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Physiological indices of workload in a simulated flight task
BIOLOGICAL PSYCHOLOGY ELSEVIER
Physiological
Biological Psychology 42 (1996) 323-342
indices of workload flight task J.A. Veltman*,
TN0
in a simulated
A.W.K. Gaillard
Human Factors Research Institute, PO Box 23. 3769 ZG Soesterberg,
The Netherlands
Abstract The sensitivity of physiological measures to evaluate workload was investigated in a simulated flight task. Heart rate, blood pressure (from beat to beat), respiration and eye blinks were recorded in 14 subjects while they performed a complex task in a flight simulator. Workload was manipulated by introducing an additional task and by varying the task difficulty of segments of the flight scenarios. Heart rate and blood pressure were both affected by the different levels of task difficulty. Heart-rate variability was found to be confounded by respiration. Slow respiratory activity contributed considerably to heart rate variability, especially after periods of high workload (for example, after landing). The gain between blood-pressure and heart-rate variability (modulus) was sensitive to mental effort and was not influences by respiration. Eye blinks, in particular the duration, were specifically affected by the visual demands of the task and not by the workload in general. When subjects had to process visual information, the number and duration of blinks decreased. Keywords:
Mental workload; Heart rate; Blood pressure; Respiration;
Spectral analysis
1. Introduction Although the concept of mental workload is used often in human factors research, there is still no adequate definition. Mental workload is mostly defined as the ratio between task demands and the capacity of the operator (Kantowitz, 1988; O’Donnell & Eggemeier, 1986). Mental workload is high when the difference between task demands and capacity is small. The operator has at least some uncertainty as to whether he can accomplish the task successfully. To evaluate mental workload, both task demands and operator capacity have to be assessed. The analysis of the task demands provides insight * Corresponding author, E-mail: [email protected] 0301-0511/96/$15.00 @ 1996 Elsevier SSDI 0301-0511(95)05165-7
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into the causes of high workload and the measurement of capacity provides information about the mental effort that has to be invested in the task. In a complex task environment, it is difficult to determine task demands. In a cockpit, for example, pilots have many tasks. Besides the primary tasks (flying, navigation and communication), they have to plan their activities, supervise the status of the system, make priorities of subtasks and anticipate future tasks. They actively have to manage their time, energy and available resources to accomplish tasks on time and at an adequate performance level (Adams, Tenney & Pew, 1991; Hart, 1989; Rouse, Edwards & Hammer, 1993). Workload cannot be regarded as a static concept. In an attempt to cope with the task demands, operators adapt by changing their strategies, delaying low priority tasks, and by lowering performance criteria. Management strategies are dependent on the level of workload. Strategies are characterized by leading (or lagging behind) events or simply reacting to them (Hart, 1989). The more complex a task becomes, the more management activities an operator has, which require attention and therefore increase the mental workload. Because these activities are difficult to specify, it is hard to evaluate their effect on the total workload. This implies that in a complex work environment it is difficult to make an estimate of the workload based on an analysis of the task characteristics. Techniques for measuring mental workload can be divided into performance, subjective ratings and physiological measures. In a complex task environment performance measures often cannot index workload, particularly for each subtask. There are also no generally agreed procedures to combine scores on different aspects of the task into one score that reflects total performance. Furthermore, it is often difficult to know which task is critical at a particular moment in time. Another complicating factor is that operators will try to keep their performance at an acceptable level. Operators adapt to increasing task demands by exerting additional effort to maintain a constant level of performance. The level of performance, therefore, only provides valuable information when techniques are used to index the invested effort. Rating scales generally provide a good indication of the total workload. In many instances, however, operators may not have sufficient time to fill out a rating scale while they are working. Moreover, operators appear not to be able to discriminate between the demands of the task and the effort invested in task performance. In other words, on the basis of rating scales it is not clear whether an operator works hard or thinks that he has to work hard. Under high workload operators have to invest more mental effort in order to maintain an adequate level of performance (Gaillard & Wientjes, 1994; Hockey, 1986). Such effort results in a decrease in parasympathetic and an increase in sympathetic activity (Gawron, Schiflett & Miller, 1989; Mulder & Mulder, 1987) which results in peripheral reactions in heart rate, respiration, and blood pressure. Since these variables are also influenced by other factors, such as physical activity, an estimation of workload should be based on several measures. The advantage of physiological measures is that they are unobtrusive and objective, and may provide continuous information about mental effort.
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In the present study heart rate (HR), HR variability (HRV), blood pressure (BP), BP variability (BPV), respiration and eye blinks are investigated. Furthermore, the relation between respiration and HRV and between BPV and HRV is explored. These techniques are used in an attempt to develop an integrative approach. Measures obtained by different techniques provide different types of information that has to be combined into one evaluation. In a previous experiment (Veltman & Gaillard, 1993), two rating scales were evaluated. The multidimensional NASA-TLX (Hart & Staveland, 1988) was found to be less sensitive than the one-dimensional RSME (Zijlstra, 1993), for an estimation of mental effort. Therefore, only the RSME was used in the present experiment. HR and HRV are assumed to be influenced by both sympathetic and parasympathetic activity of the autonomic nervous system. An increase in workload results in an increase of HR, whereas HRV decreases (Mulder, 1988). An index for HRV can be obtained by spectral analysis. The frequency spectrum is mostly divided into three bands: low band (0.02-0.06 Hz), midband (0.07-0.14 Hz), and high band (0.15-0.50 Hz). Respiration causes a change in HR; during inspiration the HR increases whereas the HR decreases during expiration. These respiration related HR changes are called respiratory sinus arrhythmia (RSA). Since the respiration frequency is mostly within the high band, the spectral energy within this band is called RSA. The mid-band is related to BP regulation. BP is controlled by many different short- and long-term mechanisms. One of these control mechanism causes a resonance in the veins with a frequency of about 0.10 Hz, which causes changes in BP with the same frequency (Hyndman, 1974). Via the short-term regulation mechanism these BP changes are followed by changes in HR in the opposite direction to stabilize the BP. Mental effort is assumed to reduce the sensitivity of this regulation mechanism (Mulder, 1980; Veldman, 1992). The amplitude of the mid-band is reduced because the HRV is less determined by changes in BP. A more direct indication of this reduced sensitivity can be obtained by the gain between BPV and HRV (Mulder, 1988; Steptoe, Fieldman & Evans, 1993). If the BP changes are less reflected by changes in HR, then the gain (modulus) between the two measures is decreased. If for instance, the modulus between systolic BP and inter-beat interval (IBI) is 10 ms/mmHg, than a change in BP of 1 mmHg corresponds to a lo-ms change in IBI. The high band is influenced by parasympathetic activity only, whereas the mid-band is influenced by both sympathetic and parasympathetic activity (Akselrod, Gordeon, Madwew, Snidman, Shannon & Cohen, 1985). Changes in the mean level of systolic and diastolic BP are mainly caused by sympathetic activity. An increase in sympathetic activity causes an increase in BP. The duration and the number of eye blinks are expected to decrease when the visual demands of the task increase (Bauer, Goldstein & Stern, 1987; Fogarty & Stern, 1989). Subjects adopt this strategy to reduce the time that no information can be obtained. In the present study physiological measures were obtained while subjects performed a flight task in a simulator. To manipulate workload subjects had to
