in-vehicle touchscreen interactions and provides local and global explanations of the factors influencing drivers’ visual attention allocation. The approach is based on large-scale natural driving data continuously collected from production line vehicles and employs the SHapley Additive exPlanation (SHAP) method to provide explanations leveraging informed design decisions. Our approach is more accurate than related work and identifies interactions during which long glances occur with 68% accuracy and predicts the total glance duration with a mean error of 2.4s. Our explanations replicate the results of various recent studies and provide fast and easily accessible insights into the effect of UI elements, driving automation, and vehicle speed on driver distraction. The system can not only help designers to evaluate current designs but also help them to better anticipate and understand the implications their design decisions might have on future designs.

Advertising disclosure in sponsored vlogs: an eye-tracking study on the effects of disclosure timing and the moderating role of disclosure type

Regulations prescribe that sponsored influencer vlogs on social media should clearly disclose the embedded advertising to naive viewers. This study conducts an eye-tracking experiment involving 200 Chinese college students (18–30 years of age) to investigate how disclosure timing and disclosure type affect advertising effectiveness, including brand trust, brand attitude, purchase intention, and electronic word-of-mouth (eWOM) intent. The results showed that, compared to post-disclosure, prior disclosure attracted more viewers’ visual attention, leading to higher persuasion knowledge activation, and then negatively affected para-social interaction (PSI), resulting in decreased advertising effectiveness. Meanwhile, the higher visual attention allocation to the prior disclosure also activated higher sponsorship transparency perception, which then increased the PSI and, subsequently, led to more positive advertising effectiveness. However, these relationships were only significant for implicit (vs. explicit) disclosure. This study provides theoretical and practical implications for disclosure usage in Chinese influencer marketing.

Mining attention distribution paradigm: Discover gaze patterns and their association rules behind the visual image

Background and ObjectiveAttention allocation reflects the way of humans filtering and organizing the information. On one hand, different task scenarios seriously affect human's rule of attention distribution, on the other hand, visual attention reflecting the cognitive and psychological process. Most of the previous studies on visual attention allocation are based on cognitive models, predicted models, or statistical analysis of eye movement data or visual images, however, these methods are inadequate to provide an inside view of gaze behavior to reveal the attention distribution pattern within scenario context. Moreover, they seldom study the association rules of these patterns. Therefore, we adopted the big data mining approach to discover the paradigm of visual attention distribution. MethodsWe applied the data mining method to extract the gaze patterns to discover the regularities of attention distribution behavior within the scenario context. The proposed method consists of three components, tasks scenario segmented and clustered, gaze pattern mining, and association rule of frequent pattern mining. ResultsThe proposed approach is tested on the operation platform. The complex operation task is simultaneously segmented and clustered with the TICC-based method and evaluated by the BCI index. The operator's eye movement frequent patterns and their association rule are discovered. The results demonstrate that our method can associate the eye-tracking data with the task-oriented scene data. DiscussionThe proposed method provides the benefits of being able to explicitly express and quantitatively analyze people's visual attention patterns. The proposed method can not only be applied in the field of aerospace medicine and aviation psychology, but also can likely be applied to computer-aided diagnosis and follow-up tool for neurological disease and cognitive impairment related disease, such as ADHD (Attention Deficit Hyperactivity Disorder), neglect syndrome, social attention differences in ASD (Autism spectrum disorder).

The dynamic allocation of visual attention in space through statistical learning

The dynamic allocation of visual attention in space through statistical learning

Head-Up Display Graphic Warning to Support Collision Avoidance: Effect of Graphic Animation and Border on Driving Behavior and Eye Movement Pattern

Several studies have determined the positive effect of head-up display (HUD) graphic warning to support collision avoidance. However, the visual complexity of the graphic warning may overburden the driver. Design principles and empirical evidence suggest that animation and border designs may increase visual saliency and reduce the visual complexity of graphic warnings. This study aimed to examine the effects of animation (within-subject factors: animated/non-animated) and border (within-subject factors: with/without border) on drivers’ attention and driving performance. Thirty drivers (19 females) experienced five warning conditions (the no-warning condition was the baseline condition) in simulated driving conditions. The results showed the positive effects of animation and border designs on the visual detection time (animation: 25.64% reduction; border: 6.73% reduction) and attention allocation strategies (animation: 9.53% less dwell time on the warning; border: 6.62% less dwell time on the warning) when compared with the original HUD warning. We only observed a positive effect of borders on brake reaction time (17.14% reduction). However, the positive effect of the border design on brake reaction time was only observed in the non-animated warning condition, and not in the animated warning condition. A limitation of the study is that the results were generated from young and inexperienced drivers, and they should be generalized to other driver groups only with caution. Overall, we recommend using non-animated warnings with borders in conditions that require fast reactions, and animated warnings with borders in conditions that require a superior attention strategy.

Areas of Interest (AOI) on marketing mix elements of green and non-green products in customer decision making

Technological advancements in eye-tracking have enabled the development of interactive experimental setups for studying consumer behaviour. A common method for examining gaze data is Area of Interest (AOI). Therefore, this study fully utilised eye-tracking tools to measure participants' allocation of visual attention to the marketing mix elements in green and non-green products. This is because the product, price, place, and promotion are still the most crucial factors that customers consider when purchasing. The primary objective of this study is to discover and understand the primary marketing function that directly influences customer decision-making from a neuromarketing perspective. Their eye movements were simultaneously registered using SMI Eye Tracking Glasses 2 Wireless, and the gaze locations of participants were measured from AOI. The findings of this study have a significant impact on the importance of eye movements in decision-making, particularly when choosing important marketing elements before purchasing green and non-green products.