# Presentation: 934200

## PHYSIOLOGICAL SELF

- <date/time>

-   HAZARDOUS STATES

- Lance Prinzel, Fred F

- Old Dominio
- NASA Langley

## -REGULATION OF

- OF AWARENESS

- reeman, & Alan Pope

- n University
- Research Center

## INTRODUCTION

- The aviation industry has witnessed a dramatic increase in the level of automation control over the past twenty years.  The benefits of automation have been well-documented and include increased fuel economy, reduction of manual workload and fatigue, economy of cockpit space, increased precision of flight maneuvers and navigation to name but a few.  However, the benefits realized with increased automation have not been without cost.  Research has shown that automation also has a number of problems associated with its use including monitoring inefficiency, loss of situational awareness, increased mental workload, cognitive overhead, poor return-to-manual control, decreased vigilance and inattention, mode unawareness, and complacency.
- Such problems as these have resulted in a number of “human-centered” automation concepts that have been proposed to improve the pilot-automation interaction.  One such concept has been termed adaptive task allocation or adaptive automation.  Adaptive automation refers to automation that is invoked in response to changing task demands.  Unlike traditional forms of automation, adaptive automation attempts to regulate pilot situational awareness and other capabilities through real-time task allocation between the automated system and pilot.

## Woods (1996) noted that automation represents “apparent simplicity, real complexity” referring to the idea that automation transforms the nature of pilot-automation interaction.  New forms of automation may bring with them new problems.  Adaptive automation may not be an exception to such an observation.  Rudisill (1994) found that pilots tended to be positive about technological innovations, but still had concerns about advanced forms of automation.  They noted that advanced automation kept them “out-of-the-loop” and that pilots constantly needed to monitor what the automation was doing.  Also, advanced automation tended to increase their workload and decrease cockpit management and flight crew communication.  Pilots reported that what they wanted were new approaches to training that would ameliorate some of these problems associated with advanced automation.  

- Prinzel, Hadley, Freeman, & Mikulka (1997) reported that adaptive task allocation may be best reserved at the endpoints of the task engagement continuum and other techniques should be used in conjunction with adaptive automation to help minimize the onset of hazardous states of awareness (Pope & Bogart, 1992) and keep the pilot “in-the-loop.”  They suggested that one training technique that may be employed  is psychophysiological self-regulation.

## Psychophysiological self-regulation refers to the ability of a person to control affective and cognitive states based on autonomic (ANS) and central nervous system (CNS) functioning.  The techniques use physiological markers of these states and provide biofeedback so that the person learns these associations and how to modulate their occurrence.  A biofeedback system is actually an example of a closed-loop feedback system.  While ecological and engineering feedback control systems regulate physical variables, biofeedback systems take advantage of the benefits of knowledge-of-results and immediate reinforcement effects to facilitate learning of voluntary physiological control.  Such systems can be used to reveal information about relationships among physiological and psychological variables.  Knowledge of these relationships can, in turn, be used to assess the interaction of human operators with complex systems.  And some of these alternative applications can "feed back" to inform the design of adaptive systems.

_**The Closed-Loop System**_
- Research conducted at the Crew Hazards and Error Management laboratory at NASA Langley Research Center and the psychology department at Old Dominon University has been directed on the development of a biocybernetic, closed-loop system that uses psychophysiological measures to help control the onset of “hazardous states of awareness.”

## This closed-loop method was developed to support the determination of optimal human (manual)/system (automated) task allocation "mixes," based upon a brain activity criterion of consistent mental engagement.  In this method, the level of automation of the tasks may be varied so that all, none, or a subset of tasks require subject intervention, enabling a range of levels of demand for operator involvement in system management to be imposed.  A biocybernetic loop is formed by adjusting the number of manual tasks imposed on the subject, based on electroencephalographic (EEG) signals reflecting an operator's engagement in the task set.  An optimal number of manual versus automated tasks is arrived at by allowing the closed-loop (negative) feedback system to attain stable operation, reflecting stable engagement, and is defined by the subset of tasks that maintain stable operation. 

- Research has shown promise for the closed-loop system to serve in both regulatory and developmental roles for the use of psychophysiology in adaptive automated systems (Byrne & Parasuraman, 1996). To date, however, our research had focused on the examination of system parameters for the real-time task allocation of automation modes.  Furthermore, the task mode alone was responsible for determining task allocation sequencing; that is, what automation mode the task was in determined the engagement level of the participants which therein determined subsequent task modes.  However, research in biofeedback and self-regulation has demonstrated the capabilities that people have to control their own engagement states.  Therefore, it seems reasonable to explore the biofeedback potential of the system as a training tool for developing self-regulation skills for managing hazardous states of awareness.