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follow another jet with four fixed distances in four separate scenarios. In addition, a secondary task had to be performed several times. The scenarios ended with a landing. The aim of the present study is to validate physiological workload measures and to investigate the relation among the cardiovascular measures. 2. Method 2.1. Subjects Sixteen subjects participated in the experiment. Two were excluded from the analysis; one had cold fingers which produced artifacts in the continuous monitoring of BP and the other was not able to follow the target jet adequately. The mean age of the remaining fourteen subjects (thirteen males and one female) was 22 years (range 20-30). Twelve subjects (including the female) were students of the Royal Netherlands Military Academy. The students had been selected to become F-16 fighter pilots (n = 3) fighter controllers (n = 6) or air traffic controllers (n = 3). One subject was an employee of the Royal Netherlands Military Academy with a flight licence and one was an employee of the institute who had ample experience with the simulator. 2.2. Simulator The task was presented in a spherical dome (radius 3 m, projected image of 156” horizontal and 42” vertical). The projected data base (desert environment) was generated with a graphics system with three parallel channels (Evans & Sutherland, ESIG-2000). A target jet (F-18) flew over the data base. The subjects sat in a mock-up of a cockpit with a force stick on the right and a throttle on the left side. On the stick was a button for the secondary task (see Section 2.4). A display with a synthetic picture of the world (60” vertical and 45” horizontal), generated by an IRIS graphical computer, was positioned in front of the subject. The target jet was presented with a half-circular symbol. The position of the target jet was also presented on the display with a radar that could be used to find the target jet when it was not visible on the dome or display. System information (airspeed, altitude, heading and rate of descent) was presented over the synthetic picture of the world. 2.3. Primary task
Subjects had to follow the target jet at a fixed distance. The scenarios started at the runway were the piloted jet was positioned behind the target jet. After take off, the target jet flew a scenario that returned the aircraft to the same runway. The target jet made turns that were at most 5” min-’ and the changes
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in altitude were almost negligible. The speed of the target jet was constant (500 knots). The scenario had to be flown four times with different following distances (750, 1100, 1700 and 2500 ft). The distance to the target jet was depicted on the tactical display by means of an arrow that moved along a vertical indicator. The centre of the indicator corresponded to the required distance. The scenarios ended with a landing. The start of the approach was signalled by a highway in the sky that appeared on the tactical display. It presented the optimal flight path for the landing. To motivate the subjects a landing score was provided immediately after touchdown. 2.4. Secondary task An auditory continuous memory task (CMT), consisting of series of spoken letters of the Dutch alphabet was presented through headphones. Each time the subject detected a target letter a button had to be pressed, that was positioned on the stick near the thumb. There were four target letters (A, B, Y and Z). The letters had also to be counted in separate tallies. The button had to be pressed twice when a target letter was presented for the third time. If this response was correct, the subject heard the word ‘correct’. When the subject pressed the button at an incorrect moment or when an omission was made, the word ‘wrong’ was presented in the headphone. After the feedback (‘correct’ or ‘wrong’) the tally for the last letter had to be set to zero. The letters D, H, P and W were not used because the sound of these letters could be confused with the target letters. Target letters were never presented in succession. Forty percent of the letters were targets. The CMT was presented half way into the scenario for a duration of 4 min. 2.5. Procedure Subjects participated for two days in the experiment. There were always two subjects at a time. The first day was a training day and the experiment took place on the second day. The first part of the training day was used to train the flight and landing. The scenarios were trained in the afternoon. Each follow distance was flown twice, the second time with the CMT. The second day started with a 30-min training period. The four scenarios were flown in succession in the experiment, starting and ending with a rest period of about 15 min during which the subjects sat quietly in the simulator. The order in which the subjects flew the scenarios was balanced across subjects. 2.6. Rating scale After touchdown, subjects were asked to rate their effort on a scale that appeared on the display (RSME; Zijlstra, 1993). The RSME ranges from 0 to 150 and has also nine descriptive indicators along the axis (e.g., value 2
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corresponds to ‘not effortful’, 58 to ‘rather effortful’, 113 to ‘awfully effortful’). Subjects indicated their rating by moving an arrow along a vertical axis with an optical mouse. The scale disappeared when a key of the mouse was pressed. 2.7. Physiological measures The electrocardiogram (ECG), respiration, blood pressure and the electrooculogram (EOG) were digitally recorded with the CODAS system (DATAQ Instruments, Inc.). The sample frequency of all channels was 100 Hz. Pre-processing of the signals was also done with the CODAS software. Spectral analysis and the calculation of mean values were done by Carspan (Mulder, 1988). 2.7.1. ECG
The times of the R-peaks from the ECG were automatically detected. Omissions were corrected manually after visual inspection of the data. The inter-beat interval (IBI) is calculated from these R-peak times. 2.7.2. Blood pressure
BP was recorded continuously by means of the TN0 Finapress tonometer that measures both systolic and diastolic BP from beat to beat. Subjects could decide whether the cuff was placed around the middle or ring finger. They were instructed not to bend this finger. Systolic and diastolic values of the blood pressure were detected automatically. Omissions were corrected manually. Values were excluded when the apparatus was being calibrated or when the signal was disturbed. Most artifacts were caused by bending of the finger. The missing blood pressure values were replaced by values estimated by the Carspan program. 2.7.3. Respiration
Respiration was measured by means of inductive plethysmography (Respitrace, Inc.). The expansion of the chest and abdomen was measured with two elastic belts. Due to noise in one of the respiratory channels, the respiratory volume could not be calculated in this experiment. The best signal for each subject was used for further analysis. From this signal, thirty sample points (0.3 s) around each R-peak were averaged and used for spectral analysis. Spectral analysis and the calculation of mean values were done within 40-s periods. The periods were shifted 10 s from the previous one (overlap of 30 s). In this way, the output of the Carspan program contained values for each 10 s that had 75% overlap with the adjacent values. Mean values were calculated for IBI, systolic and diastolic BP. Spectral analysis was done for heart rate (HR), systolic BP, diastolic BP and respiration. Since the relation between IBI and HR is not linear, an HR spectrum does not reveal the same results as an IBI spectrum. When IBI increases for example, the amplitude of the IBI spectrum also increases, whereas the amplitude from the HR spectrum
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calculated from the corresponding HR values will increase less or even decrease. Thus, when spectral values of a condition with a low mean IBI (e.g., during a task) are compared with a condition with a high mean IBI (e.g., rest), IBI and HR spectra will reveal different results. Because mental effort affects both the mean level and the variability, an IBI spectrum will overestimate the effects of effort upon the variability while an HR spectrum will underestimate the effect. When the spectral values are divided by the squared mean value of HR or IBI then the spectral values are no longer statistically dependant upon the mean HR or IBI (van Dellen, Aasman, Mulder & Mulder, 1983). After this correction of the mean values, an HR spectrum reveals the same results as an IBI spectrum. In the present experiment, the spectral values of HR, BP and respiration are divided by the squared mean levels (of HR, BP or respiration) within the 40-s windows. The spectral values have no dimension after the division. According to Mulder (1988) they are labelled (modulation index)2/ Hz. Spectral values were calculated for the mid-band (0.07-0.14 Hz) and high band (0.15-0.50 Hz). Coherence and modulus were calculated between systolic BP and IBI and between IBI and respiration. The coherence expresses the resemblance of two channels in the frequency domain and is comparable to a squared correlation in the time domain. The modulus is the gain between the two channels in the frequency domain; this is the spectral energy of the IBI spectrum divided by the spectral energy of the systolic BP spectrum. Because a modulus is only relevant when two channels have a high resemblance, the average modulus within a spectral band is based only on frequencies in which the coherence is greater than 0.50.
