How do humans learn about the reliability of automation?

In a range of settings, human operators make decisions with the assistance of automation, the reliability of which can vary depending upon context. Currently, the processes by which humans track the level of reliability of automation are unclear. In the current study, we test cognitive models of learning that could potentially explain how humans track automation reliability. We fitted several alternative cognitive models to a series of participants' judgements of automation reliability observed in a maritime classification task in which participants were provided with automated advice. We examined three experiments including eight between-subjects conditions and 240 participants in total. Our results favoured a two-kernel delta-rule model of learning, which specifies that humans learn by prediction error, and respond according to a learning rate that is sensitive to environmental volatility. However, we found substantial heterogeneity in learning processes across participants. These outcomes speak to the learning processes underlying how humans estimate automation reliability and thus have implications for practice.

Inhibitory Cognitive Control Allows Automated Advice to Improve Accuracy While Minimizing Misuse.

Humans increasingly use automated decision aids. However, environmental uncertainty means that automated advice can be incorrect, creating the potential for humans to act on incorrect advice or to disregard correct advice. We present a quantitative model of the cognitive process by which humans use automation when deciding whether aircraft would violate requirements for minimum separation. The model closely fitted the performance of 24 participants, who each made 2,400 conflict-detection decisions (conflict vs. nonconflict), either manually (with no assistance) or with the assistance of 90% reliable automation. When the decision aid was correct, conflict-detection accuracy improved, but when the decision aid was incorrect, accuracy and response time were impaired. The model indicated that participants integrated advice into their decision process by inhibiting evidence accumulation toward the task response that was incongruent with that advice, thereby ensuring that decisions could not be made solely on automated advice without first sampling information from the task environment.

Understanding fatigue in a naval submarine: Applying biomathematical models and workload measurement in an intensive longitudinal design.

Fatigue is a critically important aspect of crew endurance in submarine operations, with continuously high fatigue being associated with increased risk of human error and long-term negative health ramifications. Submarines pose several unique challenges to fatigue mitigation, including requirements for continuous manning for long durations, a lack of access to critical environmental zeitgebers (stimuli pertinent to circadian physiology; e.g., natural sunlight), and work, rest and sleep occurring within an encapsulated environment. In this paper, we examine the factors that underlie fatigue in such a context with the aim of evaluating the predictive utility of a biomathematical model (BMM) of fatigue. Three experience sampling studies were conducted with submarine crews using a participant-led measurement protocol that included assessments of subjective sleepiness, workload (NASA-Task Load Index [TLX] and a bespoke underload-overload scale), and sleep. As expected, results indicated that predicting KSS with a BMM approach outperformed more conventional linear modelling approaches (e.g., time-of-day, sleep duration, time awake). Both the homeostatic and circadian components of the BMM were significantly associated with KSS and used as controls in the workload models. We found increased NASA-TLX workload was significantly associated with increased average KSS ratings at the between-person level. However, counter to expectations, the two workload measures were not found to have significant linear or quadratic relationship with fatigue at the within-person level. An important outcome of the research is that applied fatigue researchers should be extremely cautious applying conventional linear predictors when predicting fatigue. Practical implications for the submarine and related extreme work context are discussed. Important avenues for continued research are outlined, including directly estimating BMM parameters.

Prospective Memory Performance in Simulated Air Traffic Control : Robust to Interruptions but Impaired by Retention Interval.

OBJECTIVE: To examine the effects of interruptions and retention interval on prospective memory for deferred tasks in simulated air traffic control. BACKGROUND: In many safety-critical environments, operators need to remember to perform a deferred task, which requires prospective memory. Laboratory experiments suggest that extended prospective memory retention intervals, and interruptions in those retention intervals, could impair prospective memory performance. METHOD: Participants managed a simulated air traffic control sector. Participants were sometimes instructed to perform a deferred handoff task, requiring them to deviate from a routine procedure. We manipulated whether an interruption occurred during the prospective memory retention interval or not, the length of the retention interval (37-117 s), and the temporal proximity of the interruption to deferred task encoding and execution. We also measured performance on ongoing tasks. RESULTS: Increasing retention intervals (37-117 s) decreased the probability of remembering to perform the deferred task. Costs to ongoing conflict detection accuracy and routine handoff speed were observed when a prospective memory intention had to be maintained. Interruptions did not affect individuals' speed or accuracy on the deferred task. CONCLUSION: Longer retention intervals increase risk of prospective memory error and of ongoing task performance being impaired by cognitive load; however, prospective memory can be robust to effects of interruptions when the task environment provides cuing and offloading. APPLICATION: To support operators in performing complex and dynamic tasks, prospective memory demands should be reduced, and the retention interval of deferred tasks should be kept as short as possible.

The long road home from distraction: Investigating the time-course of distraction recovery in driving.

Driver distraction is a leading cause of accidents. While there has been significant research examining driver performance during a distraction, there has been less focus on how much time is required to recover performance following a distraction. To address this issue, participants in the current study completed a simulated 40-min drive while being presented with distractions. Distractions were followed by a visual Detection Response Task (DRT) to assess participants' resource availability and potential capacity to respond to hazards, as well as continuous measures of driving performance including their ability to maintain a consistent speed and lane position. We examined recovery for a 40 s period following three types of distraction: cognitive only, cognitive + visual, and cognitive + visual + manual. Since safe driving requires cognitive, visual, and manual resources, we expected recovery to take longer when the distraction involved more of these resources. Consistent with this, each additional level of distraction further slowed DRT response times and increased speed variability during 0-10 s post-distraction. However, DRT accuracy was equally impaired for all conditions during 0-20 s post-distraction, while lane position maintenance from 0 to 10 s post-distraction was only impaired when the distraction included a manual component. In addition, while participants in all three conditions exhibited some degree of post-distraction impairment, only those in the cognitive + visual + manual condition reduced their speed during the time when distracted, suggesting drivers show limited awareness of the potential persistent consequences of distraction.