Operator selection for human-automation teaming: The role of manual task skill in predicting automation failure intervention.

Humans working in modern work systems are increasingly required to supervise task automation. We examined whether manual aircraft conflict detection skill predicted participants' ability to respond to conflict detection automation failures in simulated air traffic control. In a conflict discrimination task (to assess manual skill), participants determined whether pairs of aircraft were in conflict or not by judging their relative-arrival time at common intersection points. Then in a simulated air traffic control task, participants supervised automation which either partially or fully detected and resolved conflicts on their behalf. Automation supervision required participants to detect when automation may have failed and effectively intervene. When automation failed, participants who had better manual conflict detection skill were faster and more accurate to intervene. However, a substantial proportion of variance in failure intervention was not explained by manual conflict detection skill, potentially reflecting that future research should consider other cognitive skills underlying automation supervision.

Prospective memory decision control: A computational model of context effects on prospective memory.

Prospective memory (PM) tasks require remembering to perform a deferred action and can be associated with predictable contexts. We present a theory and computational model, prospective memory decision control (PMDC), of the cognitive processes by which context supports PM. Under control conditions, participants completed lexical decisions. Under PM conditions, participants had the additional PM task of responding to letter strings containing certain syllables. Stimuli were presented in one of two colors, with color potentially changing after each set of four trials. A pretrial colored fixation was presented before each set. Under control and PM standard conditions, fixation color was meaningless. Under PM context conditions, fixation color indicated whether a PM target could occur within the next set. We replicated prior findings of higher PM accuracy for context compared to standard conditions, and the expected variation in PM costs (slowed lexical decisions) as a function of context relevance. PMDC, which formalizes PM as a process of evidence accumulation among ongoing and PM task responses, accounted for the impact of context on PM costs and accuracy via proactive and reactive cognitive control. Increased ongoing task thresholds and decreased PM thresholds in relevant contexts indicated proactive control. With context provision, PM accumulation rates on PM trials increased, as did inhibition of accumulation to competing responses, indicating reactive control. Although an observed capacity-sharing effect explained some portion of PM costs, we found no evidence that participants redirected more capacity from the ongoing to the PM task when contextually cued to relevant contexts. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Memory benefits when actively, rather than passively, viewing images.

Serial visual presentations of images exist both in the laboratory and increasingly on virtual platforms such as social media feeds. However, the way we interact with information differs between these. In many laboratory experiments participants view stimuli passively, whereas on social media people tend to interact with information actively. This difference could influence the way information is remembered, which carries practical and theoretical implications. In the current study, 821 participants viewed streams containing seven landscape images that were presented at either a self-paced (active) or an automatic (passive) rate. Critically, the presentation speed in each automatic trial was matched to the speed of a self-paced trial for each participant. Both memory accuracy and memory confidence were greater on self-paced compared to automatic trials. These results indicate that active, self-paced progression through images increases the likelihood of them being remembered, relative to when participants have no control over presentation speed and duration.

Listening to Misinformation while Driving: Cognitive Load and the Effectiveness of (Repeated) Corrections.

Corrected misinformation can continue to influence inferential reasoning. It has been suggested that such continued influence is partially driven by misinformation familiarity, and that corrections should therefore avoid repeating misinformation to avoid inadvertent strengthening of misconceptions. However, evidence for such familiarity-backfire effects is scarce. We tested whether familiarity backfire may occur if corrections are processed under cognitive load. Although misinformation repetition may boost familiarity, load may impede integration of the correction, reducing its effectiveness and therefore allowing a backfire effect to emerge. Participants listened to corrections that repeated misinformation while in a driving simulator. Misinformation familiarity was manipulated through the number of corrections. Load was manipulated through a math task administered selectively during correction encoding. Multiple corrections were more effective than a single correction; cognitive load reduced correction effectiveness, with a single correction entirely ineffective under load. This provides further evidence against familiarity-backfire effects and has implications for real-world debunking.

Transparent Automated Advice to Mitigate the Impact of Variation in Automation Reliability.