2.7.4. Eye blinks Eye blinks were derived from the EOG measured with electrodes above and next to the left eye. The signals from these electrodes are differentially recorded with respect to an electrode placed on the forehead. The EOG was recorded with an AC coupled amplifier (r = 3 s). The derivative of the EOG signal was calculated and used for the detection of blinks (see Fig. 1). A blink was defined by a peak in the differential signal that followed a valley after 0.06-0.20 s. To distinguish blinks from noise in the signal, the amplitude of the peaks and the valleys should be at least 10% of the maximum amplitude in the signal. This maximum was established separately for each subject. The duration of a blink is defined as the time between the valley and the peak in the differential signal. The algorithm for the detection of blinks and calculation of the duration deviates from conventional algorithms in which a blink is defined by a decrease in amplitude and the duration by the time between the halfway amplitude of blink onset and offset (e.g., Goldstein, Bauer & Stern, 1992; Wilson, Purvis, Skelly, Fullenkamp & Davis, 1987). The present algorithm is much simpler and easier to automate. The result for normal (short lasting)
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/
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time(s) Fig. 1. Example of the EOG signal and its derivative. A blink is defined by a valley in the derivative followed by a peak within 0.06-0.2 s. The duration of the blink is the time between the valley and the peak.
blinks is almost the same because the halfway amplitude point is generally the point were the slope is maximal. 2.8. Performance The distance to the target jet was continuously recorded. Mean deviations from the required distance and S.D. were calculated for three 60-s intervals the last minute before the CMT, the last minute of the CMT, and 2-3 min after the CMT. For each scenario the mean reaction time and the percentage correct responses to the third target letter were calculated. 2.9. Analysis
Physiological measures were obtained for the following six segments: rest period before the flight (15 min), flight (before and after the CMT, about 8 min), flight and CMT (4 min), landing (about 2 min), after landing (about 2 min), and rest period after the flight (about 15 min). Three ANOVAs were carried out utilizing a repeated measures design. The first analysis had two factors: required distance (750, 1100,170O and 2500 ft) and two flight segments (flight, and flight and CMT). The factor distance revealed no significant main effects, nor a significant interaction for any of the dependent measures. A second analysis was carried out, pooled over the four required distances, in
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which differences between the rest periods and task periods were examined. This analysis had one factor with six levels (all segments). The differences between the three flight segments (flight, flight and CMT, and landing) were examined in the third analysis. Differences between the segments were tested with a post-hoc test (Tukey HSD). Since the first analysis revealed no significant results for the physiological measures, only the results of the second and third analysis are reported. 3. Results 3.1. Performance The distance to the target jet was recorded continuously during the flight. Mean deviations from the required distance and SD. were calculated for each segment. The results are presented in Table 1. The mean and S.D. did not change as a function of required distance. Both mean and S.D. changed significantly during the flight (F(2,26) = 7.84, p < 0.01; F(2,26) = 15.07, p < 0.01, respectively). Post-hoc analysis showed that the mean and S.D. were significantly larger during the last minute of CMT performance than in the other two segments. During the performance of the CMT, distance to the target jet was larger and more variable. The mean reaction time to target letters of the CMT was 776 ms and the percentage correct responses to targets that appeared for the third time was 79%. 3.2. Subjective ratings Subjects rated their effort after each scenario. No differences between the four required distances (mean value 69).
were found
3.3. Physiological measures Table 2 summarizes the differences of the second statistical analysis. The table gives the results of the post-hoc analysis between rest 1, rest 2, after landing and the three flight segments. Table 3 summarizes the differences between the three flight segments. A description of the effects is given below.
Table 1 Mean follow distances and S.D. during the flight (last minute before the CMT, the last minute of the CMT, and third minute after the CMT)
Last minute before CMT Last minute of CMT Third minute after CMT
Mean (ft)
S.D. (ft)
150 1359 190
250 840 370
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Table 2 Comparisons (Tukey three flight segments
Rest I IBI Systolic BP Diastolic BP HR mid-band HR high band Systolic mid-band Systolic high band Diastolic mid-band Diastolic high band Respiration mid-band Respiration high band Modulus Blink duration Blink interval time
***
Rest 2 IBI Systolic BP Diastolic BP HR mid-band HR high band Systolic mid-band Systolic high band Diastolic mid-band Diastolic high band Respiration mid-band Respiration high band Modulus Blink duration Blink interval time After landing IBI Systolic BP Diastolic BP HR mid-band HR high band Systolic mid-band Systolic high band Diastolic mid-band Diastolic high band Respiration mid-band Respiration high band Modulus Blink duration Blink interval time **
p <
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Flight and CMT
Landing
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After landing
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* *** ** *** c** *** *** *** **
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Rest 2
J.A. Veltman, A.W.K. Gaillard I Biological Psychology 42 (1996) 323-342 Table 3 Comparisons
(Tukey
IBI Systolic BP Diastolic BP HR mid-band HR high band Systolic mid-band Systolic high band Diastolic mid-band Diastolic high band Respiration mid-band Respiration high band Modulus Blink duration Blink interval
HSD
tests) between
the three
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Landing/flight
CMTiflight
GMT/landing
*** *** *** _ _ _ _ _ _ _ _ * ** ***
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_ _ _ _ _ _ _ ** *
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* p < 0.05; ** p < 0.01; *** p < 0.001; -, n.s.
3.3.1. IBI
Results are presented in Fig. 2. The IBI was affected by the segments (F(5,65) = 18.51, p < 0.01). The IBI in both rest segments was larger than in the flight and CMT, landing and the segment after the landing. The IBI in the three flight segments differed significantly (F(2,26) = 17.14, p ~0.01) the IBI in the flight and CMT, and landing was smaller than in the flight alone. 3.3.2. HRV The energy in the mid- and high band of the frequency spectrum differed between the segments (F(5,65) = 10.01, p < 0.01; F(5,65) = 5.11, p < 0.01, respectively). Post-hoc analysis showed that the energy in the mid-band after landing was larger than in all other segments. The energy in the high band after landing is larger than in the three flight segments. 3.3.3. Blood pressure Results are presented in Fig. 3. Both systolic and diastolic BP differed between the segments (F(5,65) = 9.76, p < 0.01; F(5,65) = 12.31, p < 0.01). Systolic and diastolic BP in the first rest segment was lower than in the flight and CMT, and landing. The systolic and diastolic BP in the second rest segment was lower than in the three flight segments. Systolic BP in the second rest segment was also lower than in the segment after landing. Both systolic and diastolic BP during landing were higher than in the segment immediately after landing. Systolic and diastolic BP in the flight segments differed significantly (F(2,26) = 17.47, p < 0.01; F(2,26) = 21.28, p < 0.01, respectively). The pressures during flight were lower than in the flight and CMT, and landing.
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Fig. 2. Mean inter-beat interval (IBI) and HRV in the mid- and high band for each segment.
3.3.4. BPV
The energy in the mid- and high band of the spectrum is given in Fig. 3. Both systolic and diastolic BPV differed significantly between the segments (F(5,65) = 7.25, p < 0.01; F(5,65) = 9.48, p < 0.01, respectively). The energy in the high bands also differed significantly between the segments (F(5,65) = 10.46, p < 0.01; F(5,65) = 17.29, p < 0.01, respectively). As was the case with HRV, both systolic and diastolic variability (in both spectral bands) after landing was larger than in the other segments. Only the mid-band of the diastolic BP differed significantly between the segments (F(2,26) = 8.13, p <
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BP
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high-band
diastolic
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flight & CMT
landing
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rest
Fig. 3. Systolic and diastolic BP and the variability of both systolic and diastolic BP in the mid- and high band for each segment.
