Dual-task performance consequences of imperfect alerting associated with a cockpit display of traffic information

Attention to Changes on a Head-Worn Display: Two Preclinical Studies with Healthcare Scenarios

OBJECTIVE

In two experiments, we examined how quickly different visual alerts on a head-worn display (HWD) would capture participants' attention to a matrix of patient vital sign values, while multitasking.

BACKGROUND

An HWD could help clinicians monitor multiple patients, regardless of where the clinician is located. We sought effective ways for HWDs to alert multitasking wearers to important events.

METHODS

In two preclinical experiments, university student participants performed a visuomotor tracking task while simultaneously monitoring simulated patient vital signs on an HWD to detect abnormal values. Methods to attract attention to abnormal values included highlighting abnormal vital signs and imposing a white flash over the entire display.

RESULTS

Experiment 1 found that participants detected abnormal values faster with high contrast than low contrast greyscale highlights, even while performing difficult tracking. In Experiment 2, a white flash of the entire screen quickly and reliably captured attention to vital signs, but less so on an HWD than on a conventional screen.

CONCLUSION

Visual alerts on HWDs can direct users' attention to patient transition events (PTEs) even under high visual-perceptual load, but not as quickly as visual alerts on fixed displays. Aspects of the results have since been tested in a healthcare context.

APPLICATION

Potential applications include informing the design of HWD interfaces for monitoring multiple processes and informing future research on capturing attention to HWDs.

The effects of distraction on younger drivers: A neurophysiological perspective

Distracted driving remains a significant cause of traffic accidents globally, including in Australia. However, many younger drivers still admit to using a phone while driving. A simulated driving study investigated the neurophysiological effects of visual, auditory, and higher-order cognitive (i.e., requiring the use of executive functions) distraction on young drivers. In total, 24 young adults aged 18-25 years completed four 8 min simulated driving sessions while concurrently engaging in various distractor tasks. Neurophysiological arousal was measured via EEG. Additionally, subjective workload and objective driving performance were assessed. Frontal beta and gamma power exhibited their highest levels during tasks involving higher-order cognitive and visual demands. The higher-order cognitive condition was rated as the most mentally demanding. In comparison, the visual condition had the most significant impact on both the standard deviation of speed and standard deviation of lateral positioning. This study has significant implications for all road users, particularly those aged 18-25 years, and it reinforces the importance of not using a phone while driving.

Simple auditory and visual interruptions of a continuous visual tracking task: modality effects and time course of interference

Research has produced conflicting evidence regarding whether performance of an on-going visual task is disrupted more by an interruption from a visual or an auditory alert. Tasks and alerts studied to date have been complex or idiosyncratic. This experiment examined how the modality of simple alerts-visual icons or auditory tones-affected performance of an on-going visual task. Participants (58 females and 4 males) tracked a visual target while performing a choice reaction time task in response to alerts. Visual alerts were more harmful to performance of the tracking task. Dual task workload was lowest with an auditory alert, provided there was not noise present. Interruptions affected tracking task performance for around 1500 ms. Results supported the predictions of Multiple Resources Theory and showed no evidence of auditory preemption. In practical applications for which an on-going visual task is interrupted, auditory alerts may be less disruptive and may reduce perceived workload. Practitioner Summary: Many practical scenarios involve on-going visual tasks that are interrupted by simple alerts requiring a simple response. Auditory alerts may be less disruptive than visual alerts and may reduce perceived workload. A conservative estimate is that the effects of even simple interruptions will last a minimum of 1500 ms. Abbreviations: ANOVA: analysis of variance; LSD: least significant difference; TLX: task load index.

