Lower prospective memory is associated with higher neurocognitive dispersion in two samples of people with HIV: A conceptual replication study.

OBJECTIVES: People living with HIV (PLWH) often experience deficits in the strategic/executive aspects of prospective memory (PM) that can interfere with instrumental activities of daily living. This study used a conceptual replication design to determine whether cognitive intraindividual variability, as measured by dispersion (IIV-dispersion), contributes to PM performance and symptoms among PLWH. METHODS: Study 1 included 367 PLWH who completed a comprehensive clinical neuropsychological test battery, the Memory for Intentions Test (MIsT), and the Prospective and Retrospective Memory Questionnaire (PRMQ). Study 2 included 79 older PLWH who completed the Cogstate cognitive battery, the Cambridge Prospective Memory Test (CAMPROMPT), an experimental measure of time-based PM, and the PRMQ. In both studies, a mean-adjusted coefficient of variation was derived to measure IIV-dispersion using normative T-scores from the cognitive battery. RESULTS: Higher IIV-dispersion was significantly associated with lower time-based PM performance at small-to-medium effect sizes in both studies (mean r s  = -0.30). The relationship between IIV-dispersion and event-based PM performance was comparably small in magnitude in both studies (r s  = -0.19, -0.20), but it was only statistically significant in Study 1. IIV-dispersion showed very small, nonsignificant relationships with self-reported PM symptoms in both samples (r s < 0.10). CONCLUSIONS: Extending prior work in healthy adults, these findings suggest that variability in performance across a cognitive battery contributes to laboratory-based PM accuracy, but not perceived PM symptoms, among PLWH. Future studies might examine whether daily fluctuations in cognition or other aspects of IIV (e.g., inconsistency) play a role in PM failures in everyday life.

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.

The Perception of Automation Reliability and Acceptance of Automated Advice.

OBJECTIVE: Examine (1) the extent to which humans can accurately estimate automation reliability and calibrate to changes in reliability, and how this is impacted by the recent accuracy of automation; and (2) factors that impact the acceptance of automated advice, including true automation reliability, reliability perception, and the difference between an operator's perception of automation reliability and perception of their own reliability. BACKGROUND: Existing evidence suggests humans can adapt to changes in automation reliability but generally underestimate reliability. Cognitive science indicates that humans heavily weight evidence from more recent experiences. METHOD: Participants monitored the behavior of maritime vessels (contacts) in order to classify them, and then received advice from automation regarding classification. Participants were assigned to either an initially high (90%) or low (60%) automation reliability condition. After some time, reliability switched to 75% in both conditions. RESULTS: Participants initially underestimated automation reliability. After the change in true reliability, estimates in both conditions moved towards the common true reliability, but did not reach it. There were recency effects, with lower future reliability estimates immediately following incorrect automation advice. With lower initial reliability, automation acceptance rates tracked true reliability more closely than perceived reliability. A positive difference between participant assessments of the reliability of automation and their own reliability predicted greater automation acceptance. CONCLUSION: Humans underestimate the reliability of automation, and we have demonstrated several critical factors that impact the perception of automation reliability and automation use. APPLICATION: The findings have potential implications for training and adaptive human-automation teaming.

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.

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.

Meaningful Communication but not Superficial Anthropomorphism Facilitates Human-Automation Trust Calibration: The Human-Automation Trust Expectation Model (HATEM).

OBJECTIVE: The objective was to demonstrate anthropomorphism needs to communicate contextually useful information to increase user confidence and accurately calibrate human trust in automation. BACKGROUND: Anthropomorphism is believed to improve human-automation trust but supporting evidence remains equivocal. We test the Human-Automation Trust Expectation Model (HATEM) that predicts improvements to trust calibration and confidence in accepted advice arising from anthropomorphism will be weak unless it aids naturalistic communication of contextually useful information to facilitate prediction of automation failures. METHOD: Ninety-eight undergraduates used a submarine periscope simulator to classify ships, aided by the Ship Automated Modelling (SAM) system that was 50% reliable. A between-subjects 2 × 3 design compared SAM appearance (anthropomorphic avatar vs. camera eye) and voice inflection (monotone vs. meaningless vs. meaningful), with the meaningful inflections communicating contextually useful information about automated advice regarding certainty and uncertainty. RESULTS: Avatar SAM appearance was rated as more anthropomorphic than camera eye, and meaningless and meaningful inflections were both rated more anthropomorphic than monotone. However, for subjective trust, trust calibration, and confidence in accepting SAM advice, there was no evidence of anthropomorphic appearance having any impact, while there was decisive evidence that meaningful inflections yielded better outcomes on these trust measures than monotone and meaningless inflections. CONCLUSION: Anthropomorphism had negligible impact on human-automation trust unless its execution enhanced communication of relevant information that allowed participants to better calibrate expectations of automation performance. APPLICATION: Designers using anthropomorphism to calibrate trust need to consider what contextually useful information will be communicated via anthropomorphic features.