0.01). Post-hoc analysis showed that the variability in the flight and CMT segment was lower than in the flight or in the landing segments. 3.3.5. Respiration The spectral energy in the mid- and high band for the respiratory signal is presented in Fig. 4. The energy in both the mid- and high band was different between segments (F(5,65) = 16.79, p < 0.01; F(5,65) = 4.34, p < 0.01, respectively). The energy after landing in both the mid- and high band was larger
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respiration
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Fig. 4. Spectral
energy
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flight & landing CMT
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after landing
rest 2
in the mid- and high band for each segment
than in all other segments. The increase in respiratory amplitude after landing was most pronounced for the mid-band. Subjects tended to breathe deeper and more slowly just after landing. There were no differences between the other segments. Fig. 5 presents the results of two subjects to illustrate the relation between respiratory activity and HRV in the mid-band. The first subject frequently demonstrated respiratory activity in the mid-band (corresponding to breathing cycles of between 7 and 14 s) in all segments. The second subject had respiratory activity after three of the four landings (touchdown). The respiratory activity always co-occurred with large HRV This figure clearly shows that respiration can seriously confound HRV. The coherence values between respiration and IBI for each segment are presented in Table 4. The largest values were found for the high band, because respiratory frequencies were most prominent in this range. The modulus between systolic BP and IBI in the mid-band are presented in Fig. 6. The modulus expresses the change in IBI per mmHg change in systolic blood pressure. The modulus differed significantly between the segments (F(5,65) = 6.05, p < 0.01). The modulus in both rest segments was larger than in the flight and CMT, landing and the segment after landing. The modulus during landing was lower than during flight.
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time (min) Fig. 5. The data of two subjects, illustrating the relation between respiratory activity and HRV in the mid-band. Rest 1 is followed by the four scenarios and ends with rest 2. Table 4 Coherence values between respiration and IBI for each segment
Mid-band High band
Rest 1
Flight
Flight and CMT
Landing
After landine
Rest 2
0.39 0.65
0.34 0.67
0.32 0.65
0.33 0.68
0.38 0.53
0.42 0.60
3.3.6. Eye blinks
The duration and interval times of the eye blinks differed significantly between the segments (F(5,65) = 61.86, p < 0.01; F(5,65) = 13.75, p < 0.01. respectively) (see Fig. 7). The average duration in both rest segments was
15
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5 rest
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rest 2
Fig. 6. Modulus between systolic BP and IBI in the mid-band for each segment.
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blinks
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z130 2 E ‘E 120 P 2 Y 110 c .3 100 rest 1
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fl$$&
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Fig. 7. Interval between blinks and duration of eye blinks for each segment.
longer than in all other segments. The average iaterblink interval in the rest segments was shorter than in the landing. Both the duration and interval time differed significantly between the flight segments (F(2,26) = 9.99, p < 0.01; F(2,26) = 18.11, p < 0.01, respectively). During landing the interval time was longer and the duration shorter compared to the other flight segments.
4. Discussion 4.1. Performance
When subjects are sufficiently motivated, they will invest more effort with increasing task demands. Although this was not tested explicitly, we have good reason to assume that our subjects were well motivated. Most subjects asked to do the task another time at the end of the experiment because they found the task very interesting. Furthermore, the performance was observed by a second subject who was a colleague. Performance decrement during the CMT seems not to be caused by a lack of motivation to exert additional effort. The task
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capacity of the
4.2. Physiological measures IBI and BP were sensitive to the different levels of task load. IBI was shorter, and BP was higher during the flight task, as compared to rest. When the CMT was added to the flight task, IBI reduced and BP increased further. Although the absolute differences in systolic BP were larger, the effects of diastolic BP were more stable. HRV in the mid- and the high band was not sensitive to the different levels of task load in the present experiment. Significant effects were only found between the segment ‘after landing’ and the other segments, with the strongest effects in the mid-band. The spectral energy was increased after landing, to a level that was even higher than during rest. In several laboratory studies HRV has been found to be a sensitive index for mental effort. In cognitive reaction-time tasks similar to the CMT used in the present experiment, HRV decreases as the memory load increases. The HRV decreases further as subjects in addition have to count the target letters (Aasman, Mulder & Mulder, 1987; Veltman, 1989). In field studies, HRV has been found to be less sensitive. Only differences between rest and task are reported (Jorna, 1992; Wilson, 1992). BPV was not sensitive to the different levels of task load. Like HRV, BPV increased only after landing. The same pattern of results has been found for the spectral energy of respiration, for both mid- and high band. After the landing the respiratory activity increased, especially in the mid-band. The modulus between systolic BP and IBI decreased during flight and decreased further during landing as compared to rest. After landing, the modulus was not increased, as was the case with HRV and BPV The reduced modulus, which indicates a reduced baroreceptor sensitivity, suggests that mental effort influences the central control of cardiovascular functions, rather than a general effect on peripheral physiological activation (Steptoe et al., 1993). Blink duration was decreased during flight and reduced further during landing. The addition of the CMT to the flight task did not affect blink duration. Blink interval was increased only during landing. The CMT does not impose additional visual demands. However, during landing the visual demands are very high because subjects constantly have to check the air speed, the roll, the position of the runway, and the vertical speed. These results, which have been observed in other studies (e.g. Goldstein et al., 1992; Stern, Boyer & Schroeder, 1994; Wilson et al., 1987) show that eye blinks are sensitive to visual load but not to cognitive load. When operators have to process a lot of visual information, they tend to blink less frequently and when they blink, it is shorter, because otherwise information may be missed. It seems that blink duration and blink interval provide valuable information and may be used as an index for variations in visual load.
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4.3. influence o,f respiration upon HRV The changes in BP and IBI are assumed to indicate that subjects spend more effort during the more difficult segments of the task. It is surprising that no changes in HRV were found, since HRV is assumed to be an index of mental effort (Mulder, 1986; Mulder & Mulder, 1987). The insensitivity of HRV appears to be caused by the intrusion of respiratory activity. Angelone and Coulter (1964) demonstrated that HRV is affected by respiratory frequency. To examine this relation, they asked subjects to breathe at different frequencies. HRV increased when respiratory frequency decreased. The effect was largest at a respiratory frequency of 0.10 Hz (the mid-band in the present study). Even within the high-band region, changes in respiratory frequency may have considerable effects on HRV (Grossman, 1992). These respiratory related changes in HR called respiratory sinus arrhythmia (RSA) are believed to reflect parasympathetic activity. Grossman (1992) warned against a straightforward use of RSA as an index of parasympathetic activity. RSA reflects only parasympathetic activity when corrected for changes in respiratory frequency and amplitude. Respiration can provide valuable information about mental effort. Wientjes (1992) demonstrated that the subjects change their respiratory pattern when the task (similar to the CMT) becomes more difficult. The tidal volume and the inspiratory flow increases. However, respiration can influence the HRV in a direction opposite to the effects of mental effort, as was the case in the present study. Subjects breathed slowly and deeply as was evident from the increased energy in both