The impact of predictability on dual-task performance and implications for resource-sharing accounts

The aim of this study was to examine the impact of predictability on dual-task performance by systematically manipulating predictability in either one of two tasks, as well as between tasks. According to capacity-sharing accounts of multitasking, assuming a general pool of resources two tasks can draw upon, predictability should reduce the need for resources and allow more resources to be used by the other task. However, it is currently not well understood what drives resource-allocation policy in dual tasks and which resource allocation policies participants pursue. We used a continuous tracking task together with an audiomotor task and manipulated advance visual information about the tracking path in the first experiment and a sound sequence in the second experiments (2a/b). Results show that performance predominantly improved in the predictable task but not in the unpredictable task, suggesting that participants did not invest more resources into the unpredictable task. One possible explanation was that the re-investment of resources into another task requires some relationship between the tasks. Therefore, in the third experiment, we covaried the two tasks by having sounds 250 ms before turning points in the tracking curve. This enabled participants to improve performance in both tasks, suggesting that resources were shared better between tasks.

Do We Really Need More Stages? Comparing the Effects of Likelihood Alarm Systems and Binary Alarm Systems

OBJECTIVE

This research investigates the potential behavioral and performance benefits of a four-stage likelihood alarm system (4-LAS) contrasting a 3-LAS, a binary alarm system with a liberal threshold (lib-BAS), and a BAS with a conservative threshold (con-BAS).

BACKGROUND

Prior research has shown performance benefits of 3-LASs over conventional lib-BASs due to more distinct response strategies and better discriminating true from false alerts. This effect might be further enhanced using 4-LASs. However, the increase in stages could cause users to reduce cognitive complexity by responding in the same way to the two lower and the two higher stages, thus treating the 4-LAS like a con-BAS.

METHOD

All systems were compared using a dual-task paradigm. Response strategies, number of joint human machine (JHM) false alarms (FAs), misses, and sensitivity were regarded.

RESULTS

Compared with the lib-BAS, JHM sensitivity only improved with the 4-LAS and the con-BAS. However, the number of JHM misses was lowest for the con-BAS compared with all other systems.

CONCLUSION

JHM sensitivity improvements can be achieved by using a 4-LAS, as well as a con-BAS. However, only the latter one may also reduce the number of JHM misses, which is remarkable considering that BASs with conservative thresholds a priori commit more inbuilt misses than other systems.

APPLICATION

Results suggest implementing conservative BASs in multi-task working environments to improve JHM sensitivity and reduce the number of JHM misses. When refraining from designing systems which are miss prone, 4-LASs represent a suitable compromise.

Alerts for community pharmacist-provided medication therapy management: recommendations from a heuristic evaluation

BACKGROUND

Medication therapy management (MTM) is a service, most commonly provided by pharmacists, intended to identify and resolve medication therapy problems (MTPs) to enhance patient care. MTM is typically documented by the community pharmacist in an MTM vendor's web-based platform. These platforms often include integrated alerts to assist the pharmacist with assessing MTPs. In order to maximize the usability and usefulness of alerts to the end users (e.g., community pharmacists), MTM alert design should follow principles from human factors science. Therefore, the objectives of this study were to 1) evaluate the extent to which alerts for community pharmacist-delivered MTM align with established human factors principles, and 2) identify areas of opportunity and recommendations to improve MTM alert design.

METHODS

Five categories of MTM alerts submitted by community pharmacists were evaluated: 1) indication, 2) effectiveness; 3) safety; 4) adherence; and 5) cost-containment. This heuristic evaluation was guided by the Instrument for Evaluating Human-Factors Principles in Medication-Related Decision Support Alerts (I-MeDeSA) which we adapted and contained 32 heuristics. For each MTM alert, four analysts' individual ratings were summed and a mean score on the modified I-MeDeSA computed. For each heuristic, we also computed the percent of analyst ratings indicating alignment with the heuristic. We did this for all alerts evaluated to produce an "overall" summary of analysts' ratings for a given heuristic, and we also computed this separately for each alert category. Our results focus on heuristics where ≤50% of analysts' ratings indicated the alerts aligned with the heuristic.

RESULTS

I-MeDeSA scores across the five alert categories were similar. Heuristics pertaining to visibility and color were generally met. Opportunities for improvement across all MTM alert categories pertained to the principles of alert prioritization; text-based information; alarm philosophy; and corrective actions.

CONCLUSIONS

MTM alerts have several opportunities for improvement related to human factors principles, resulting in MTM alert design recommendations. Enhancements to MTM alert design may increase the effectiveness of MTM delivery by community pharmacists and result in improved patient outcomes.