OBJECTIVE: To examine the extent to which increased automation transparency can mitigate the potential negative effects of low and high automation reliability on disuse and misuse of automated advice, and perceived trust in automation. BACKGROUND: Automated decision aids that vary in the reliability of their advice are increasingly used in workplaces. Low-reliability automation can increase disuse of automated advice, while high-reliability automation can increase misuse. These effects could be reduced if the rationale underlying automated advice is made more transparent. METHODS: Participants selected the optimal UV to complete missions. The Recommender (automated decision aid) assisted participants by providing advice; however, it was not always reliable. Participants determined whether the Recommender provided accurate information and whether to accept or reject advice. The level of automation transparency (medium, high) and reliability (low: 65%, high: 90%) were manipulated between-subjects. RESULTS: With high- compared to low-reliability automation, participants made more accurate (correctly accepted advice and identified whether information was accurate/inaccurate) and faster decisions, and reported increased trust in automation. Increased transparency led to more accurate and faster decisions, lower subjective workload, and higher usability ratings. It also eliminated the increased automation disuse associated with low-reliability automation. However, transparency did not mitigate the misuse associated with high-reliability automation. CONCLUSION: Transparency protected against low-reliability automation disuse, but not against the increased misuse potentially associated with the reduced monitoring and verification of high-reliability automation. APPLICATION: These outcomes can inform the design of transparent automation to improve human-automation teaming under conditions of varied automation reliability.

Reducing the Costs of Automation Failure by Providing Voluntary Automation Checking Tools.

OBJECTIVE: We investigated the extent to which a voluntary-use range and bearing line (RBL) tool improves return-to-manual performance when supervising high-degree conflict detection automation in simulated air traffic control. BACKGROUND: High-degree automation typically benefits routine performance and reduces workload, but can degrade return-to-manual performance if automation fails. We reasoned that providing a voluntary checking tool (RBL) would support automation failure detection, but also that automation induced complacency could extend to nonoptimal use of such tools. METHOD: Participants were assigned to one of three conditions, where conflict detection was either performed: manually, with RBLs available to use (Manual + RBL), automatically with RBLs (Auto + RBL), or automatically without RBLs (Auto). Voluntary-use RBLs allowed participants to reliably check aircraft conflict status. Automation failed once. RESULTS: RBLs improved automation failure detection - with participants intervening faster and making fewer false alarms when provided RBLs compared to not (Auto + RBL vs Auto). However, a cost of high-degree automation remained, with participants slower to intervene to the automation failure than to an identical manual conflict event (Auto + RBL vs Manual + RBL). There was no difference in RBL engagement time between Auto + RBL and Manual + RBL conditions, suggesting participants noticed the conflict event at the same time. CONCLUSIONS: The cost of automation may have arisen from participants' reconciling which information to trust: the automation (which indicated no conflict and had been perfectly reliable prior to failing) or the RBL (which indicated a conflict). APPLICATIONS: Providing a mechanism for checking the validity of high-degree automation may facilitate human supervision of automation.

Do concurrent task demands impact the benefit of automation transparency?

Automated decision aids typically improve decision-making, but incorrect advice risks automation misuse or disuse. We examined the novel question of whether increased automation transparency improves the accuracy of automation use under conditions with/without concurrent (non-automated assisted) task demands. Participants completed an uninhabited vehicle (UV) management task whereby they assigned the best UV to complete missions. Automation advised the best UV but was not always correct. Concurrent non-automated task demands decreased the accuracy of automation use, and increased decision time and perceived workload. With no concurrent task demands, increased transparency which provided more information on how the automation made decisions, improved the accuracy of automation use. With concurrent task demands, increased transparency led to higher trust ratings, faster decisions, and a bias towards agreeing with automation. These outcomes indicate increased reliance on highly transparent automation under conditions with concurrent task demands and have potential implications for human-automation teaming design.

Automated decision aids: When are they advisors and when do they take control of human decision making?