the mid- and high band of the respiration. High respiratory activity in the mid-band also occurred during other segments, as is shown by the time plots in Fig. 5. Changes in respiration corresponded with an increased HRV and BPV, in particular in the mid-band. The individual data show that some subjects have higher respiratory activity during rest and others during task segments. We have considered several ways to correct the HRV for respiratory effects. One possibility is to compute the modulus between respiration and HR. The modulus however, can only be used when there is a high coherence between the two signals. In the present study the coherence (computed for each time window) was generally very low. This does not mean however, that the HRV was not affected by respiratory activity. The HRV can be affected when there is only one respiratory cycle inside the mid-band. The coherence is based on the average resemblance in the time window and is relatively insensitive to abrupt changes. Another problem with this correction is that respiratory changes outside the mid-band may also effect the mid-band of HRV. Any effect on the BP regulation system will result in changes in the amplitude of the resonance frequency of the system. For example, a sudden increase in respiratory volume, will increase the HRV in the mid-band, even when the frequency of the respiratory cycle is outside this band. Thus, the paradox with the mid-band is that it is the most sensitive band to mental effort but it is also sensitive to other systems, like respiration. When HRV is used as a measure of mental effort, it is
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important to measure respiration too, because it gives more insight into the factors that determine HRV changes. Another possibile way to correct for respiratory activity is to compute the modulus between systolic BP and IBI in the mid-band (HRV divided by BPV). This modulus was decreased during flight and reduced further during landing. The modulus did not increase after landing, as was the case with HRV and BPV. The modulus is not affected by respiration, because respiration affects both HRV and BPV The modulus will only be affected by respiration, when the influence upon HRV is different from that upon BPV, or when the influence varies over experimental conditions. However, in both cases the effect of respiration upon the modulus will be far less than the effect upon HRV. Thus, the modulus is a better measure of mental effort than HRV A disadvantage of using the modulus instead of HRV is that BP should be measured from beat to beat. The system (Finapres) we have used measures the BP from the finger, which is not practical when subjects have to use both hands intensively. The present study demonstrates that mental workload can be evaluated by a combination of physiological measures. In a complex task environment, respiratory activity is likely to disturb HRV, especially in the mid-band of the frequency spectrum. The modulus between systolic BP and IBI is hardly influenced by respiration and is therefore a better measure than HRV BP provides information about the increase of sympathetic activity. The modulus between BP and IBI provides information about sympathetic and parasympathetic branches of the autonomic nervous system. Eye blinks can provide information about the visual information load independent of the total workload. References Aasman, J., Mulder, G.. & Mulder, L.J.M. (1987). Operator effort and the measurement of heart rate variability. Human Factors, 29, 161-170. Adams, M.J., Tenney, Y.J., & Pew, R.W. (1991). Strategic workload and the cognitive management of advanced multi-task systems. Report, SOAR, CSERIAC 91-96. Akselrod, S., Gordeon, D., Madwew, J.B., Snidman, N.C., Shannon, C., & Cohen, A. (1985). Hemodynamic regulation: investigation by spectral analysis. American Journal of Physiology, 249, H867-H875. Angelone. A., & Coulter, N.A. (1964). Respiratory-sinus arrhytmia: a frequency dependent phenomenon. Journal of Applied Physiology, 19, 479-482. Bauer, L.O.. Goldstein. R., & Stern, J.A. (1987). Effects of information-processing demands on physiological response patterns. Human Factors, 29, 213-234. van Dellen, H.J., Aasman, J., Mulder, L.J.M., & Mulder, G. (1983). Time domain versus frequency domain measures of heart rate. In J.F. Orlebeke, G. Mulder, & L.J.P van Doornen (Eds.). Psychophysiology of cardiovascular control: models, methods and data. (pp. 353-374) New York: Plenum Press. Fogarty, C.. & Stern, J.A. (1989). Eye movements and blinks: their relationship to higher cognitive processes. International Journal of Psychophysiology, 8, 35-42. Gaillard, A.W.K., & Wientjes, C.J.E. ( 1994). Mental load and work stress as two types of energy mobilization. Work & Stress, 8, 141-152. Gawron, VJ., Schiflett, S.G., & Miller, J.C. (1989). Measures of in-flight workload. In R.S. Jensen (Ed.). Aviafion psychology (pp. 240-287). Aldershot: Brooktield.
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Goldstein, R., Bauer, L.O., & Stern, J.A. (1992). Effect of task difficulty and interstimulus interval on blink parameters. International Journal of Psychophysiology, 13, 111-117. Grossman, P. (1992). Respiratory and cardiac rhythms as windows to central and autonomic biobehavioral regulation: selecting of window frames, keeping the panes clean and viewing the neural topography. Biological Psychology, 34(2/_?). 131-161. Hart, S.G. (1989). Crew workload-management strategies: a critical factor in system performance. Proceedings of the Fifth International Symposion on Aviation Psychology (pp. 22-27). Columbus, OH. Hart, S.G., & Staveland, L.E. (1988). Developement of NASA-TLX (task load index): results of empirical and theoretical research. In P.A. Hancock, & N. Meshkati (Eds.). Human mentat workload (pp. 139-184). Amsterdam: Elsevier. Hockey, G.R.J. (1986). Changes in operator efficiency as a function of environmental stress, fatigue, and circadian rhythms. In K.R. Boff, L. Kaufman, & J.P. Thomas (Eds.). Handbook of perception and human performance. (vol. 2, pp. 44.1-44.49). New York: John Wiley. Hyndman, B.W. (1974). The role of rhythms in homeostasis. Kybernetik 15, 227-236. Jorna, P.G.A.M. (1992). Spectral analysis of heart rate and psychological state: a review of its validity as a workload index. Biological Psychology, 34(.?/3). 237-258. Kantowitz, B.H. (1988). Mental workload. In P.A. Hancock (Ed.). Human factors psychology. Amsterdam: Elsevier. Mulder, G. (1980). The heart of mental effort. Thesis. Groningen: University of Groningen. Mulder, G. (1986). The concept and measurement of mental effort. In G.R.J. Hockey, A.W.K. Gaillard, & M.G.H. Coles (Eds.). Energetics and human information processing. Dordrecht: Nijhoff. Mulder, L.J.M. (1988). Assessment of cardiovascular reactivity by means of spectral analysis. Thesis. Groningen: University of Groningen. Mulder, L.J.M., & Mulder, G. (1987). Cardiovascular reactivity and mental workload. In R.I. Kitney, & 0. Rompelman (Eds.). The beat-by-beat investigation of cardiovascular function (pp. 216-253). Oxford: Clarendon Press. O’Donnell. R.D., & Eggemeier, F.T. (1986). Workload assessment methodology. In K. Boff, L. Kaufman, & J.P. Thomas (Eds.). Handbook of perception and human performance (pp. 42.144.49). New York: Wiley. Rouse, W.B., Edwards, S.L., & Hammer, J.M. (1993). Modeling the dynamics of mental workload and human performance in complex systems. /EEE Transactions on Systems, Man and Cybernetics, 23. 1662-1671. Steptoe, A., Fieldman, G., & Evans, 0. (1993). An experimental study of the effects of control over work pace on cardiovascular responsivity. Journal of Psychophysiology, 7, 290-300. Stern, J.A., Boyer, D. & Schroeder, D. (1994). Blink rate: a possible measure of “fatigue”. Human Factors, 36(Z), p. 285-297. Veldman, J.P.B. (1992). Hidden effects of noise as revealed by cardiovascular analysis. Thesis. Groningen: University of Groningen. Veltman. J.A. (1989). Cardiovasculaire mechanismen en ERP’s als indikatoren van mentale inspanning. Heymans bulletin, No. HB-89-977 SW. Groningen: University of Groningen. Veltman, J.A., & Gaillard, A.W.K. (1993). Measurement of pilot workload with subjective and physiological techniques. Proceedings of the Workload Assessment and Aviation Safety Conference, Royal Aeronautical Society, April 27-28. Wientjes, C.J.E. (1992). Respiration in psychophysiology: methods and applications. Biological Psychology, 34(2/3). 179-204. Wilson, G.F. (1992). Applied use of cardiac and respiratory measures: practical considerations and precautions. Biological Psychology. 34, 163-178. Wilson, G.F., Purvis, B., Skelly, J., Fullenkamp, P., & Davis, I. (1987). Physiological data used to measure pilot workload in actual flight and simulatot conditions. Proceedings of the 31th Annual Meeting of the Human Factors Society (pp. 779-783). Zijlstra, F.R.H. (1993). Effi clenc y in work behaviour: a design approach for modern tools. Thesis. Delft: Technical University of Delft.
