Objective: We controlled participants' glance behavior while using head-down displays (HDDs) and head-up displays (HUDs) to isolate driving behavioral changes due to use of different display types across different driving environments.
Background: Recently, HUD technology has been incorporated into vehicles, allowing drivers to, in theory, gather display information without moving their eyes away from the road. Previous studies comparing the impact of HUDs with traditional displays on human performance show differences in both drivers' visual attention and driving performance. Yet no studies have isolated glance from driving behaviors, which limits our ability to understand the cause of these differences and resulting impact on display design.
Method: We developed a novel method to control visual attention in a driving simulator. Twenty experienced drivers sustained visual attention to in-vehicle HDDs and HUDs while driving in both a simple straight and empty roadway environment and a more realistic driving environment that included traffic and turns.
Results: In the realistic environment, but not the simpler environment, we found evidence of differing driving behaviors between display conditions, even though participants' glance behavior was similar.
Conclusion: Thus, the assumption that visual attention can be evaluated in the same way for different types of vehicle displays may be inaccurate. Differences between driving environments bring the validity of testing HUDs using simplistic driving environments into question.
Application: As we move toward the integration of HUD user interfaces into vehicles, it is important that we develop new, sensitive assessment methods to ensure HUD interfaces are indeed safe for driving.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1177/00187208211031416 | DOI Listing |
J Chem Inf Model
January 2025
Institute of Chemistry, Technische Universität Berlin, Straße des 17. Juni 135, Berlin 10623, Germany.
Machine learning (ML) is a powerful tool for the automated data analysis of molecular dynamics (MD) simulations. Recent studies showed that ML models can be used to identify protein-ligand unbinding pathways and understand the underlying mechanism. To expedite the examination of MD simulations, we constructed PathInHydro, a set of supervised ML models capable of automatically assigning unbinding pathways for the dissociation of gas molecules from [NiFe] hydrogenases, using the unbinding trajectories of CO and H from [NiFe] hydrogenase as a training set.
View Article and Find Full Text PDFAust N Z J Psychiatry
January 2025
Centre for Mental Health, Swinburne University of Technology, Melbourne, VIC, Australia.
Objective: Neurocognitive underpinnings are implicated in the aetiology and maintenance of body dysmorphic disorder (BDD); however, inconsistent findings across a range of neurocognitive domains suggest that a comprehensive synthesis of the literature using a hierarchical framework of neurocognition is needed.
Methods: A final search across OVID Medline, PsycNET, Scopus and Web of Science databases was conducted on 20 June 2024 to identify research that examined performance on behavioural tasks of objective neurocognition in BDD. Risk of bias was assessed using the Newcastle-Ottawa Scale.
Retinopathy of prematurity (ROP) and diabetic retinopathy (DR) are ocular disorders in which a loss of retinal vasculature leads to ischemia followed by a compensatory neovascularization response. In mice, this is modeled using oxygen-induced retinopathy (OIR), whereby neonatal animals are transiently housed under hyperoxic conditions that result in central retina vessel regression and subsequent neovascularization. Using endothelial cell (EC)-specific gene deletion, we found that loss of two ETS-family transcription factors, ERG and FLI1, led to regression of OIR-induced neovascular vessels but failed to improve visual function, suggesting that relevant retinal damage occurs prior to and independently of neovascularization.
View Article and Find Full Text PDFUnlabelled: Evaluating tissue microstructure and membrane integrity in the living human brain through diffusion-water exchange imaging is challenging due to requirements for a high signal-to-noise ratio and short diffusion times dictated by relatively fast exchange processes. The goal of this work was to demonstrate the feasibility of imaging of tissue micro-geometries and water exchange within the brain gray matter using the state-of-the-art Connectome 2.0 scanner equipped with an ultra-high-performance gradient system (maximum gradient strength=500 mT/m, maximum slew rate=600 T/m/s).
View Article and Find Full Text PDFClin EEG Neurosci
January 2025
Advanced Brain Monitoring, Carlsbad, CA, USA.
Resting-state EEG measures have shown potential in distinguishing individuals with PTSD from healthy controls. ERP components such as N2, P3, and late positive potential have been consistently linked to cognitive abnormalities in PTSD, especially in tasks involving emotional or trauma-related stimuli. However, meta-analyses have reported inconsistent findings.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!