The Impact of Transparency and Decision Risk on Human-Automation Teaming Outcomes.

OBJECTIVE: Examine the effects of decision risk and automation transparency on the accuracy and timeliness of operator decisions, automation verification rates, and subjective workload. BACKGROUND: Decision aids typically benefit performance, but can provide incorrect advice due to contextual factors, creating the potential for automation disuse or misuse. Decision aids can reduce an operator's manual problem evaluation, and it can also be strategic for operators to minimize verifying automated advice in order to manage workload. METHOD: Participants assigned the optimal unmanned vehicle to complete missions. A decision aid provided advice but was not always reliable. Two levels of decision aid transparency were manipulated between participants. The risk associated with each decision was manipulated using a financial incentive scheme. Participants could use a calculator to verify automated advice; however, this resulted in a financial penalty. RESULTS: For high- compared with low-risk decisions, participants were more likely to reject incorrect automated advice and were more likely to verify automation and reported higher workload. Increased transparency did not lead to more accurate decisions and did not impact workload but decreased automation verification and eliminated the increased decision time associated with high decision risk. CONCLUSION: Increased automation transparency was beneficial in that it decreased automation verification and decreased decision time. The increased workload and automation verification for high-risk missions is not necessarily problematic given the improved automation correct rejection rate. APPLICATION: The findings have potential application to the design of interfaces to improve human-automation teaming, and for anticipating the impact of decision risk on operator behavior.

The Impact of Information Integration in a Simulation of Future Submarine Command and Control.

OBJECTIVE: Examine the extent to which increasing information integration across displays in a simulated submarine command and control room can reduce operator workload, improve operator situation awareness, and improve team performance. BACKGROUND: In control rooms, the volume and number of sources of information are increasing, with the potential to overwhelm operator cognitive capacity. It is proposed that by distributing information to maximize relevance to each operator role (increasing information integration), it is possible to not only reduce operator workload but also improve situation awareness and team performance. METHOD: Sixteen teams of six novice participants were trained to work together to combine data from multiple sensor displays to build a tactical picture of surrounding contacts at sea. The extent that data from one display were available to operators at other displays was manipulated (information integration) between teams. Team performance was assessed as the accuracy of the generated tactical picture. RESULTS: Teams built a more accurate tactical picture, and individual team members had better situation awareness and lower workload, when provided with high compared with low information integration. CONCLUSION: A human-centered design approach to integrating information in command and control settings can result in lower workload, and enhanced situation awareness and team performance. APPLICATION: The design of modern command and control rooms, in which operators must fuse increasing volumes of complex data from displays, may benefit from higher information integration based on a human-centered design philosophy, and a fundamental understanding of the cognitive work that is carried out by operators.

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.

Exploring the Peak-End Effects in Air Traffic Controllers' Mental Workload Ratings.

OBJECTIVE: This study examined whether professional air traffic controllers (ATCos) were subject to peak-end effects in reporting their mental workload after performing an air traffic control task, and in predicting their mental workload in future scenarios. BACKGROUND: In affective experience studies, people's evaluation of a period of experience is strongly influenced by the most intense (peak) point and the endpoint. However, whether the effects exist in mental workload evaluations made by professional operators is still not known. METHOD: In Study 1, 20 ATCos performed air traffic control scenarios on high-fidelity radar simulators and reported their mental workload. We used a 2 (high peak, low peak) × 2 (high end, low end) within-subject design. In Study 2, another group of 43 ATCos completed a survey asking them to predict their mental workload given the same air traffic control scenarios. RESULTS: In Study 1, ATCos reported higher mental workload after completing the high-peak and the high-end scenarios. In contrast, in Study 2, ATCos predicted the peak workload effect but not the end workload effect when asked to predict their experience in dealing with the same scenarios. CONCLUSION: Peak and end effects exist in subjective mental workload evaluation, but experts only had meta-cognitive awareness of the peak effect, and not the end effect. APPLICATION: Researchers and practitioners that use subjective workload estimates for work design decisions need to be aware of the potential impact of peak and end task demand effects on subjective mental workload ratings provided by expert operators.

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.

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.

Improving Prospective Memory Performance in Community-dwelling Older Adults: Goal Management Training and Implementation Intentions.