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indices of workload flight task J.A. Veltman*,
TN0
in a simulated
A.W.K. Gaillard
Human Factors Research Institute, PO Box 23. 3769 ZG Soesterberg,
The Netherlands
Abstract The sensitivity of physiological measures to evaluate workload was investigated in a simulated flight task. Heart rate, blood pressure (from beat to beat), respiration and eye blinks were recorded in 14 subjects while they performed a complex task in a flight simulator. Workload was manipulated by introducing an additional task and by varying the task difficulty of segments of the flight scenarios. Heart rate and blood pressure were both affected by the different levels of task difficulty. Heart-rate variability was found to be confounded by respiration. Slow respiratory activity contributed considerably to heart rate variability, especially after periods of high workload (for example, after landing). The gain between blood-pressure and heart-rate variability (modulus) was sensitive to mental effort and was not influences by respiration. Eye blinks, in particular the duration, were specifically affected by the visual demands of the task and not by the workload in general. When subjects had to process visual information, the number and duration of blinks decreased. Keywords:
Mental workload; Heart rate; Blood pressure; Respiration;
Spectral analysis
1. Introduction Although the concept of mental workload is used often in human factors research, there is still no adequate definition. Mental workload is mostly defined as the ratio between task demands and the capacity of the operator (Kantowitz, 1988; O’Donnell & Eggemeier, 1986). Mental workload is high when the difference between task demands and capacity is small. The operator has at least some uncertainty as to whether he can accomplish the task successfully. To evaluate mental workload, both task demands and operator capacity have to be assessed. The analysis of the task demands provides insight * Corresponding author, E-mail: [email protected] 0301-0511/96/$15.00 @ 1996 Elsevier SSDI 0301-0511(95)05165-7
Science B.V All rights reserved
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into the causes of high workload and the measurement of capacity provides information about the mental effort that has to be invested in the task. In a complex task environment, it is difficult to determine task demands. In a cockpit, for example, pilots have many tasks. Besides the primary tasks (flying, navigation and communication), they have to plan their activities, supervise the status of the system, make priorities of subtasks and anticipate future tasks. They actively have to manage their time, energy and available resources to accomplish tasks on time and at an adequate performance level (Adams, Tenney & Pew, 1991; Hart, 1989; Rouse, Edwards & Hammer, 1993). Workload cannot be regarded as a static concept. In an attempt to cope with the task demands, operators adapt by changing their strategies, delaying low priority tasks, and by lowering performance criteria. Management strategies are dependent on the level of workload. Strategies are characterized by leading (or lagging behind) events or simply reacting to them (Hart, 1989). The more complex a task becomes, the more management activities an operator has, which require attention and therefore increase the mental workload. Because these activities are difficult to specify, it is hard to evaluate their effect on the total workload. This implies that in a complex work environment it is difficult to make an estimate of the workload based on an analysis of the task characteristics. Techniques for measuring mental workload can be divided into performance, subjective ratings and physiological measures. In a complex task environment performance measures often cannot index workload, particularly for each subtask. There are also no generally agreed procedures to combine scores on different aspects of the task into one score that reflects total performance. Furthermore, it is often difficult to know which task is critical at a particular moment in time. Another complicating factor is that operators will try to keep their performance at an acceptable level. Operators adapt to increasing task demands by exerting additional effort to maintain a constant level of performance. The level of performance, therefore, only provides valuable information when techniques are used to index the invested effort. Rating scales generally provide a good indication of the total workload. In many instances, however, operators may not have sufficient time to fill out a rating scale while they are working. Moreover, operators appear not to be able to discriminate between the demands of the task and the effort invested in task performance. In other words, on the basis of rating scales it is not clear whether an operator works hard or thinks that he has to work hard. Under high workload operators have to invest more mental effort in order to maintain an adequate level of performance (Gaillard & Wientjes, 1994; Hockey, 1986). Such effort results in a decrease in parasympathetic and an increase in sympathetic activity (Gawron, Schiflett & Miller, 1989; Mulder & Mulder, 1987) which results in peripheral reactions in heart rate, respiration, and blood pressure. Since these variables are also influenced by other factors, such as physical activity, an estimation of workload should be based on several measures. The advantage of physiological measures is that they are unobtrusive and objective, and may provide continuous information about mental effort.
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In the present study heart rate (HR), HR variability (HRV), blood pressure (BP), BP variability (BPV), respiration and eye blinks are investigated. Furthermore, the relation between respiration and HRV and between BPV and HRV is explored. These techniques are used in an attempt to develop an integrative approach. Measures obtained by different techniques provide different types of information that has to be combined into one evaluation. In a previous experiment (Veltman & Gaillard, 1993), two rating scales were evaluated. The multidimensional NASA-TLX (Hart & Staveland, 1988) was found to be less sensitive than the one-dimensional RSME (Zijlstra, 1993), for an estimation of mental effort. Therefore, only the RSME was used in the present experiment. HR and HRV are assumed to be influenced by both sympathetic and parasympathetic activity of the autonomic nervous system. An increase in workload results in an increase of HR, whereas HRV decreases (Mulder, 1988). An index for HRV can be obtained by spectral analysis. The frequency spectrum is mostly divided into three bands: low band (0.02-0.06 Hz), midband (0.07-0.14 Hz), and high band (0.15-0.50 Hz). Respiration causes a change in HR; during inspiration the HR increases whereas the HR decreases during expiration. These respiration related HR changes are called respiratory sinus arrhythmia (RSA). Since the respiration frequency is mostly within the high band, the spectral energy within this band is called RSA. The mid-band is related to BP regulation. BP is controlled by many different short- and long-term mechanisms. One of these control mechanism causes a resonance in the veins with a frequency of about 0.10 Hz, which causes changes in BP with the same frequency (Hyndman, 1974). Via the short-term regulation mechanism these BP changes are followed by changes in HR in the opposite direction to stabilize the BP. Mental effort is assumed to reduce the sensitivity of this regulation mechanism (Mulder, 1980; Veldman, 1992). The amplitude of the mid-band is reduced because the HRV is less determined by changes in BP. A more direct indication of this reduced sensitivity can be obtained by the gain between BPV and HRV (Mulder, 1988; Steptoe, Fieldman & Evans, 1993). If the BP changes are less reflected by changes in HR, then the gain (modulus) between the two measures is decreased. If for instance, the modulus between systolic BP and inter-beat interval (IBI) is 10 ms/mmHg, than a change in BP of 1 mmHg corresponds to a lo-ms change in IBI. The high band is influenced by parasympathetic activity only, whereas the mid-band is influenced by both sympathetic and parasympathetic activity (Akselrod, Gordeon, Madwew, Snidman, Shannon & Cohen, 1985). Changes in the mean level of systolic and diastolic BP are mainly caused by sympathetic activity. An increase in sympathetic activity causes an increase in BP. The duration and the number of eye blinks are expected to decrease when the visual demands of the task increase (Bauer, Goldstein & Stern, 1987; Fogarty & Stern, 1989). Subjects adopt this strategy to reduce the time that no information can be obtained. In the present study physiological measures were obtained while subjects performed a flight task in a simulator. To manipulate workload subjects had to