Effects of Trust, Self-Confidence, and Feedback on the Use of Decision Automation

Operators often fail to rely sufficiently on alarm systems. This results in a joint human-machine (JHM) sensitivity below the one of the alarm system. The 'confidence vs. trust hypothesis' assumes the use of the system depends on the weighting of both values. In case of higher confidence, the task is performed manually, if trust is higher, the user relies on the system. Thus, insufficient reliance may be due to operators' overconfidence in their own abilities and/or insufficient trust in the decision automation, but could be mitigated by providing feedback. That was investigated within a signal detection task, supported by a system with either higher sensitivity (HSS) or lower sensitivity (LSS) than the human, while being provided with feedback or not. We expected disuse of the LSS and insufficiently reliance on the HSS, in the condition without feedback. The feedback was expected to increase reliance on the HSS through an increase in trust and/or decreases in confidence, and thus, improve performance. Hypotheses were partly supported. Confidence in manual performance was similar to trust in the HSS even though humans' sensitivity was significantly lower than systems' sensitivity. While confidence had not effect on reliance or JHM sensitivity, trust was found to be positively related with both. We found disuse of the HSS, that could be improved through feedback, increasing also trust and JHM sensitivity. However, contrary to 'confidence vs. trust' expectations, participants were also found to make use of the LSS. This misuse could not be reduced by feedback. Results indicate the use of feedback being beneficial for the overall performance (with HSS only). Findings do not support the idea that misuse or disuse of the system may result from comparison of confidence and trust. We suppose it may rather be the product of users' wrong strategy of function allocation, based on the underlying idea of team work in combination with missing assignment of responsibility. We discuss this alternative explanation.

No Effect of Cue Format on Automation Dependence in an Aided Signal Detection Task

OBJECTIVE

To investigate whether manipulating the format of an automated decision aid's cues can improve participants' information integration strategies in a signal detection task.

BACKGROUND

Automation-aided decision making is often suboptimal, falling well short of statistically ideal levels. The choice of format in which the cues from the aid are displayed may help users to better understand and integrate the aid's judgments with their own.

METHOD

Participants performed a signal detection task that asked them to classify random dot images as either blue or orange dominant. They made their judgments either unaided or with assistance from a 93% reliable automated decision aid. The aid provided a binary judgment, along with an estimate of signal strength in the form of either a raw value, a likelihood ratio, or a confidence rating (Experiments 1 and 2) or a binary judgment along with either a verbal or verbal-visuospatial expression of confidence (Experiment 3). Aided sensitivity was benchmarked to the predictions of various statistical models of collaborative decision making.

RESULTS AND CONCLUSION

Aided performance was suboptimal, matching the predictions of some of the least efficient models. Most importantly, performance was similar across cue formats.

APPLICATION

Results indicate that changes to the format in which cues from a signal detection aid are rendered are unlikely to dramatically improve the efficiency of automation-aided decision making.

The Effect of Culture on Trust in Automation

Trust in automation has become a topic of intensive study since the late 1990s and is of increasing importance with the advent of intelligent interacting systems. While the earliest trust experiments involved human interventions to correct failures/errors in automated control systems, a majority of subsequent studies have investigated information acquisition and analysis decision aiding tasks such as target detection for which automation reliability is more easily manipulated. Despite the high level of international dependence on automation in industry, almost all current studies have employed Western samples primarily from the U.S. The present study addresses these gaps by running a large sample experiment in three (U.S., Taiwan, and Turkey) diverse cultures using a “trust sensitive task” consisting of both automated control and target detection subtasks. This article presents results for the target detection subtask for which reliability and task load were manipulated. The current experiments allow us to determine whether reported effects are universal or specific to Western culture, vary in baseline or magnitude, or differ across cultures. Results generally confirm consistent effects of manipulations across the three cultures as well as cultural differences in initial trust and variation in effects of manipulations consistent with 10 cultural hypotheses based on Hofstede's Cultural Dimensions and Leung and Cohen's theory of Cultural Syndromes. These results provide critical implications and insights for correct trust calibration and to enhance human trust in intelligent automation systems across cultures. Additionally, our results would be useful in designing intelligent systems for users of different cultures. Our article presents the following contributions: First, to the best of our knowledge, this is the first set of studies that deal with cultural factors across all the cultural syndromes identified in the literature by comparing trust in the Honor, Face, Dignity cultures. Second, this is the first set of studies that uses a validated cross-cultural trust measure for measuring trust in automation. Third, our experiments are the first to study the dynamics of trust across cultures.

