Prolonging the Response Movement Reduces Commission Errors in a High-Go, Low-No-Go Target Detection Task and Composite Metrics of Performance Miss This Effect

OBJECTIVE

Expand research on the Sustained Attention to Response Task (SART) to a more applied agricultural target detection/selection task and examine the utility of various performance metrics, including composite measures of speed and accuracy, in a High-Go/Low-No-Go stimuli task.

BACKGROUND

Modified SARTs have been utilized to investigate mechanisms, such as failures of response inhibition, occurring in friendly fire and collateral damage incidents. Researchers have demonstrated that composite measures of speed and accuracy are useful for Low Go/High No-Go stimuli tasks, but this has not been demonstrated for High-Go/Low-No-Go tasks, such as the SART.

METHOD

Participants performed a modified SART, where they selected ("sprayed") images of weeds (Go stimuli) that appeared on a computer screen, while withholding to rarer soybean plant images (No-Go stimuli).

RESULTS

Response time was a function of distance from a central starting point. Participants committed commission errors (sprayed the soybeans) at a significantly higher rate when the stimuli appeared under the cursor centered on the screen for each trial. Participant's omission errors (failure to spray a weed) increased significantly as a function of distance. The composite measures examined were primarily influenced by response time and omission errors limiting their utility when commission errors are of particular interest.

CONCLUSION

Participants are far more accurate in their decision making when required to execute a longer duration motor task in High-Go/Low-No-Go experiments.

APPLICATION

Demonstrates a serious human factors liability of target detection and snap-to-target systems.

Improving Healthcare Practice Through the Implementation of Human Factors and Ergonomics Principles

OBJECTIVE

Present a collection of papers focusing on improving healthcare practice through the implementation of human factors and ergonomics principles that were presented at the International Ergonomics Association (IEA) 2021 international conference.

BACKGROUND

The mission of the IEA is to elaborate and advance ergonomics science and practice and to expand its scope of application.

METHOD

We reviewed papers that were submitted for presentation at the IEA 2021 international conference and focused on improving healthcare practice through the implementation of human factors and ergonomics principles.

RESULTS

The eight papers that are included in this special issue cover varied aspects of human factors application and implementation.

CONCLUSION

This special issue provides clear evidence that the science of human factors is relevant and is continuing to grow and so is its implementation in healthcare.

APPLICATION

This special issue offers a selection of applied works, providing a wide scope of human factors guidelines, methods, and theories in healthcare work environments.

Predicting Design-Induced Error on the Flight Deck : An Aircraft Engine Oil Leak Scenario

OBJECTIVE

To explore the types of errors that commercial pilots may make when trying to resolve a suspected engine oil leak using the interfaces currently available.

BACKGROUND

The decisions that pilots make often have to be made quickly and under time pressure, with the emphasis on avoiding critical situations from arising. To make the correct decisions, it is vital that pilots have accurate and up-to-date information available. However, interaction with flight deck interfaces may lead to error if they are not effectively designed.

METHOD

A hierarchical task analysis was conducted using evidence from pilot interview data to understand the pilots' typical response to a suspected engine oil leak scenario. This was used as the primary input into the Systematic Human Error Reduction and Prediction Approach (SHERPA).

RESULTS

A total of 108 possible errors were identified. The most common error type was a retrieval error, in which flight crews may retrieve the wrong information about the engine. A number of remedial measures are proposed to try and overcome such issues.

CONCLUSION

This analysis provides an initial starting point for identifying potential future design ideas that can assist the pilots in dealing with oil leaks.

APPLICATION

This work has identified the value of applying human error identification methodologies to the assessment of current flight deck processes surrounding engine oil leaks. The method presented permits the operational analysis of possible errors on the flight deck and facilitates the proposition of remedial measures to implement technological innovations that can mitigate error.

Effects of Neural Mechanisms of Pretask Resting EEG Alpha Information on Situational Awareness: A Functional Connectivity Approach

OBJECTIVE

In this study, the influence of pretask resting neural mechanisms on situational awareness (SA)-task is studied.

BACKGROUND

Pretask electroencephalography (EEG) information and Stroop effect are known to influence task engagement independently. However, neural mechanisms of pretask resting absolute alpha (PRAA) and pretask resting alpha frontal asymmetry (PRAFA) in influencing SA-task which is undergoing Stroop effect is still not understood.

METHOD

The study involved pretask resting EEG measurements from 18 healthy individuals followed by functional magnetic resonance imaging (fMRI) acquisition during SA-task. To understand the effect of pretask alpha information and Stroop effect on SA, a robust correlation between mean reaction time, SA Index, PRAA, and PRAFA were assessed. Furthermore, neural underpinnings of PRAA, PRAFA in SA-task, and functional connectivity were analyzed through the EEG-informed fMRI approach.

RESULTS

Significant robust correlation of reaction time was observed with SA Index (Pearson: r = .50, pcorr = .05) and PRAFA (Pearson: r = .63; pcorr = .01), respectively. Similarly, SA Index significantly correlated with PRAFA (Pearson: r = .56, pcorr = .01; Spearman: r = .61, pcorr = .007), and PRAA (Pearson: r = .59, pcorr = .005; Spearman: r = .59, pcorr = .002). Neural underpinnings of SA-task revealed regions involved in visual-processing and higher-order cognition. PRAA was primarily underpinned at frontal-temporal areas and functionally connected to SA-task regions pertaining to the emotional regulation. PRAFA has correlated with limbic and parietal regions, which are involved in integration of visual, emotion, and memory information of SA-task.