## METHOD

_**Participants**_.  Eighteen students served as participants for the study. Participants were randomly assigned to either the self-regulation,  control, or false feedback condition.
_**Experimental Task**_**.  **The task used for the experiment was a compensatory tracking task from the Multi-Attribute Task Battery (Figure 1; Comstock & Arnegard, 1992).
_**Experimental Variables**_.  Tracking performance was measured by root-mean-squared-error (RMSE), and subjective workload was assessed using the NASA-TLX.  Relative power of theta, beta, and alpha at each cortical site was measured.  The EEG engagement index (see below) used was  beta / (alpha + theta) and has been shown to be a valid index of task engagement.  Number of task allocations between engagement levels was also gathered as a dependent variable.
_**EEG Recording and Analysis**_*.*  The EEG was recorded from sites Cz, Pz, P3, and P4. A ground site was used located midway between Fpz and Fz.  Each site was referenced to the right mastoid. The total EEG power from the bands of theta, alpha, and beta for each of the three sites was measured.  The EEG frequency bands were set as follows: alpha (8-13 Hz), beta (13-22 Hz), and theta (4-8 Hz).

## EEG Engagement Index.  Prior to starting the task, a five-minute EEG baseline period was recorded.  The mean of the EEG engagement index derived from the baseline EEG was then fed into the biocybernetic system.  The system uses a moving window process in which a derived EEG index was calculated over a 40-second window.  The window was advanced two seconds and a new average is derived.  This moving window process continues for the duration of the trial.  An increasing EEG index indicates that the participant's arousal level is increasing and a decreasing index represents that arousal is decreasing. 

_**Experimental Procedure**_.  Participants in the self-regulation condition were provided with two 30-minute training session, each consisting of two experimental blocks, that provided neurofeedback as to their engagement level with the task.  These participants were then asked to return one week later to participate in the assessment session.  Feedback on engagement level was provided in the right-hand corner of the tracking window (e.g., “Level One”) and a tone during the training sessions, and they were encouraged to try and maintain Level 3 or 4 engagement levels.  The false feedback condition  consisted of random feedback as to the participant's engagement level while performing the task.  Participants in the control condition did receive any feedback.
- All participants performed one 30-minute assessment trial, and no neurofeedback was provided to either group.  The NASA-TLX and a post-assessment interview were completed once the session had ended.

## Slide 9

## }

*f*

- Hazardous States of
- Awareness Assessment

- Physiological Self-Regulation
- Tools and Methods would be
- implemented into simulator
- facilities

- Where it would be used as
- a  tool for helping
- pilots regulate their states of
- awareness

- Where they could then take
- the skills learned and apply them
- in an operational context

- Also, flight crews could
- learn to help each other
- regulate their engagement
- states and guard against
- HSAs

## Performance Better

- EEG is collected for later
- ERP analyses

- Analog Signal Out

- EEG engagement index calculated.
- Feedback and task allocation
- decisions made

- Tone generated

- Subjective Workload Rated Better
- Task Allocation Range Narrower
- Better Physiological Engagement

## TRACKING PERFORMANCE

## EEG DIFFERENCE INDEX

## NASA-TLX

## TASK ALLOCATIONS

## DISCUSSION

- “Human-centered” automation design details how technology changes human-automation interaction and how best *to support* the roles that people now have to play as supervisory controllers, exception handlers, and monitors and managers of automated resources (Sarter, Woods, & Billings, 1997).  Self-regulation may represent another tool for supporting human-centered design.  Participants in the self-regulation condition were better able to maintain their task engagement level within a narrower range of task modes thereby reducing the need for task allocations.

## The effect of this was an increase in task performance as well as a decrease in reported workload.  Furthermore, these results may have been due to the increase in return-to-manual performance deficits witnessed in the control and false feedback conditions. Although both conditions experienced task allocations, participants in the self-regulation group had fewer task allocations and had significantly lower tracking error scores just after a task allocation than those participants in the other two conditions.   The neurofeedback provided may have allowed these participants to better manage their “resources” and thereby regulate their engagement state allowing them to better respond to a change in automation mode.  

- Scerbo (1996) noted that there is a need to understand how this new form of technology will change the human-automation interaction and to develop training methods to help support the development of adaptive automation.  Sarter, Woods, and Billings (1997) noted that “...training cannot and should not be a fix for bad design” and there are many issues that still need to be addressed with adaptive automation.  Nevertheless, these results support other studies that have demonstrated that physiological self-regulation can help in controlling the onset of hazardous states of awareness, and suggests itself to be a valuable complement to other training procedures for use with adaptive task allocation specifically and intra-personal attention management generally.