We applied a computational model to examine the extent to which participants used an automated decision aid as an advisor, as compared to a more autonomous trigger of responding, at varying levels of decision aid reliability. In an air traffic control conflict detection task, we found higher accuracy when the decision aid was correct, and more errors when the decision aid was incorrect, as compared to a manual condition (no decision aid). Responses that were correct despite incorrect automated advice were slower than matched manual responses. Decision aids set at lower reliability (75%) had smaller effects on choices and response times, and were subjectively trusted less, than decision aids set at higher reliability (95%). We fitted an evidence accumulation model to choices and response times to measure how information processing was affected by decision aid inputs. Participants primarily treated low-reliability decision aids as an advisor rather than directly accumulating evidence based on its advice. Participants directly accumulated evidence based upon the advice of high-reliability decision aids, consistent with granting decision aids more autonomous influence over decisions. Individual differences in the level of direct accumulation correlated with subjective trust, suggesting a cognitive mechanism by which trust impacts human decisions. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Detecting a Single Automation Failure: The Impact of Expected (But Not Experienced) Automation Reliability.

OBJECTIVE: Examine the impact of expected automation reliability on trust, workload, task disengagement, nonautomated task performance, and the detection of a single automation failure in simulated air traffic control. BACKGROUND: Prior research has focused on the impact of experienced automation reliability. However, many operational settings feature automation that is reliable to the extent that operators will seldom experience automation failures. Despite this, operators must remain aware of when automation is at greater risk of failing. METHOD: Participants performed the task with or without conflict detection/resolution automation. Automation failed to detect/resolve one conflict (i.e., an automation miss). Expected reliability was manipulated via instructions such that the expected level of reliability was (a) constant or variable, and (b) the single automation failure occurred when expected reliability was high or low. RESULTS: Trust in automation increased with time on task prior to the automation failure. Trust was higher when expecting high relative to low reliability. Automation failure detection was improved when the failure occurred under low compared with high expected reliability. Subjective workload decreased with automation, but there was no improvement to nonautomated task performance. Automation increased perceived task disengagement. CONCLUSIONS: Both automation reliability expectations and task experience played a role in determining trust. Automation failure detection was improved when the failure occurred at a time it was expected to be more likely. Participants did not effectively allocate any spared capacity to nonautomated tasks. APPLICATIONS: The outcomes are applicable because operators in field settings likely form contextual expectations regarding the reliability of automation.

Return-to-Manual Performance can be Predicted Before Automation Fails.

OBJECTIVE: This study aimed to examine operator state variables (workload, fatigue, and trust in automation) that may predict return-to-manual (RTM) performance when automation fails in simulated air traffic control. BACKGROUND: Prior research has largely focused on triggering adaptive automation based on reactive indicators of performance degradation or operator strain. A more direct and effective approach may be to proactively engage/disengage automation based on predicted operator RTM performance (conflict detection accuracy and response time), which requires analyses of within-person effects. METHOD: Participants accepted and handed-off aircraft from their sector and were assisted by imperfect conflict detection/resolution automation. To avoid aircraft conflicts, participants were required to intervene when automation failed to detect a conflict. Participants periodically rated their workload, fatigue and trust in automation. RESULTS: For participants with the same or higher average trust than the sample average, an increase in their trust (relative to their own average) slowed their subsequent RTM response time. For participants with lower average fatigue than the sample average, an increase in their fatigue (relative to own average) improved their subsequent RTM response time. There was no effect of workload on RTM performance. CONCLUSIONS: RTM performance degraded as trust in automation increased relative to participants' own average, but only for individuals with average or high levels of trust. APPLICATIONS: Study outcomes indicate a potential for future adaptive automation systems to detect vulnerable operator states in order to predict subsequent RTM performance decrements.

Should We Just Let the Machines Do It? The Benefit and Cost of Action Recommendation and Action Implementation Automation.

OBJECTIVE: To examine the effects of action recommendation and action implementation automation on performance, workload, situation awareness (SA), detection of automation failure, and return-to-manual performance in a submarine track management task. BACKGROUND: Theory and meta-analytic evidence suggest that with increasing degrees of automation (DOA), operator performance improves and workload decreases, but SA and return-to-manual performance declines. METHOD: Participants monitored the location and heading of contacts in order to classify them, mark their closest point of approach (CPA), and dive when necessary. Participants were assigned either no automation, action recommendation automation, or action implementation automation. An automation failure occurred late in the task, whereby the automation provided incorrect classification advice or implemented incorrect classification actions. RESULTS: Compared to no automation, action recommendation automation benefited automated task performance and lowered workload, but cost nonautomated task performance. Action implementation automation resulted in perfect automated task performance (by default) and lowered workload, with no costs to nonautomated task performance, SA, or return-to-manual performance compared to no automation. However, participants provided action implementation automation were less likely to detect the automation failure compared to those provided action recommendations, and made less accurate classifications immediately after the automation failure, compared to those provided no automation. CONCLUSION: Action implementation automation produced the anticipated benefits but also caused poorer automation failure detection. APPLICATION: While action implementation automation may be effective for some task contexts, system designers should be aware that operators may be less likely to detect automation failures and that performance may suffer until such failures are detected.