Automation in airport security X-ray screening of cabin baggage: Examining benefits and possible implementations of automated explosives detection

Bomb attacks on civil aviation make detecting improvised explosive devices and explosive material in passenger baggage a major concern. In the last few years, explosive detection systems for cabin baggage screening (EDSCB) have become available. Although used by a number of airports, most countries have not yet implemented these systems on a wide scale. We investigated the benefits of EDSCB with two different levels of automation currently being discussed by regulators and airport operators: automation as a diagnostic aid with an on-screen alarm resolution by the airport security officer (screener) or EDSCB with an automated decision by the machine. The two experiments reported here tested and compared both scenarios and a condition without automation as baseline. Participants were screeners at two international airports who differed in both years of work experience and familiarity with automation aids. Results showed that experienced screeners were good at detecting improvised explosive devices even without EDSCB. EDSCB increased only their detection of bare explosives. In contrast, screeners with less experience (tenure < 1 year) benefitted substantially from EDSCB in detecting both improvised explosive devices and bare explosives. A comparison of all three conditions showed that automated decision provided better human-machine detection performance than on-screen alarm resolution and no automation. This came at the cost of slightly higher false alarm rates on the human-machine system level, which would still be acceptable from an operational point of view. Results indicate that a wide-scale implementation of EDSCB would increase the detection of explosives in passenger bags and automated decision instead of automation as diagnostic aid with on screen alarm resolution should be considered.

Benchmarking Aided Decision Making in a Signal Detection Task

OBJECTIVE

A series of experiments examined human operators' strategies for interacting with highly (93%) reliable automated decision aids in a binary signal detection task.

BACKGROUND

Operators often interact with automated decision aids in a suboptimal way, achieving performance levels lower than predicted by a statistically ideal model of information integration. To better understand operators' inefficient use of decision aids, we compared participants' automation-aided performance levels with the predictions of seven statistical models of collaborative decision making.

METHOD

Participants performed a binary signal detection task that asked them to classify random dot images as either blue or orange dominant. They made their judgments either unaided or with assistance from a 93% reliable automated decision aid that provided either graded (Experiments 1 and 3) or binary (Experiment 2) cues. We compared automation-aided performance with the predictions of seven statistical models of collaborative decision making, including a statistically optimal model and Robinson and Sorkin's contingent criterion model.

RESULTS AND CONCLUSION

Automation-aided sensitivity hewed closest to the predictions of the two least efficient collaborative models, well short of statistically ideal levels. Performance was similar whether the aid provided graded or binary judgments. Model comparisons identified potential strategies by which participants integrated their judgments with the aid's.

APPLICATION

Results lend insight into participants' automation-aided decision strategies and provide benchmarks for predicting automation-aided performance levels.

Benefits of Imperfect Conflict Resolution Advisory Aids for Future Air Traffic Control

OBJECTIVE

The aim of this study was to examine the human-automation interaction issues and the interacting factors in the context of conflict detection and resolution advisory (CRA) systems.

BACKGROUND

The issues of imperfect automation in air traffic control (ATC) have been well documented in previous studies, particularly in conflict-alerting systems. The extent to which the prior findings can be applied to an integrated conflict detection and resolution system in future ATC remains unknown.

METHOD

Twenty-four participants were evenly divided into two groups corresponding to a medium- and a high-traffic density condition, respectively. In each traffic density condition, participants were instructed to perform simulated ATC tasks under four automation conditions, including reliable, unreliable with short time allowance to secondary conflict (TAS), unreliable with long TAS, and manual conditions. Dependent variables accounted for conflict resolution performance, workload, situation awareness, and trust in and dependence on the CRA aid, respectively.

