Decoding Task-Based fMRI Data with Graph Neural Networks, Considering Individual Differences.

Task fMRI provides an opportunity to analyze the working mechanisms of the human brain during specific experimental paradigms. Deep learning models have increasingly been applied for decoding and encoding purposes study to representations in task fMRI data. More recently, graph neural networks, or neural networks models designed to leverage the properties of graph representations, have recently shown promise in task fMRI decoding studies.

Toward a Theory of Visual Information Acquisition in Driving.

Objective: The aim of this study is to describe information acquisition theory, explaining how drivers acquire and represent the information they need.

Background: While questions of what drivers are aware of underlie many questions in driver behavior, existing theories do not directly address how drivers in particular and observers in general acquire visual information. Understanding the mechanisms of information acquisition is necessary to build predictive models of drivers' representation of the world and can be applied beyond driving to a wide variety of visual tasks.

Glanceable, legible typography over complex backgrounds.

Modern digital interfaces display typeface in ways new to the 500 year old art of typography, driving a shift in reading from primarily long-form to increasingly short-form. In safety-critical settings, such at-a-glance reading competes with the need to understand the environment. To keep both type and the environment legible, a variety of 'middle layer' approaches are employed.

The Effects of Virtual Reality, Augmented Reality, and Mixed Reality as Training Enhancement Methods: A Meta-Analysis.

Objective: The objective of this meta-analysis is to explore the presently available, empirical findings on transfer of training from virtual (VR), augmented (AR), and mixed reality (MR) and determine whether such extended reality (XR)-based training is as effective as traditional training methods.

Background: MR, VR, and AR have already been used as training tools in a variety of domains. However, the question of whether or not these manipulations are effective for training has not been quantitatively and conclusively answered.

The great typography bake-off: comparing legibility at-a-glance.

Typography plays an increasingly important role in today's dynamic digital interfaces. Graphic designers and interface engineers have more typographic options than ever before. Sorting through this maze of design choices can be a daunting task.

Detection of brake lights while distracted: Separating peripheral vision from cognitive load.

Drivers rarely focus exclusively on driving, even with the best of intentions. They are distracted by passengers, navigation systems, smartphones, and driver assistance systems. Driving itself requires performing simultaneous tasks, including lane keeping, looking for signs, and avoiding pedestrians.

Microsaccades Distinguish Looking From Seeing.

Understanding our visual world requires both looking and seeing. Dissociation of these processes can result in the phenomenon of inattentional blindness or 'looking without seeing'. Concomitant errors in applied settings can be serious, and even deadly.

Angry Drivers Take Risky Decisions: Evidence from Neurophysiological Assessment.

The present study investigated the risk-taking behaviors of angry drivers, which were coincidentally measured via behavioral and electroencephalographic (EEG) recordings. We manipulated a driving scenario that concerned a Go/No-Go decision at an intersection when the controlling traffic light was in its yellow phase. This protocol was based upon the underlying format of the Iowa gambling task.

Hacking the Human: The Prevalence Paradox in Cybersecurity.

Objective: This work assesses the efficacy of the "prevalence effect" as a form of cyberattack in human-automation teaming, using an email task.

Background: Under the prevalence effect, rare signals are more difficult to detect, even when taking into account their proportionally low occurrence. This decline represents diminished human capability to both detect and respond.

Hysteresis in Mental Workload and Task Performance: The Influence of Demand Transitions and Task Prioritization.

Objective: We examine how transitions in task demand are manifested in mental workload and performance in a dual-task setting.

Background: Hysteresis has been defined as the ongoing influence of demand levels prior to a demand transition. Authors of previous studies predominantly examined hysteretic effects in terms of performance.

Detection of error-related negativity in complex visual stimuli: a new neuroergonomic arrow in the practitioner's quiver.

Brain processes responsible for the error-related negativity (ERN) evoked response potential (ERP) have historically been studied in highly controlled laboratory experiments through presentation of simple visual stimuli. The present work describes the first time the ERN has been evoked and successfully detected in visual search of complex stimuli. A letter flanker task and a motorcycle conspicuity task were presented to participants during electroencephalographic (EEG) recording.

Judging Thieves of Attention: Commentary on "Assessing Cognitive Distraction in the Automobile," by Strayer, Turrill, Cooper, Coleman, Medeiros-Ward, and Biondi (2015).

The laudable effort by Strayer and his colleagues to derive a systematic method to assess forms of cognitive distraction in the automobile is beset by the problem of nonstationary in driver response capacity. At the level of the overall goal of driving, this problem conflates actual on-road behavior; characterized by underspecified task satisficing, with our own understandable, scientifically inspired aspiration for measuring deterministic performance optimization. Measures of response conceived under this latter imperative are, at best, only shadowy reflections of the actual phenomenological experience involved in real-world vehicle control.

Google glass: a driver distraction cause or cure?

Objective: We assess the driving distraction potential of texting with Google Glass (Glass), a mobile wearable platform capable of receiving and sending short-message-service and other messaging formats.

Background: A known roadway danger, texting while driving has been targeted by legislation and widely banned. Supporters of Glass claim the head-mounted wearable computer is designed to deliver information without concurrent distraction.