Aim: The present study tested a compensatory executive intervention for prospective memory (goal management training) for the first time in older adults. Prospective memory (the ability to remember and execute a task in the future) declines with age, with significant implications for older adults' activities of daily living and quality of life. Prospective memory interventions have focused primarily on the retrospective component of prospective memory (e.g., implementation intentions). However, executive dysfunction is also implicated in age-related prospective memory decline.Methods: Community-dwelling older adults were randomly allocated to receive goal management training, implementation intentions or no intervention. Prospective memory was assessed before and after the intervention with a well-validated laboratory-based prospective memory measure. Results: Contrary to predictions, neither goal management training nor implementation intentions were successful at improving prospective memory in healthy older adults. Participants who received goal management training were more likely to have difficulty comprehending the intervention. Post-hoc analyses suggested implementation intentions improved prospective memory specifically for participants with poorer baseline prospective memory. Conclusions: These results represent important cautionary findings about the possible limitations of goal management training to improve prospective memory in older adults. Future research should also consider the role of baseline prospective memory ability in affecting response to compensatory intervention.

The Difficulty to Break a Relational Complexity Network Can Predict Air Traffic Controllers' Mental Workload and Performance in Conflict Resolution.

OBJECTIVE: To test the network disentangling model for explaining air traffic controllers' (ATCos) conflict resolution performance. The network rigidity index (NRI), and the steps to break the relational complexity network following a central-available-node-first rule, was hypothesized to explain the overall task demand, whereas marginal-effort-decrease rule was expected to explain the actual operational outcome. BACKGROUND: Understanding the conflict resolution process of ATCos is important for aviation safety and efficiency. However, linear models are insufficient. We proposed a new model that ATCos behavior can be largely considered as a process to break the relational complexity network, in which nodes represent the aircraft while links represent the cognitive complexity to understand the aircraft dyad relationship. METHOD: Twenty-one professional ATCos completed 27 conflict resolution scenarios that varied in the NRI and other control variables. Multilevel regression analyses were performed to understand the influence of the NRI on the number of interventions, mental workload, and unresolved rate. A cross-validation was performed to evaluate the predictive power of the model. RESULTS: NRI influenced ATCos intervention number in a curvilinear manner, which further leads to ATCo's mental workload. The deviance between the number of interventions and the NRI was strongly linked with unresolved rate. Cross-validation suggests that the models predictions are robust. CONCLUSION: The network disentangling model provides a useful theory-driven way to explain controllers' conflict resolution workload and other important performance outcomes such as intervention probability. APPLICATION: The proposed model can potentially be used for workload management, sector design, and intelligent decision support tool development.

Explaining short-term memory phenomena with an integrated episodic/semantic framework of long-term memory.

Current thinking about human memory is dominated by distinctions between episodic and semantic memory and between short-term memory (STM) and long-term memory (LTM). However, many memory phenomena seem to cut across these distinctions. This article attempts to set the groundwork for the issues that need to be resolved in generating an integrated model of long-term memory that incorporates semantic, episodic, and short-term memory. We contrast Nairne's (2002, Annual Review of Psychology) consensus account of short-term memory with a relatively generic theory of an integrated episodic-semantic memory. The later consists primarily of a list of principles which we and others argue are necessary to include in any theory of long-term memory. We then add some more specific assumptions to outline a modern theory of forgetting. We then turn to the issue of much of the phenomena thought to necessitate a dedicated short-term memory can be explained by an integrated theory of episodic and semantic memory. Our conclusion is that an integrated theory of long-term memory must be augmented to explain a small number of outstanding memory phenomena. Finally, we ask whether the augmentation needs to involve a dedicated mnemonic system, or sensory or language-based systems, which also have mnemonic capabilities.

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 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.

Using Situation Awareness and Workload to Predict Performance in Submarine Track Management: A Multilevel Approach.

OBJECTIVE: Examine the extent to which subjective workload and situation awareness (SA) can predict variance in performance at the between- and within-person levels of analysis in a simulated submarine track management task. BACKGROUND: SA and workload are crucial constructs in human factors that are conceptualized as states that change within individuals over time. Thus, a change in an individual's subjective workload or SA over the course of performing a task should be predictive of their subsequent performance (within-person effects). However, there is little empirical evidence for this. METHOD: Participants monitored displays to track the behaviors of contacts in relationship to their own ship (Ownship) and landmarks. The Situational Awareness Global Assessment Technique measured SA, and the Air Traffic Workload Input Technique measured subjective workload. RESULTS: When a participant's subjective workload rating increased, their subsequent performance decreased, but there was no evidence for within-person effects of SA on performance. We replicated prior between-person level effects of SA; participants with higher SA performed better than those with lower SA. CONCLUSION: Change in an individual's subjective workload rating (but not SA) was predictive of their subsequent performance. Because an increase in SA should increase the extent to which operators hold the knowledge required to perform subsequent tasks, further research is required to examine SA effects on performance at the within-person level. APPLICATION: Adapting automation is more likely to produce optimal outcomes if based on measurement of operator states that predict future task performance, such as workload.