RESULTS

Imposing the CRA automation did increase performance and reduce workload as compared with manual performance. The CRA aid did not decrease situation awareness. The benefits of the CRA aid were manifest even when it was imperfectly reliable and were apparent across traffic loads. In the unreliable blocks, trust in the CRA aid was degraded but dependence was not influenced, yet the performance was not adversely affected.

CONCLUSION

The use of CRA aid would benefit ATC operations across traffic densities.

APPLICATION

CRA aid offers benefits across traffic densities, regardless of its imperfection, as long as its reliability level is set above the threshold of assistance, suggesting its application for future ATC.

Approaches towards training in human risk management of surgical technology

A safe application of modern surgical technology and computer-assisted surgery devices is based on an operation by adequately trained surgeons who are familiar with the benefits and limitations of the devices. We analyzed the in-depth interviews with seven Spanish and 10 German surgeons. Together with other studies, this analysis highlights the need for specific training in technological competence for surgeons. One way to train technological competence is to help surgeons understanding the basic principles of medical devices as well as explaining the basic concepts of risk analysis and risk management. Based on this premise, a stage model for risk assessment was developed and adapted for the training of surgeons. This was developed further into a train the trainer (TTT) concept, which was then evaluated for two example cases. During TTT-training, the trainers (expert surgeons) performed a risk analysis for several medical devices. Afterwards, the trainers organized a surgical workshop for surgical trainees (resident surgeons), in which high-fidelity simulators and the original medical devices were used. The results showed that the surgeons performed the risk analysis correctly with the stage model and afterwards were able to successfully apply the results in the workshop context.

Comparing the Effectiveness of Alerts and Dynamically Annotated Visualizations (DAVs) in Improving Clinical Decision Making

OBJECTIVE

The aim of this study was to compare the effectiveness of two types of real-time decision support, an interrupting pop-up alert and a noninterrupting dynamically annotated visualization (DAV), in reducing clinically inappropriate diagnostic imaging orders.

BACKGROUND

Alerts in electronic health record software are frequently disregarded due to high false-alarm rates, interruptions, and uncertainty about what triggered the alert. In other settings, providing visualizations and improving understandability of the guidance has been shown to improve overall decision making.

METHOD

Using a between-subject design, we examined the effect of two forms of decision support, alerts and DAVs, on reducing the proportion of inappropriate diagnostic imaging orders for 11 patients in a simulated environment. Nine attending and 11 resident physicians with experience using an electronic health record were randomly assigned to the form of decision support. Secondary measures were self-reported understandability, algorithm transparency, and clinical relevance.

RESULTS

Fewer inappropriate diagnostic imaging tests were ordered with DAVs than with alerts (18% vs. 34%, p < .001). The DAV was rated higher for all three secondary measures (p < .001) for all participants.

CONCLUSION

DAVs were more effective than alerts in reducing inappropriate imaging orders and were preferred for all patient scenarios, especially scenarios where guidance was ambiguous or based on inaccurate information.

APPLICATION

Creating visualizations that are permanently displayed and vary in the strength of their guidance can mitigate the risk of system performance degradation due to incomplete or incorrect data. This interaction paradigm may be applicable for other settings with high false-alarm rates or where there is a need to reduce interruptions during decision making.

Alarm system management: evidence-based guidance encouraging direct measurement of informativeness to improve alarm response

Although there are powerful incentives for creating alarm management programmes to reduce 'alarm fatigue', they do not provide guidance on how to reduce the likelihood that clinicians will disregard critical alarms. The literature cites numerous phenomena that contribute to alarm fatigue, although many of these, including total rate of alarms, are not supported in the literature as factors that directly impact alarm response. The contributor that is most frequently associated with alarm response is informativeness, which is defined as the proportion of total alarms that successfully conveys a specific event, and the extent to which it is a hazard. Informativeness is low across all healthcare applications, consistently ranging from 1% to 20%. Because of its likelihood and strong evidential support, informativeness should be evaluated before other contributors are considered. Methods for measuring informativeness and alarm response are discussed. Design directions for potential interventions, as well as design alternatives to traditional alarms, are also discussed. With the increased attention and investment in alarm system management that alarm interventions are currently receiving, initiatives that focus on informativeness and the other evidence-based measures identified will allow us to more effectively, efficiently and reliably redirect clinician attention, ultimately improving alarm response.

