Analyzing the relationship between distracted driving and eye movement using multimodal data collected during car driving

Human error is the leading cause of traffic accidents and originates from the distraction caused by various factors, such as the driver's physical condition and mental state. One of the significant factors causing driver distraction is the presence of stress. In a previous study, multiple stressors were used to examine distraction while driving. Multiple stressors were given to the driver and the corresponding driver biometric data were obtained, and a multimodal dataset was published thereafter. In this study, we reiterate the results of existing studies and investigated the relationship between gaze variability while driving and stressor intervention, which has not yet been examined. We also examined whether biometric and vehicle information can estimate the presence or absence of secondary tasks during driving.

Dividing Attention and Metacognition

Students increasingly control their learning as university instructors shift away from lecture formats, courses are offered online, and the internet offers near infinite resources for student-controlled informal learning. Students typically make effective choices about learning, including what to learn, when to learn, and how to learn, but sometimes make less-than-optimal study choices, including trying to study while multi-tasking. Dividing attention among various tasks impairs both learning and learners' control over their learning because secondary tasks divert cognitive resources away from learning and metacognition. This chapter reviews recent studies explaining how dividing attention affects students' metacognition, including their assessments of their own learning and the study choices that they make. This chapter reviews the fundamentals of metacognition, describes the impact of dividing attention on the effectiveness of learners' metacognition, and provides suggestions about how to enhance the efficacy of metacognition when students' attentional resources are limited.

Mixed-initiative Variable Autonomy for Remotely Operated Mobile Robots

This article presents an Expert-guided Mixed-initiative Control Switcher (EMICS) for remotely operated mobile robots. The EMICS enables switching between different levels of autonomy during task execution initiated by either the human operator and/or the EMICS. The EMICS is evaluated in two disaster-response-inspired experiments, one with a simulated robot and test arena, and one with a real robot in a realistic environment. Analyses from the two experiments provide evidence that: (a) Human-Initiative (HI) systems outperform systems with single modes of operation, such as pure teleoperation, in navigation tasks; (b) in the context of the simulated robot experiment, Mixed-initiative (MI) systems provide improved performance in navigation tasks, improved operator performance in cognitive demanding secondary tasks, and improved operator workload compared to HI. Last, our experiment on a physical robot provides empirical evidence that identify two major challenges for MI control: (a) the design of context-aware MI control systems; and (b) the conflict for control between the robot’s MI control system and the operator. Insights regarding these challenges are discussed and ways to tackle them are proposed.

A simulation of older adults’ associative memory deficit using structural process interference in young adults

Associative memory deficit underlies a part of older adults’ deficient episodic memory due to the reduced ability to bind units of information. In this article we further assess the mechanism underlying this deficit, by assessing the degree to which we can model it in young adults under conditions of divided attention. We shall describe two experiments in this paper; these experiments investigate item and associative recognition in young adults under full- or divided-attention conditions. The secondary tasks employed were N-back like (NBL), which serves as a working memory updating task, and parity judgement and visuospatial (VS) tasks, which serve as non-working memory tasks. The results of both experiments show that only the NBL specifically affected associative recognition, while the other tasks affected item and associative memory to the same degree, indicating a general resource competition. These results presented a convergence of evidence for the associative deficit in older adults by modelling it in young adults.

Pathfinder

Designers of instructional software use gamification to help motivate and engage learners. Typically focusing on gamifying a single task, designers aim to provide a straightforward path through learning. In contrast, video games frequently provide optional secondary tasks using collectibles. Collectibles-like coins-are secondary, non-essential goals that encourage players to selectively take on additional challenges and engage more with a game. While research supports the idea that by increasing engagement learning can be improved, exactly how collectibles-an extremely common element in games-might be employed in gamified learning and how it might affect the play experience is underexplored. We present the results of a study comparing a gamified photo-editing training game that uses collectibles to one without collectibles. Our results show that learners choose to engage more when collectibles are present, and that this has a positive effect on software skills applied to a representative out-of-game challenge. Our findings provide a nuanced view of the tradeoffs in motivation and experience when collectibles are used.