CONCLUSION

The results suggest a strong association of reaction time with SA-task and PRAFA and strongly support the hypothesis that PRAFA, PRAA, and associated neural mechanisms significantly influence the outcome of SA-task.

APPLICATION

It is beneficial to study the effect of pretask resting information on SA-task to improve SA.

Extending Three Existing Models to Analysis of Trust in Automation: Signal Detection, Statistical Parameter Estimation, and Model-Based Control

OBJECTIVE

The objective is to propose three quantitative models of trust in automation.

BACKGROUND

Current trust-in-automation literature includes various definitions and frameworks, which are reviewed.

METHOD

This research shows how three existing models, namely those for signal detection, statistical parameter estimation calibration, and internal model-based control, can be revised and reinterpreted to apply to trust in automation useful for human-system interaction design.

RESULTS

The resulting reinterpretation is presented quantitatively and graphically, and the measures for trust and trust calibration are discussed, along with examples of application.

CONCLUSION

The resulting models can be applied to provide quantitative trust measures in future experiments or system designs.

APPLICATIONS

Simple examples are provided to explain how model application works for the three trust contexts that correspond to signal detection, parameter estimation calibration, and model-based open-loop control.

Perceived Spaciousness and Preference in Sequential Experience

OBJECTIVE

We assessed the perceived spaciousness and preference for a destination space in relation to six attributes (size, lighting, window size, texture, wall mural, and amount of furniture) of it and of the space experienced before it.

BACKGROUND

Studies have examined effects of these attributes but not for dynamic experience or preference.

METHOD

We created 24 virtual reality walks between each possible pair of two levels of each attribute. For each destination space, 31 students (13 men, 18 women) rated spaciousness and 30 students (16 men, 14 women) rated preference. We conducted separate 2 × 2 repeated-measure ANOVAs across each condition for perceived spaciousness and preference.

RESULTS

Participants judged the space that was larger, was more brightly lit, with a larger window, or with less furniture as the more spacious. These attributes also increased preference. Consonant with adaptation-level theory, participants judged offices as higher in spaciousness and preference if preceded by a space that was smaller, was more dimly lit, or had smaller windows.

CONCLUSION

The findings suggest that perceived spaciousness varies with size, lightness, window size, and amount of furniture but that perception also depends on the size, lightness, and size of the space experienced before.

APPLICATION

Designers could use the findings to manipulate features to make a space appear larger or more desirable.

Time Sharing Between Robotics and Process Control: Validating a Model of Attention Switching

OBJECTIVE

The aim of this study was to validate the strategic task overload management (STOM) model that predicts task switching when concurrence is impossible.

BACKGROUND

The STOM model predicts that in overload, tasks will be switched to, to the extent that they are attractive on task attributes of high priority, interest, and salience and low difficulty. But more-difficult tasks are less likely to be switched away from once they are being performed.

METHOD

In Experiment 1, participants performed four tasks of the Multi-Attribute Task Battery and provided task-switching data to inform the role of difficulty and priority. In Experiment 2, participants concurrently performed an environmental control task and a robotic arm simulation. Workload was varied by automation of arm movement and both the phases of environmental control and existence of decision support for fault management. Attention to the two tasks was measured using a head tracker.

RESULTS

Experiment 1 revealed the lack of influence of task priority and confirmed the differing roles of task difficulty. In Experiment 2, the percentage attention allocation across the eight conditions was predicted by the STOM model when participants rated the four attributes. Model predictions were compared against empirical data and accounted for over 95% of variance in task allocation. More-difficult tasks were performed longer than easier tasks. Task priority does not influence allocation.

CONCLUSIONS

The multiattribute decision model provided a good fit to the data.

APPLICATIONS

The STOM model is useful for predicting cognitive tunneling given that human-in-the-loop simulation is time-consuming and expensive.

Exploring relationships of human-automation interaction consequences on pilots: uncovering subsystems

OBJECTIVE

We attempted to understand the latent structure underlying the systems pilots use to operate in situations involving human-automation interaction (HAI).

BACKGROUND

HAI is an important characteristic of many modern work situations. Of course, the cognitive subsystems are not immediately apparent by observing a functioning system, but correlations between variables may reveal important relations.

METHOD

The current report examined pilot judgments of 11 HAI dimensions (e.g., Workload, Task Management, Stress/Nervousness, Monitoring Automation, and Cross-Checking Automation) across 48 scenarios that required airline pilots to interact with automation on the flight deck.

RESULTS

We found three major clusters of the dimensions identifying subsystems on the flight deck: a workload subsystem, a management subsystem, and an awareness subsystem.

DISCUSSION

Relationships characterized by simple correlations cohered in ways that suggested underlying subsystems consistent with those that had previously been theorized.

APPLICATION

Understanding the relationship among dimensions affecting HAI is an important aspect in determining how a new piece of automation designed to affect one dimension will affect other dimensions as well.