Predictive displays for a process-control schematic interface

OBJECTIVE

Our objective was to examine the extent to which increasing precision of predictive (rate of change) information in process control will improve performance on a simulated process-control task.

BACKGROUND

Predictive displays have been found to be useful in process control (as well as aviation and maritime industries). However, authors of prior research have not examined the extent to which predictive value is increased by increasing predictor resolution, nor has such research tied potential improvements to changes in process control strategy.

METHOD

Fifty nonprofessional participants each controlled a simulated chemical mixture process (honey mixer simulation) that simulated the operations found in process control. Participants in each of five groups controlled with either no predictor or a predictor ranging in the resolution of prediction of the process.

RESULTS

Increasing detail resolution generally increased the benefit of prediction over the control condition although not monotonically so. The best overall performance, combining quality and predictive ability, was obtained by the display of intermediate resolution. The two displays with the lowest resolution were clearly inferior.

CONCLUSION

Predictors with higher resolution are of value but may trade off enhanced sensitivity to variable change (lower-resolution discrete state predictor) with smoother control action (higher-resolution continuous predictors).

APPLICATION

The research provides guidelines to the process-control industry regarding displays that can most improve operator performance.

Supporting attention allocation in multitask environments: effects of likelihood alarm systems on trust, behavior, and performance

OBJECTIVE

The aim of the current study was to investigate potential benefits of likelihood alarm systems (LASs) over binary alarm systems (BASs) in a multitask environment.

BACKGROUND

Several problems are associated with the use of BASs, because most of them generate high numbers of false alarms. Operators lose trust in the systems and ignore alarms or cross-check all of them when other information is available. The first behavior harms safety, whereas the latter one reduces productivity. LASs represent an alternative, which is supposed to improve operators' attention allocation.

METHOD

We investigated LASs and BASs in a dual-task paradigm with and without the possibility to cross-check alerts with raw data information. Participants' trust in the system, their behavior, and their performance in the alert and the concurrent task were assessed.

RESULTS

Reported trust, compliance with alarms, and performance in the alert and the concurrent task were higher for the LAS than for the BAS. The cross-check option led to an increase in alert task performance for both systems and a decrease in concurrent task performance for the BAS, which did not occur in the LAS condition.

CONCLUSION

LASs improve participants' attention allocation between two different tasks and therefore lead to an increase in alert task and concurrent task performance. The performance maximum is achieved when LAS is combined with a cross-check option for validating alerts with additional information.

APPLICATION

The use of LASs instead of BASs in safety-related multitask environments has the potential to increase safety and productivity likewise.

Decision-making and response strategies in interaction with alarms: the impact of alarm reliability, availability of alarm validity information and workload

Responding to alarm systems which usually commit a number of false alarms and/or misses involves decision-making under uncertainty. Four laboratory experiments including a total of 256 participants were conducted to gain comprehensive insight into humans' dealing with this uncertainty. Specifically, it was investigated how responses to alarms/non-alarms are affected by the predictive validities of these events, and to what extent response strategies depend on whether or not the validity of alarms/non-alarms can be cross-checked against other data. Among others, the results suggest that, without cross-check possibility (experiment 1), low levels of predictive validity of alarms ( ≤ 0.5) led most participants to use one of two different strategies which both involved non-responding to a significant number of alarms (cry-wolf effect). Yet, providing access to alarm validity information reduced this effect dramatically (experiment 2). This latter result emerged independent of the effort needed for cross-checkings of alarms (experiment 3), but was affected by the workload imposed by concurrent tasks (experiment 4). Theoretical and practical consequences of these results for decision-making and response selection in interaction with alarm systems, as well as the design of effective alarm systems, are discussed.

