Engineering topics

Freeman, Frederick G.

Publications and source records attributed to Freeman, Frederick G..

11 records

NASA NTRS2007

Physiological Self-Regulation and Adaptive Automation

Adaptive automation has been proposed as a solution to current problems of human-automation interaction. Past research has shown the potential of this advanced form of automation to enhance pilot engagement and lower cognitive workload. However, there have been concerns voiced regarding issues, such as automation surprises, associated with the use of adaptive automation. This study examined the use of psychophysiological self-regulation training with adaptive automation that may help pilots deal with these problems through the enhancement of cognitive resource management skills. Eighteen participants were assigned to 3 groups (self-regulation training, false feedback, and control) and performed resource management, monitoring, and tracking tasks from the Multiple Attribute Task Battery. The tracking task was cycled between 3 levels of task difficulty (automatic, adaptive aiding, manual) on the basis of the electroencephalogram-derived engagement index. The other two tasks remained in automatic mode that had a single automation failure. Those participants who had received self-regulation training performed significantly better and reported lower National Aeronautics and Space Administration Task Load Index scores than participants in the false feedback and control groups. The theoretical and practical implications of these results for adaptive automation are discussed.

Prinzell, Lawrence J.↗

NASA NTRS2004

A Comparison of a Brain-Based Adaptive System and a Manual Adaptable System for Invoking Automation

Two experiments are presented that examine alternative methods for invoking automation. In each experiment, participants were asked to perform simultaneously a monitoring task and a resource management task as well as a tracking task that changed between automatic and manual modes. The monitoring task required participants to detect failures of an automated system to correct aberrant conditions under either high or low system reliability. Performance on each task was assessed as well as situation awareness and subjective workload. In the first experiment, half of the participants worked with a brain-based system that used their EEG signals to switch the tracking task between automatic and manual modes. The remaining participants were yoked to participants from the adaptive condition and received the same schedule of mode switches, but their EEG had no effect on the automation. Within each group, half of the participants were assigned to either the low or high reliability monitoring task. In addition, within each combination of automation invocation and system reliability, participants were separated into high and low complacency potential groups. The results revealed no significant effects of automation invocation on the performance measures; however, the high complacency individuals demonstrated better situation awareness when working with the adaptive automation system. The second experiment was the same as the first with one important exception. Automation was invoked manually. Thus, half of the participants pressed a button to invoke automation for 10 s. The remaining participants were yoked to participants from the adaptable condition and received the same schedule of mode switches, but they had no control over the automation. The results showed that participants who could invoke automation performed more poorly on the resource management task and reported higher levels of subjective workload. Further, those who invoked automation more frequently performed more poorly on the tracking task and reported higher levels of subjective workload. and the adaptable condition in the second experiment revealed only one significant difference: the subjective workload was higher in the adaptable condition. Overall, the results show that a brain-based, adaptive automation system may facilitate situation awareness for those individuals who are more complacent toward automation. By contrast, requiring operators to invoke automation manually may have some detrimental impact on performance but does appear to increases subjective workload relative to an adaptive system.

Bailey, Nathan R.↗

NASA NTRS2003

Effects of a psychophysiological system for adaptive automation on performance, workload, and the event-related potential P300 component

The present study examined the effects of an electroencephalographic- (EEG-) based system for adaptive automation on tracking performance and workload. In addition, event-related potentials (ERPs) to a secondary task were derived to determine whether they would provide an additional degree of workload specificity. Participants were run in an adaptive automation condition, in which the system switched between manual and automatic task modes based on the value of each individual's own EEG engagement index; a yoked control condition; or another control group, in which task mode switches followed a random pattern. Adaptive automation improved performance and resulted in lower levels of workload. Further, the P300 component of the ERP paralleled the sensitivity to task demands of the performance and subjective measures across conditions. These results indicate that it is possible to improve performance with a psychophysiological adaptive automation system and that ERPs may provide an alternative means for distinguishing among levels of cognitive task demand in such systems. Actual or potential applications of this research include improved methods for assessing operator workload and performance.

Task Performance and Analysis↗

NASA NTRS2003

Three Experiments Examining the Use of Electroencephalogram,Event-Related Potentials, and Heart-Rate Variability for Real-Time Human-Centered Adaptive Automation Design

Adaptive automation represents an advanced form of human-centered automation design. The approach to automation provides for real-time and model-based assessments of human-automation interaction, determines whether the human has entered into a hazardous state of awareness and then modulates the task environment to keep the operator in-the-loop , while maintaining an optimal state of task engagement and mental alertness. Because adaptive automation has not matured, numerous challenges remain, including what the criteria are, for determining when adaptive aiding and adaptive function allocation should take place. Human factors experts in the area have suggested a number of measures including the use of psychophysiology. This NASA Technical Paper reports on three experiments that examined the psychophysiological measures of event-related potentials, electroencephalogram, and heart-rate variability for real-time adaptive automation. The results of the experiments confirm the efficacy of these measures for use in both a developmental and operational role for adaptive automation design. The implications of these results and future directions for psychophysiology and human-centered automation design are discussed.