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follow another jet with four fixed distances in four separate scenarios. In addition, a secondary task had to be performed several times. The scenarios ended with a landing. The aim of the present study is to validate physiological workload measures and to investigate the relation among the cardiovascular measures. 2. Method 2.1. Subjects Sixteen subjects participated in the experiment. Two were excluded from the analysis; one had cold fingers which produced artifacts in the continuous monitoring of BP and the other was not able to follow the target jet adequately. The mean age of the remaining fourteen subjects (thirteen males and one female) was 22 years (range 20-30). Twelve subjects (including the female) were students of the Royal Netherlands Military Academy. The students had been selected to become F-16 fighter pilots (n = 3) fighter controllers (n = 6) or air traffic controllers (n = 3). One subject was an employee of the Royal Netherlands Military Academy with a flight licence and one was an employee of the institute who had ample experience with the simulator. 2.2. Simulator The task was presented in a spherical dome (radius 3 m, projected image of 156” horizontal and 42” vertical). The projected data base (desert environment) was generated with a graphics system with three parallel channels (Evans & Sutherland, ESIG-2000). A target jet (F-18) flew over the data base. The subjects sat in a mock-up of a cockpit with a force stick on the right and a throttle on the left side. On the stick was a button for the secondary task (see Section 2.4). A display with a synthetic picture of the world (60” vertical and 45” horizontal), generated by an IRIS graphical computer, was positioned in front of the subject. The target jet was presented with a half-circular symbol. The position of the target jet was also presented on the display with a radar that could be used to find the target jet when it was not visible on the dome or display. System information (airspeed, altitude, heading and rate of descent) was presented over the synthetic picture of the world. 2.3. Primary task
Subjects had to follow the target jet at a fixed distance. The scenarios started at the runway were the piloted jet was positioned behind the target jet. After take off, the target jet flew a scenario that returned the aircraft to the same runway. The target jet made turns that were at most 5” min-’ and the changes
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in altitude were almost negligible. The speed of the target jet was constant (500 knots). The scenario had to be flown four times with different following distances (750, 1100, 1700 and 2500 ft). The distance to the target jet was depicted on the tactical display by means of an arrow that moved along a vertical indicator. The centre of the indicator corresponded to the required distance. The scenarios ended with a landing. The start of the approach was signalled by a highway in the sky that appeared on the tactical display. It presented the optimal flight path for the landing. To motivate the subjects a landing score was provided immediately after touchdown. 2.4. Secondary task An auditory continuous memory task (CMT), consisting of series of spoken letters of the Dutch alphabet was presented through headphones. Each time the subject detected a target letter a button had to be pressed, that was positioned on the stick near the thumb. There were four target letters (A, B, Y and Z). The letters had also to be counted in separate tallies. The button had to be pressed twice when a target letter was presented for the third time. If this response was correct, the subject heard the word ‘correct’. When the subject pressed the button at an incorrect moment or when an omission was made, the word ‘wrong’ was presented in the headphone. After the feedback (‘correct’ or ‘wrong’) the tally for the last letter had to be set to zero. The letters D, H, P and W were not used because the sound of these letters could be confused with the target letters. Target letters were never presented in succession. Forty percent of the letters were targets. The CMT was presented half way into the scenario for a duration of 4 min. 2.5. Procedure Subjects participated for two days in the experiment. There were always two subjects at a time. The first day was a training day and the experiment took place on the second day. The first part of the training day was used to train the flight and landing. The scenarios were trained in the afternoon. Each follow distance was flown twice, the second time with the CMT. The second day started with a 30-min training period. The four scenarios were flown in succession in the experiment, starting and ending with a rest period of about 15 min during which the subjects sat quietly in the simulator. The order in which the subjects flew the scenarios was balanced across subjects. 2.6. Rating scale After touchdown, subjects were asked to rate their effort on a scale that appeared on the display (RSME; Zijlstra, 1993). The RSME ranges from 0 to 150 and has also nine descriptive indicators along the axis (e.g., value 2
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corresponds to ‘not effortful’, 58 to ‘rather effortful’, 113 to ‘awfully effortful’). Subjects indicated their rating by moving an arrow along a vertical axis with an optical mouse. The scale disappeared when a key of the mouse was pressed. 2.7. Physiological measures The electrocardiogram (ECG), respiration, blood pressure and the electrooculogram (EOG) were digitally recorded with the CODAS system (DATAQ Instruments, Inc.). The sample frequency of all channels was 100 Hz. Pre-processing of the signals was also done with the CODAS software. Spectral analysis and the calculation of mean values were done by Carspan (Mulder, 1988). 2.7.1. ECG
The times of the R-peaks from the ECG were automatically detected. Omissions were corrected manually after visual inspection of the data. The inter-beat interval (IBI) is calculated from these R-peak times. 2.7.2. Blood pressure
BP was recorded continuously by means of the TN0 Finapress tonometer that measures both systolic and diastolic BP from beat to beat. Subjects could decide whether the cuff was placed around the middle or ring finger. They were instructed not to bend this finger. Systolic and diastolic values of the blood pressure were detected automatically. Omissions were corrected manually. Values were excluded when the apparatus was being calibrated or when the signal was disturbed. Most artifacts were caused by bending of the finger. The missing blood pressure values were replaced by values estimated by the Carspan program. 2.7.3. Respiration
Respiration was measured by means of inductive plethysmography (Respitrace, Inc.). The expansion of the chest and abdomen was measured with two elastic belts. Due to noise in one of the respiratory channels, the respiratory volume could not be calculated in this experiment. The best signal for each subject was used for further analysis. From this signal, thirty sample points (0.3 s) around each R-peak were averaged and used for spectral analysis. Spectral analysis and the calculation of mean values were done within 40-s periods. The periods were shifted 10 s from the previous one (overlap of 30 s). In this way, the output of the Carspan program contained values for each 10 s that had 75% overlap with the adjacent values. Mean values were calculated for IBI, systolic and diastolic BP. Spectral analysis was done for heart rate (HR), systolic BP, diastolic BP and respiration. Since the relation between IBI and HR is not linear, an HR spectrum does not reveal the same results as an IBI spectrum. When IBI increases for example, the amplitude of the IBI spectrum also increases, whereas the amplitude from the HR spectrum
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calculated from the corresponding HR values will increase less or even decrease. Thus, when spectral values of a condition with a low mean IBI (e.g., during a task) are compared with a condition with a high mean IBI (e.g., rest), IBI and HR spectra will reveal different results. Because mental effort affects both the mean level and the variability, an IBI spectrum will overestimate the effects of effort upon the variability while an HR spectrum will underestimate the effect. When the spectral values are divided by the squared mean value of HR or IBI then the spectral values are no longer statistically dependant upon the mean HR or IBI (van Dellen, Aasman, Mulder & Mulder, 1983). After this correction of the mean values, an HR spectrum reveals the same results as an IBI spectrum. In the present experiment, the spectral values of HR, BP and respiration are divided by the squared mean levels (of HR, BP or respiration) within the 40-s windows. The spectral values have no dimension after the division. According to Mulder (1988) they are labelled (modulation index)2/ Hz. Spectral values were calculated for the mid-band (0.07-0.14 Hz) and high band (0.15-0.50 Hz). Coherence and modulus were calculated between systolic BP and IBI and between IBI and respiration. The coherence expresses the resemblance of two channels in the frequency domain and is comparable to a squared correlation in the time domain. The modulus is the gain between the two channels in the frequency domain; this is the spectral energy of the IBI spectrum divided by the spectral energy of the systolic BP spectrum. Because a modulus is only relevant when two channels have a high resemblance, the average modulus within a spectral band is based only on frequencies in which the coherence is greater than 0.50.