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.

"I don't think anybody really knows": Constructing reflexive ignorance in climate change adaptation.

Responding to the existential threat of climate change is often seen as requiring greater reflexivity. Imbued with notions of resilience and reflection, reflexivity is assumed to contribute to pro-environmental change. However, as the need to manage climate impacts becomes more immediate, political struggles over climate adaptation have become increasingly apparent. These impacts occur most often within local communities, in the context of competing economic interests and differing interpretations of climate science. Thus while it is increasingly difficult to deny climate change, conflicting priorities can lead to ignorance. In these circumstances, how communities build and share knowledge, and negotiate responses is central. Based on a study of a vulnerable region in Australia, we identify three processes through which the local community mobilized to disrupt local climate change adaptation. These included emphasizing uncertainty about the science of climate change, encouraging fear about property prices, and repositioning property owners as victims of climate adaptation policy. We argue that this response to climate adaptation constitutes the production of reflexive ignorance, which reinforces skepticism around scientific authority and defends particular economic interests.

Improving prospective memory with contextual cueing.

Prospective memory (PM) involves remembering to perform an intended action in the future. Researchers have demonstrated that, under certain conditions, contextual information about when PM performance opportunities are likely to occur can support PM performance while decreasing the cognitive demands of the PM task. The current study builds upon prior work to investigate whether warning participants that a PM-relevant context was approaching would improve the efficiency of PM control processes and benefit PM accuracy. Participants completed an ongoing lexical decision task with an embedded PM task of responding to a target syllable. For context conditions, targets only appeared on trials where letter strings were colored red (PM-relevant context), while PM-irrelevant trials were green. The warning in Experiment 1 was embedded in the ongoing task (trials preceding PM-relevant contexts were colored yellow). In Experiment 2 the warning was separate from the ongoing task (1-s pre-trial red fixation preceding PM-relevant contexts). Context improved PM control efficiency and PM accuracy in both experiments. Context always improved PM accuracy for targets in the second and third trial positions of PM-relevant contexts; however, only the Experiment 2 warning generated an accuracy benefit for targets in the first trial position. Experiment 3 replicated the findings of Experiments 1 and 2 and also confirmed that color change without associated context was not responsible for the current results.

The Benefits and Costs of Low and High Degree of Automation.

OBJECTIVE: The objective of this study is to examine the effects of low and high degree of automation (DOA) on performance, subjective workload, situation awareness (SA), and return-to-manual control in simulated submarine track management. BACKGROUND: Theory and meta-analytic evidence suggest that as DOA increases, operator performance improves and workload decreases, but SA and return-to-manual control declines. Research also suggests that operators have particular difficulty regaining manual control if automation provides incorrect advice. METHOD: Undergraduate student participants completed a submarine track management task that required them to track the position and behavior of contacts. Low DOA supported information acquisition and analysis, whereas high DOA recommended decisions. At a late stage in the task, automation was either unexpectedly removed or provided incorrect advice. RESULTS: Relative to no automation, low DOA moderately benefited performance but impaired SA and non-automated task performance. Relative to no automation and low DOA, high DOA benefited performance and lowered workload. High DOA did impair non-automated task performance compared with no automation, but this was equivalent to low DOA. Participants were able to return-to-manual control when they knew low or high DOA was disengaged, or when high DOA provided incorrect advice. CONCLUSION: High DOA improved performance and lowered workload, at no additional cost to SA or return-to-manual performance when compared with low DOA. APPLICATION: Designers should consider the likely level of uncertainty in the environment and the consequences of return-to-manual deficits before implementing low or high DOA.

Individual differences in higher-level cognitive abilities do not predict overconfidence in complex task performance.

