Objective This study explores whether consumer education (CE) with and without driver responsibility content improves drivers’ understanding and use of Advanced Driver-Assistance Systems (ADAS). Background Research suggests that CE with responsibility content may improve driver understanding; however, how other forms of learning interact, such as experiential learning, remains unclear. Method Eighty drivers (21–76 years) participated in a CE study using a Traffic Jam Assist ADAS. The study was conducted as a 2 (CE content: CE with responsibility content and CE without responsibility content) x 2 (driver engagement instructions: hands-free and hands-on) x 3 (timing of mental model assessment (MMA): pre-CE, post-CE, and post-drive). Results CE with responsibility content did not significantly enhance MMA understanding or confidence beyond CE without responsibility content in this study ( p > .05). Regardless of responsibility content, drivers who received hands-on instructions consistently scored higher compared to hands-free instructions ( p < .01). MMA confidence rating decreased at post-CE for all conditions but only returned to baseline levels at post-drive ( p < .05) for drivers who received hands-free instructions. Conclusion Drivers who received hands-on instructions scored higher than hands-free at all; however, other forms of learning (e.g., experiential learning) may be necessary when the system substantially changes the driver’s role. Application CE may be enough to improve drivers’ understanding if an update does not alter how a driver interacts with the system, or how it aligns with their initial mental model.
Objective This study aimed to isolate the source of memorability benefits in recognition-based graphical authentication. The effects of retrieval (recall vs. recognition) and font representation (plain vs. distinctive) on usability measures were examined.Background Strong system-assigned passwords are difficult to remember. This motivates next-generation authentication to develop schemes that are memorable. Some researchers claim that their memorability improvement is due to dual-coding in long-term memory, while others highlight recognition retrieval. A distinctiveness theory was employed to transform text into images. This study examined whether text distinctiveness and recognition could yield memorability improvements while still using strong system-assigned alphanumeric passwords.Method Participants learned strong alphanumeric passwords using both traditional (recall) and Use Your Illusion (UYI) (recognition) authentication schemes. The UYI scheme presented the passwords in either plain or distinctive fonts. After practice with each authentication type, participants completed a test login after 5 minutes and 3 weeks later. Measurements included initial practice errors, login errors, login time, and ease ratings.Results Manipulating font distinctiveness did not significantly improve memorability. Recognition-based UYI interactions significantly outperformed recall-based password entry on all metrics after a 3-week delay. Text-based recognition achieved high memorability, comparable to performance reported for a graphical authentication benchmark.Conclusion Authentication memorability was primarily driven by the retrieval type. Transforming passwords into images did not enhance memorability, possibly due to a ceiling effect. However, employing a recognition-based scheme dramatically increased text password memorability.Application Findings suggest designers can significantly enhance authentication memorability by leveraging recognition memory, even with standard text.
ObjectiveEvaluate the sensitivity of eye-tracking metrics to variations in mental workload in rail traffic control, and how they may be modulated by operator experience.BackgroundRail traffic control is a safety-critical domain characterized by high cognitive demands. While subjective workload measures are widely used, objective, real-time indicators, particularly eye-tracking metrics, remain underexplored.MethodEighteen professional rail traffic controllers (9 novice, 9 experienced) performed a simulation-based experiment involving four different scenarios with varying traffic density and task complexity, while wearing a head-mounted eye tracker. Measures of gaze behavior and visual strategies (fixations in different areas of interest (AOI)) were computed, and compared to subjective workload measures, to assess the effects of task difficulty and experience.ResultsSome measures like pupil dilation showed substantial changes (by ∼100%), while others like stationary gaze entropy showed more modest changes (∼40%), with increased task demand. Measures such as fixation rate revealed significant interaction effects with experience, indicating distinct visual strategies of novices vs. experts. Compared to experts, novices relied more on global overview displays under higher workload, and reduced planning-related information use, as revealed by AOI analysis. Subjective workload measures corroborated with increases in perceived demand.ConclusionThis study advances mental workload assessment in rail traffic control by demonstrating that eye-tracking metrics are sensitive to workload variations, and also reveal underlying cognitive strategies unique to expertise.ApplicationThese findings support the development of real-time workload monitoring systems and inform training interventions focused on information integration and cognitive strategies in rail traffic control operations.
Objective This study examined whether multidimensional recurrence quantification analysis (MdRQA) metrics derived from team heart rate data predict team performance and team workload under varied team and task demands. Background Assessing team performance in operational environments is challenging. Traditional approaches require task interruptions or ground-truth data that are often unavailable. Physiological sensing offers a less-intrusive approach, yet most applications aggregate individual-level data or rely on simple pairwise metrics that do not represent how coupling is organized across the whole team. MdRQA instead embeds multivariate time series into a shared phase space, but evidence linking MdRQA metrics to team outcomes remains limited and mixed. Method Twenty triads completed a cooperative virtual reality choice reaction time task across four conditions manipulating interdependence and time pressure. MdRQA metrics (percent determinism, average diagonal line length, diagonal line entropy) were extracted from heart rate interbeat interval data. Linear mixed-effects models tested whether these metrics predicted self-reported team workload and team performance outcomes including sensitivity and response bias from signal detection theory. Results Longer sustained coupling predicted increased sensitivity and fewer errors, while greater entropy predicted poorer performance. Higher determinism and lower entropy predicted a more liberal response bias. MdRQA metrics did not predict perceived workload beyond condition effects. Conclusion MdRQA metrics reflect joint physiological dynamics and predict performance outcomes in triads beyond the effects of task demands alone. Application Continuously computed physiological coupling metrics may serve as less-intrusive, real-time proxies for team performance in operational environments where traditional assessment is infeasible.