Attentional costs and failures in air traffic control notifications

Large display screens are common in supervisory tasks, meaning that alerts are often perceived in peripheral vision. Five air traffic control notification designs were evaluated in their ability to capture attention during an ongoing supervisory task, as well as their impact on the primary task. A range of performance measures, eye-tracking and subjective reports showed that colour, even animated, was less effective than movement, and notifications sometimes went unnoticed. Designs that drew attention to the notified aircraft by a pulsating box, concentric circles or the opacity of the background resulted in faster perception and no missed notifications. However, the latter two designs were intrusive and impaired primary task performance, while the simpler animated box captured attention without an overhead cognitive cost. These results highlight the need for a holistic approach to evaluation, achieving a balance between the benefits for one aspect of performance against the potential costs for another. Practitioner summary: We performed a holistic examination of air traffic control notification designs regarding their ability to capture attention during an ongoing supervisory task. The combination of performance, eye-tracking and subjective measurements demonstrated that the best design achieved a balance between attentional power and the overhead cognitive cost to primary task performance.

A Computational Model of Task Overload Management and Task Switching

We describe a computational model that predicts the decision aspect of sequential multitasking. We investigate how people choose to switch tasks or continue performing an ongoing task when they are in overload conditions where concurrent performance of tasks is impossible. The model is based on a metaanalytic integration of 46 experiments from two literatures: interruption management and applied task switching. Consistent trends from the meta-analysis are used to set parameters in the mathematical model, which is then implemented in a task network model.

Effect of directional speech warnings on road hazard detection

BACKGROUND

In the last 2 decades, cognitive science and the transportation psychology field have dedicated a lot of effort to designing advanced driver support systems. Verbal warning systems are increasingly being implemented in modern automobiles in an effort to increase road safety.

OBJECTIVE

The study presented here investigated the impact of directional speech alert messages on the participants' speed to judge whether or not naturalistic road scenes depicted a situation of impending danger.

METHOD

Thirty-eight volunteers performed a computer-based key-press reaction time task.

RESULTS

Findings indicated that semantic content of verbal warning signals can be used for increasing driving safety and improving hazard detection. Furthermore, the classical result regarding signal accuracy is confirmed: directional informative speech messages lead to faster hazard detection compared to drivers who received a high rate of false alarms.

CONCLUSION

Notwithstanding some study limitations (lack of driver experience and low ecological validity), this evidence could provide important information for the specification of future Human-Machine-interaction (HMI) design guidelines.

Automation bias: a systematic review of frequency, effect mediators, and mitigators

Automation bias (AB)--the tendency to over-rely on automation--has been studied in various academic fields. Clinical decision support systems (CDSS) aim to benefit the clinical decision-making process. Although most research shows overall improved performance with use, there is often a failure to recognize the new errors that CDSS can introduce. With a focus on healthcare, a systematic review of the literature from a variety of research fields has been carried out, assessing the frequency and severity of AB, the effect mediators, and interventions potentially mitigating this effect. This is discussed alongside automation-induced complacency, or insufficient monitoring of automation output. A mix of subject specific and freetext terms around the themes of automation, human-automation interaction, and task performance and error were used to search article databases. Of 13 821 retrieved papers, 74 met the inclusion criteria. User factors such as cognitive style, decision support systems (DSS), and task specific experience mediated AB, as did attitudinal driving factors such as trust and confidence. Environmental mediators included workload, task complexity, and time constraint, which pressurized cognitive resources. Mitigators of AB included implementation factors such as training and emphasizing user accountability, and DSS design factors such as the position of advice on the screen, updated confidence levels attached to DSS output, and the provision of information versus recommendation. By uncovering the mechanisms by which AB operates, this review aims to help optimize the clinical decision-making process for CDSS developers and healthcare practitioners.

An eye movement analysis of the effect of interruption modality on primary task resumption

OBJECTIVE

We examined the effect of interruption modality (visual or auditory) on primary task (visual) resumption to determine which modality was the least disruptive.