Even when people perform tasks poorly, they often report unrealistically positive estimates of their own abilities in these situations. To better understand the origins of such overconfidence, we investigated whether it could be predicted by individual differences in working memory, attentional control, and self-reported trait impulsivity. Overconfidence was estimated by contrasting objective and subjective measures of situation awareness (the ability to perceive and understand task-relevant information in the environment), acquired during a challenging air traffic control simulation. We found no significant relationships between overconfidence and either working memory or attentional control. However, increased impulsivity significantly predicted greater overconfidence. In addition, overall levels of overconfidence were lower in our complex task than in previous studies that used less-complex lab-based tasks. Our results suggest that overconfidence may not be linked to high-level cognitive abilities, but that dynamic tasks with frequent opportunities for performance feedback may reduce misconceptions about personal performance.

Phase specific shape aftereffects explained by the tilt aftereffect.

Aftereffects of adaptation are frequently used to infer mechanisms of human visual perception. Commonly, the properties of stimuli are repelled from properties of the adaptor. For example, in the tilt aftereffect a line is repelled in orientation from a previously experienced line. Perceived orientation is predicted by the centroid of the responses of a population of mechanisms individually tuned to limited ranges of orientation but collectively sensitive to the whole possible range. Aftereffects are also predictable if the mechanisms are allowed to adapt. Adaptation across radial frequency patterns, patterns deformed from circular by a sinusoidal modulation of radius, causes repulsive aftereffects, sensitive to the relative amplitudes and orientations of the patterns. Here we show that these shape aftereffects can be accounted for by the application of local tilt aftereffects around the shape contour. We suggest that fields of tilt aftereffects might provide a general mechanism for exaggerating the perceptual difference between successively experienced stimuli, making them more discriminable. If the human visual system does indeed exploit this possibility, then the conclusions often made by studies assuming adaptation within mechanisms sensitive to the shape of stimuli will need to be reconsidered. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

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.

Smartphone technology: a reliable and valid measure of knee movement in knee replacement.

Knee range of motion (ROM) following a knee arthroplasty is an important clinical outcome that directly relates to the patient's physical function. Smartphone technology has led to the creation of applications that can measure ROM. The aim was to determine the concurrent reliability and validity of the photo-based application 'Dr Goniometer' (DrG) compared with a universal goniometer performed by a clinician. A smartphone camera was used to take photographs of the knee in full flexion and full extension, and the images were sent by participants to a study phone. Participants then rated the ease of participation. To assess validity, the patient's knee was measured by a clinician using a goniometer. To examine reliability, four clinicians assessed each image using DrG on four separate occasions spaced 1 week apart. A total of 60 images of knee ROM for 30 unicondylar or total knee arthroplasty were assessed. The goniometer and DrG showed strong correlations for flexion (r=0.94) and extension (r=0.90). DrG showed good intrarater reliability and excellent inter-rater reliability for flexion (intraclass correlation coefficient=0.990 and 0.990) and good reliability for extension (intraclass correlation coefficient=0.897 and 0.899). All participants found the process easy. DrG was proven to be a valid and reliable tool in measuring knee ROM following arthroplasty. Smartphone technology, in conjunction with patient-reported outcomes, offers an accurate and practical way to remotely monitor patients. Benefit may be found in differentiating those who need face-to-face clinical consult to those who do not.

Visual search reveals a critical component to shape.

Objects are often identified by the shape of their contours. In this study, visual search tasks were used to reveal a visual dimension critical to the analysis of the shape of a boundary-defined area. Points of maximum curvature on closed paths are important for shape coding and it was shown here that target patterns are readily identified among distractors if the angle subtended by adjacent curvature maxima at the target pattern's center differs from that created in the distractors. A search asymmetry, indicated by a difference in performance in the visual search task when the roles of target and distractor patterns are reversed, was found when the critical subtended angle was only present in one of the patterns. Performance for patterns with the same subtended angle but differing local orientation and curvature was poor, demonstrating insensitivity to differences in these local features of the patterns. These results imply that the discrimination of objects by the shape of their boundaries relies on the relative positions of their curvature maxima rather than the local properties of the boundary from which these positions are derived.