Dual Tasking during Trip Recovery and Obstacle Clearance among Young, Healthy Adults in Human Factors Research

Trip-induced falls are extremely common in ergonomic settings. Such situations can lead to fatal or non-fatal injuries, affecting the workers’ quality of life and earning capacity. Dual tasking (DT) is a leading cause of trips and ineffective obstacle clearance among workers. DT increases their attentional demand, challenging both postural control and concurrent secondary tasks. As the human brain has limited attentional processing capacity, even young, healthy adults need to prioritize duties during DT. This article aimed to analyze these secondary task types and their applications in recent trip-related studies conducted on young, healthy adults. An extensive review of the recent trip-related literature was performed to provide a condensed summary of the dual tasks used. In previous trip-related literature, distinct types of secondary tasks were used. The choice of the concurrent task must be made vigilantly depending on the occupation, environmental context, available resources, and feasibility. DT can be used as a tool to train workers on selective attention, which is a lifesaving skill in ergonomic settings, especially in the occupations of roofers, construction workers, or truck drivers. Such training can result in successful obstacle clearance and trip recovery skills, which eventually minimizes the number of falls at the workplace.

Task matters: an investigation on the effect of different secondary tasks on dual-task gait in older adults

Background Dual-task gait performance declines as humans age, leading to increased fall risk among older adults. It is unclear whether different secondary cognitive tasks mediate age-related decline in dual-task gait. This study aimed to examine how type and difficulty level of the secondary cognitive tasks differentially affect dual-task gait in older adults. Methods Twenty young and twenty older adults participated in this single-session study. We employed four different types of secondary tasks and each consisted of two difficulty levels, yielding eight different dual-task conditions. The dual-task conditions included walking and 1) counting backward by 3 s or by 7 s; 2) remembering a 5-item or 7-item lists; 3) responding to a simple or choice reaction time tasks; 4) generating words from single or alternated categories. Gait speed and cognitive task performance under single- and dual-task conditions were used to compute dual-task cost (DTC, %) with a greater DTC indicating a worse performance. Results A significant three-way interaction was found for the gait speed DTC (p = .04). Increased difficulty in the reaction time task significantly increased gait speed DTC for older adults (p = .01) but not for young adults (p = .90). In contrast, increased difficulty level in the counting backward task significantly increased gait speed DTC for young adults (p = .03) but not for older adults (p = .85). Both groups responded similarly to the increased task difficulty in the other two tasks. Conclusions Older adults demonstrated a different response to dual-task challenges than young adults. Aging might have different impacts on various cognitive domains and result in distinctive dual-task gait interference patterns.

Learning from demonstration using products of experts: Applications to manipulation and task prioritization

Probability distributions are key components of many learning from demonstration (LfD) approaches, with the spaces chosen to represent tasks playing a central role. Although the robot configuration is defined by its joint angles, end-effector poses are often best explained within several task spaces. In many approaches, distributions within relevant task spaces are learned independently and only combined at the control level. This simplification implies several problems that are addressed in this work. We show that the fusion of models in different task spaces can be expressed as products of experts (PoE), where the probabilities of the models are multiplied and renormalized so that it becomes a proper distribution of joint angles. Multiple experiments are presented to show that learning the different models jointly in the PoE framework significantly improves the quality of the final model. The proposed approach particularly stands out when the robot has to learn hierarchical objectives that arise when a task requires the prioritization of several sub-tasks (e.g. in a humanoid robot, keeping balance has a higher priority than reaching for an object). Since training the model jointly usually relies on contrastive divergence, which requires costly approximations that can affect performance, we propose an alternative strategy using variational inference and mixture model approximations. In particular, we show that the proposed approach can be extended to PoE with a nullspace structure (PoENS), where the model is able to recover secondary tasks that are masked by the resolution of tasks of higher-importance.

Differences in Near-Crash Risk by Types of Distraction: A Comparison of Trends between Freeways and Two-Lane Highways using Naturalistic Driving Data

Distracted driving is among the leading causes of motor vehicle crashes in the United States, though the magnitude of this problem is difficult to quantify given limitations of police-reported crash data. This study leveraged data from the second Strategic Highway Research Program Naturalistic Driving Study to gain important insights into the risks posed by driver distraction on both freeways and two-lane highways. More than 50 types of secondary tasks were aggregated into ten distraction type categories and mixed-effects logistic regression models were estimated to discern how the risks of near-crash events varied by distraction type while controlling for the effects of driver, roadway, and traffic characteristics. In general, the types of distractions that created the most pronounced risks were those that introduced a combination of cognitive, visual, and manual distractions. For example, drivers who used cell phones were subject to higher risks and these risks tended to be most pronounced when both visual and manual distractions were involved. Likewise, risks tended to be highest when drivers reached for other objects inside the vehicle, engaged in personal hygiene-related activities, or focused on activities occurring outside of the driving environment. Although the same factors tended to increase near-crash risk on both types of facilities, the impacts of several factors tended to be more pronounced on two-lane highways where interaction with other vehicles occurred more frequently. From a policy standpoint, the results of this study provide further motivation for more aggressive legislation and enforcement of distracted driving.