Prinzel, Lawrence J., III↗

NASA NTRS2001

Application of Physiological Self-Regulation and Adaptive Task Allocation Techniques for Controlling Operator Hazardous States of Awareness

Prinzel, Hadley, Freeman, and Mikulka found that adaptive task allocation significantly enhanced performance only when used at the endpoints of the task workload continuum (i.e., very low or high workload), but that the technique degraded performance if invoked during other levels of task demand. These researchers suggested that other techniques should be used in conjunction with adaptive automation to help minimize the onset of hazardous states of awareness (HSA) and keep the operator 'in-the-loop.' The paper reports on such a technique that uses psychophysiological self-regulation to modulate the level of task engagement. Eighteen participants were assigned to three groups (self-regulation, false feedback, and control) and performed a compensatory tracking task that was cycled between three levels of task difficulty on the basis of the electroencephalogram (EEG) record. Those participants who had received self-regulation training performed significantly better and reported lower NASA-TLX scores than participants in the false feedback and control groups. Furthermore, the false feedback and control groups had significantly more task allocations resulting in return-to-manual performance decrements and higher EEG difference scores. Theoretical and practical implications of these results for adaptive automation are discussed.

Prinzel, Lawrence J., III↗

NASA NTRS2001

Empirical Analysis of EEG and ERPs for Psychophysiological Adaptive Task Allocation

The present study was designed to test the efficacy of using Electroencephalogram (EEG) and Event-Related Potentials (ERPs) for making task allocation decisions. Thirty-six participants were randomly assigned to an experimental, yoked, or control group condition. Under the experimental condition, a tracking task was switched between task modes based upon the participant's EEG. The results showed that the use of adaptive aiding improved performance and lowered subjective workload under negative feedback as predicted. Additionally, participants in the adaptive group had significantly lower RMSE and NASA-TLX ratings than participants in either the yoked or control group conditions. Furthermore, the amplitudes of the N1 and P3 ERP components were significantly larger under the experimental group condition than under either the yoked or control group conditions. These results are discussed in terms of the implications for adaptive automation design.

Prinzel, Lawrence J., III↗

NASA NTRS2001

The Efficacy of Psychophysiological Measures for Implementing Adaptive Technology

Adaptive automation refers to technology that can change its mode of operation dynamically. Further, both the technology and the operator can initiate changes in the level or mode of automation. The present paper reviews research on adaptive technology. It is divided into three primary sections. In the first section, issues surrounding the development and implementation of adaptive automation are presented. Because physiological-based measures show much promise for implementing adaptive automation, the second section is devoted to examining candidate indices. In the final section, those techniques that show the greatest promise for adaptive automation as well as issues that still need to be resolved are discussed.

Scerbo, Mark W.↗

NASA NTRS2001

User Acceptability of Physiological and Other Measures of Hazardous States of Awareness

Two studies explored user acceptance of devices that measure hazardous states of awareness. In the first study, critical incident data were collected in two workshops from 11 operators working as air traffic controllers or commercial pilots. These critical incident data were used to develop a survey of the acceptability of awareness measures. In the second study, the survey was administered to 100 people also working as air traffic controllers or commercial pilots. Results show that operators are open to the inclusion of technology to measure HSAs even if that technology is somewhat invasive as long as feedback about the HSAs is considered to be useful and helpful. Nonetheless, a major concern is the legal complications associated with being recorded, particularly for older and more experienced operators. Air traffic controllers emphasized the importance of sharing technology information with supervisors in order to receive backup or assistance under conditions of task overload, whereas pilots emphasized the influence of work schedules on problems with awareness. Recommendations are offered concerning the implementation of devices to measure hazardous states of awareness.

Dickinson, Terry L.↗

NASA NTRS2000

Biocybernetic Control of Vigilance Task Parameters

The major focus of the present proposal was to examine psychophysiological variables that are related to hazardous states of awareness induced by monitoring automated systems. With the increased use of automation in today's work environment, people's roles in the work place are being redefined from that of active participant to one of passive monitor. Although the introduction of automated systems has a number of benefits, there are also a number of disadvantages regarding the worker performance. Byrne and Parasuraman (1996) have argued for the use of psychophysiological measures in both the development and the implementation of adaptive automation. While both performance based and model based adaptive automation have been studied, the use of psychophysiological measures, especially EEG, offers the advantage of real time evaluation of the state of the subject. Previous investigations of the closed-loop adaptive automation system in our laboratory, supported by NASA, have employed a compensatory tracking task which involved the use of a joystick to maintain the position of a cursor in the middle of a video screen. This research demonstrated that, in an adaptive automation, closed-loop environment, subjects perform a tracking task better under a negative, compared to a positive, feedback condition. While tracking is comparable to some aspects of flying an airplane, it does not simulate the environment found in the cockpit of modern commercial airplanes. Since a large part of the flying responsibilities in commercial airplanes is automated, the primary responsibility of pilots is to monitor the automation and to respond when the automation fails. Because failures are relatively rare, pilots often suffer from hazardous states of awareness induced by long term vigilance of the automated system. Consequently, the aim of the current study was to investigate the ability of the closed-loop, adaptive automation system in a vigilance paradigm. It is also important to note that tracking involves a continuous, though low level, motor response. Since it is not clear how such activity might affect performance of the adaptive automation system, it was thought to be important to evaluate how the system functioned when there was minimal motor output by the subjects. The current study used the closed-loop system, developed at NASA-Langley Research Center, to control the state of awareness of subjects while they performed a vigilance task. Several experiments were conducted to examine the use of EEG feedback to control a target dimension used in the task. Changes in a subject's arousal, as defined by specific EEG indexes, produced stimulus changes known to affect task performance. In addition, different electrode sites, compared to previous research, were sampled to determine the optimum configuration with regard to the following criteria: (1) task performance and (2) EEG index.