BACKGROUND

Theories examining interruption modality have focused on specific periods of the interruption timeline. Preemption theory has focused on the switch from the primary task to the interrupting task. Multiple resource theory has focused on interrupting tasks that are to be performed concurrently with the primary task. Our focus was on examining how interruption modality influences task resumption.We leverage the memory-for-goals theory, which suggests that maintaining an associative link between environmental cues and the suspended primary task goal is important for resumption.

METHOD

Three interruption modality conditions were examined: auditory interruption with the primary task visible, auditory interruption with a blank screen occluding the primary task, and a visual interruption occluding the primary task. Reaction time and eye movement data were collected.

RESULTS

The auditory condition with the primary task visible was the least disruptive. Eye movement data suggest that participants in this condition were actively maintaining an associative link between relevant environmental cues on the primary task interface and the suspended primary task goal during the interruption.

CONCLUSION

These data suggest that maintaining cue association is the important factor for reducing the disruptiveness of interruptions, not interruption modality.

APPLICATION

Interruption-prone computing environments should be designed to allow for the user to have access to relevant primary task cues during an interruption to minimize disruptiveness.

Response Criterion Placement Modulates the Benefits of Graded Alerting Systems in a Simulated Baggage Screening Task

An experiment compared the benefits of two-level and graded alerting systems as human performance aids in a simulated baggage x-ray screening task. Decision boundaries for the graded systems were varied to produce an unhesitant aid, one which rendered a diagnostic judgment on a majority of trials, and a hesitant aid, one which rendered a judgment on only a minority of all trials. Judgments from the aid were rendered as text messages preceding each trial. The participants' task was to search for threat objects in simulated baggage x-rays; true target presence rate was 50%. The unhesitant three-level aid significantly improved human performance relative to an unaided control condition, and produced better human performance than either the hesitant three-level aid or the two-level aid. The benefits of the unhesitant three-level system arose from the human operators' increased willingness to act on the aid's diagnoses. Results carry implications for the design of automated diagnostic decision aids for security screening and similar signal detection tasks.

Multiple resources and mental workload

OBJECTIVE

The objective is to lay out the rationale for multiple resource theory and the particular 4-D multiple resource model, as well as to show how the model is useful both as a design tool and as a means of predicting multitask workload overload.

BACKGROUND

I describe the discoveries and developments regarding multiple resource theory that have emerged over the past 50 years that contribute to performance and workload prediction.

METHOD

The article presents a history of the multiple resource concept, a computational version of the multiple resource model applied to multitask driving simulation data, and the relation of multiple resources to workload.

RESULTS

Research revealed the importance of the four dimensions in accounting for task interference and the association of resources with brain structure. Multiple resource models yielded high correlations between model predictions and data. Lower correlations also identified the existence of additional resources.

CONCLUSION

The model was shown to be partially relevant to the concept of mental workload, with greatest relevance to performance breakdowns related to dual-task overload. Future challenges are identified.

APPLICATION

The most important application of the multiple resource model is to recommend design changes when conditions of multitask resource overload exist.

Humans: still vital after all these years of automation

OBJECTIVE

The authors discuss empirical studies of human-automation interaction and their implications for automation design.

BACKGROUND

Automation is prevalent in safety-critical systems and increasingly in everyday life. Many studies of human performance in automated systems have been conducted over the past 30 years.

METHODS

Developments in three areas are examined: levels and stages of automation, reliance on and compliance with automation, and adaptive automation.

RESULTS

Automation applied to information analysis or decision-making functions leads to differential system performance benefits and costs that must be considered in choosing appropriate levels and stages of automation. Human user dependence on automated alerts and advisories reflects two components of operator trust, reliance and compliance, which are in turn determined by the threshold designers use to balance automation misses and false alarms. Finally, adaptive automation can provide additional benefits in balancing workload and maintaining the user's situation awareness, although more research is required to identify when adaptation should be user controlled or system driven.

CONCLUSIONS

The past three decades of empirical research on humans and automation has provided a strong science base that can be used to guide the design of automated systems.

APPLICATION

This research can be applied to most current and future automated systems.