Freeman, Frederick G.↗

NASA NTRS1998

NASA Graduate Student Researchers Program (GSRP)

The primary area of research that Dr. Prinzel conducted was concerned with the effects of automation on the ability to perform various tasks which simulated those performed in an airplane cockpit. While automation is generally considered to be a positive addition to the cockpit, it also has some negative effects. The primary negative effect is related to the decrease in the number of tasks the pilot needs to perform. Having less to do can actually result in a decreased ability to be aware of and react to ongoing events, some of which may be critical to flight operations. The research was concerned with the ability of adaptive automation using psychophysiological measures, specifically brain waves (i.e. EEG), to control the degree to which tasks are automated as a function of the subject's level of alertness. Dr. Prinzel conducted and participated in the running of a number of experiments that examined adaptive automation in conjunction with the MultiAttribute Task Battery. Since there has been very little research conducted in this area, experiments needed to be conducted which established basic parameters for future research. The experiments that Dr. Prinzel was involved with examined the optimum number and location of electrode sites for recording EEG, the types of tasks which could readily be automated, the number of tasks which could be controlled by the adaptive automation system, and various EEG recording parameters which affect system functioning. A secondary area in which Dr. Prinzel conducted research concerned performance on vigiliance tasks. This research, while not initially directly related to adaptive automation, does have implication for such systems. Vigilance requires long term monitoring which often leads to the kind of negative effects seen when automation is introduced into a work environment. A third area of research in which Dr. Prinzel conducted research was concerned with gender differences in cognitive functioning and how that related to strategies for performing various tasks.

Freeman, Frederick G.↗

NASA NTRS1993

Brain wave correlates of attentional states: Event related potentials and quantitative EEG analysis during performance of cognitive and perceptual tasks

The increased use of automation in the cockpits of commercial planes has dramatically decreased the workload requirements of pilots, enabling them to function more efficiently and with a higher degree of safety. Unfortunately, advances in technology have led to an unexpected problem: the decreased demands on pilots have increased the probability of inducing 'hazardous states of awareness.' A hazardous state of awareness is defined as a decreased level of alertness or arousal which makes an individual less capable of reacting to unique or emergency types of situations. These states tend to be induced when an individual is not actively processing information. Under such conditions a person is likely to let his/her mind wander, either to internal states or to irrelevant external conditions. As a result, they are less capable of reacting quickly to emergency situations. Since emergencies are relatively rare, and since the high automated cockpit requires progressively decreasing levels of engagement, the probability of being seduced into a lowered state of awareness is increasing. This further decreases the readiness of the pilot to react to unique circumstances such as system failures. The HEM Lab at NASA-Langley Research Center has been studying how these states of awareness are induced and what the physiological correlates of these different states are. Specifically, they have been interested in studying electroencephalographic (EEG) measures of different states of alertness to determine if such states can be identified and, hopefully, avoided. The project worked on this summer involved analyzing the EEG and the event related potentials (ERP) data collected while subjects performed under two conditions. Each condition required subjects to perform a relatively boring vigilance task. The purpose of using these tasks was to induce a decreased state of awareness while still requiring the subject to process information. Each task involved identifying an infrequently presented target stimulus. In addition to the task requirements, irrelevant tones were presented in the background. Research has shown that even though these stimuli are not attended, ERP's to them can still be elicited. The amplitude of the ERP waves has been shown to change as a function of a person's level of alertness. ERP's were also collected and analyzed for the target stimuli for each task. Brain maps were produced based on the ERP voltages for the different stimuli. In addition to the ERP's, a quantitative EEG (QEEG) was performed on the data using a fast Fourier technique to produce a power spectral analysis of the EEG. This analysis was conducted on the continuous EEG while the subjects were performing the tasks. Finally, a QEEG was performed on periods during the task when subjects indicated that they were in an altered state of awareness. During the tasks, subjects were asked to indicate by pressing a button when they realized their level of task awareness had changed. EEG epochs were collected for times just before and just after subjects made this reponse. The purpose of this final analysis was to determine whether or not subjective indices of level of awareness could be correlated with different patterns of EEG.

Freeman, Frederick G.↗