2.7.4. Eye blinks Eye blinks were derived from the EOG measured with electrodes above and next to the left eye. The signals from these electrodes are differentially recorded with respect to an electrode placed on the forehead. The EOG was recorded with an AC coupled amplifier (r = 3 s). The derivative of the EOG signal was calculated and used for the detection of blinks (see Fig. 1). A blink was defined by a peak in the differential signal that followed a valley after 0.06-0.20 s. To distinguish blinks from noise in the signal, the amplitude of the peaks and the valleys should be at least 10% of the maximum amplitude in the signal. This maximum was established separately for each subject. The duration of a blink is defined as the time between the valley and the peak in the differential signal. The algorithm for the detection of blinks and calculation of the duration deviates from conventional algorithms in which a blink is defined by a decrease in amplitude and the duration by the time between the halfway amplitude of blink onset and offset (e.g., Goldstein, Bauer & Stern, 1992; Wilson, Purvis, Skelly, Fullenkamp & Davis, 1987). The present algorithm is much simpler and easier to automate. The result for normal (short lasting)
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/
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time(s) Fig. 1. Example of the EOG signal and its derivative. A blink is defined by a valley in the derivative followed by a peak within 0.06-0.2 s. The duration of the blink is the time between the valley and the peak.
blinks is almost the same because the halfway amplitude point is generally the point were the slope is maximal. 2.8. Performance The distance to the target jet was continuously recorded. Mean deviations from the required distance and S.D. were calculated for three 60-s intervals the last minute before the CMT, the last minute of the CMT, and 2-3 min after the CMT. For each scenario the mean reaction time and the percentage correct responses to the third target letter were calculated. 2.9. Analysis
Physiological measures were obtained for the following six segments: rest period before the flight (15 min), flight (before and after the CMT, about 8 min), flight and CMT (4 min), landing (about 2 min), after landing (about 2 min), and rest period after the flight (about 15 min). Three ANOVAs were carried out utilizing a repeated measures design. The first analysis had two factors: required distance (750, 1100,170O and 2500 ft) and two flight segments (flight, and flight and CMT). The factor distance revealed no significant main effects, nor a significant interaction for any of the dependent measures. A second analysis was carried out, pooled over the four required distances, in
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which differences between the rest periods and task periods were examined. This analysis had one factor with six levels (all segments). The differences between the three flight segments (flight, flight and CMT, and landing) were examined in the third analysis. Differences between the segments were tested with a post-hoc test (Tukey HSD). Since the first analysis revealed no significant results for the physiological measures, only the results of the second and third analysis are reported. 3. Results 3.1. Performance The distance to the target jet was recorded continuously during the flight. Mean deviations from the required distance and SD. were calculated for each segment. The results are presented in Table 1. The mean and S.D. did not change as a function of required distance. Both mean and S.D. changed significantly during the flight (F(2,26) = 7.84, p < 0.01; F(2,26) = 15.07, p < 0.01, respectively). Post-hoc analysis showed that the mean and S.D. were significantly larger during the last minute of CMT performance than in the other two segments. During the performance of the CMT, distance to the target jet was larger and more variable. The mean reaction time to target letters of the CMT was 776 ms and the percentage correct responses to targets that appeared for the third time was 79%. 3.2. Subjective ratings Subjects rated their effort after each scenario. No differences between the four required distances (mean value 69).
were found
3.3. Physiological measures Table 2 summarizes the differences of the second statistical analysis. The table gives the results of the post-hoc analysis between rest 1, rest 2, after landing and the three flight segments. Table 3 summarizes the differences between the three flight segments. A description of the effects is given below.
Table 1 Mean follow distances and S.D. during the flight (last minute before the CMT, the last minute of the CMT, and third minute after the CMT)
Last minute before CMT Last minute of CMT Third minute after CMT
Mean (ft)
S.D. (ft)
150 1359 190
250 840 370
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Table 2 Comparisons (Tukey three flight segments
Rest I IBI Systolic BP Diastolic BP HR mid-band HR high band Systolic mid-band Systolic high band Diastolic mid-band Diastolic high band Respiration mid-band Respiration high band Modulus Blink duration Blink interval time
***
Rest 2 IBI Systolic BP Diastolic BP HR mid-band HR high band Systolic mid-band Systolic high band Diastolic mid-band Diastolic high band Respiration mid-band Respiration high band Modulus Blink duration Blink interval time After landing IBI Systolic BP Diastolic BP HR mid-band HR high band Systolic mid-band Systolic high band Diastolic mid-band Diastolic high band Respiration mid-band Respiration high band Modulus Blink duration Blink interval time **
p <
I Biological
HSD tests) between the segments (flight, flight and CMT. landing) Flight
* p < 0.05;
Gaillard
0.01;***
* **
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42 (1996) 323-342
rest 1, rest 2 and after
Flight and CMT
Landing
*** * ***
*** ** ***
After landing
* *** ***
*** *** ***
*** ** *
***
** *** *** *** *** ** ** ***
* *** ***
_ *** ** *** *** *** *** *** * _ *** p
* *** ** *** c** *** *** *** **
*** ***
** *** ** *** *** *** *** *** ** ***
landing
with the
Rest 2
J.A. Veltman, A.W.K. Gaillard I Biological Psychology 42 (1996) 323-342 Table 3 Comparisons
(Tukey
IBI Systolic BP Diastolic BP HR mid-band HR high band Systolic mid-band Systolic high band Diastolic mid-band Diastolic high band Respiration mid-band Respiration high band Modulus Blink duration Blink interval
HSD
tests) between
the three
333
flight segments
Landing/flight
CMTiflight
GMT/landing
*** *** *** _ _ _ _ _ _ _ _ * ** ***
*** *** ***
_ _ _ _ _ _ _ ** *
_ _ ** _ _ _ _ _ _
_ _ _ *** ***
* p < 0.05; ** p < 0.01; *** p < 0.001; -, n.s.
3.3.1. IBI
Results are presented in Fig. 2. The IBI was affected by the segments (F(5,65) = 18.51, p < 0.01). The IBI in both rest segments was larger than in the flight and CMT, landing and the segment after the landing. The IBI in the three flight segments differed significantly (F(2,26) = 17.14, p ~0.01) the IBI in the flight and CMT, and landing was smaller than in the flight alone. 3.3.2. HRV The energy in the mid- and high band of the frequency spectrum differed between the segments (F(5,65) = 10.01, p < 0.01; F(5,65) = 5.11, p < 0.01, respectively). Post-hoc analysis showed that the energy in the mid-band after landing was larger than in all other segments. The energy in the high band after landing is larger than in the three flight segments. 3.3.3. Blood pressure Results are presented in Fig. 3. Both systolic and diastolic BP differed between the segments (F(5,65) = 9.76, p < 0.01; F(5,65) = 12.31, p < 0.01). Systolic and diastolic BP in the first rest segment was lower than in the flight and CMT, and landing. The systolic and diastolic BP in the second rest segment was lower than in the three flight segments. Systolic BP in the second rest segment was also lower than in the segment after landing. Both systolic and diastolic BP during landing were higher than in the segment immediately after landing. Systolic and diastolic BP in the flight segments differed significantly (F(2,26) = 17.47, p < 0.01; F(2,26) = 21.28, p < 0.01, respectively). The pressures during flight were lower than in the flight and CMT, and landing.
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high-band
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1
20 rest 1
flight
flight & landing CMT
after landing
rest 2
Fig. 2. Mean inter-beat interval (IBI) and HRV in the mid- and high band for each segment.
3.3.4. BPV
The energy in the mid- and high band of the spectrum is given in Fig. 3. Both systolic and diastolic BPV differed significantly between the segments (F(5,65) = 7.25, p < 0.01; F(5,65) = 9.48, p < 0.01, respectively). The energy in the high bands also differed significantly between the segments (F(5,65) = 10.46, p < 0.01; F(5,65) = 17.29, p < 0.01, respectively). As was the case with HRV, both systolic and diastolic variability (in both spectral bands) after landing was larger than in the other segments. Only the mid-band of the diastolic BP differed significantly between the segments (F(2,26) = 8.13, p <
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BP
15
high-band
systolic
BP 1
10
0
rest 1
flight
mid-band
diastolic
BP
high-band
diastolic
BP
flight & CMT
landing
after landing
rest
Fig. 3. Systolic and diastolic BP and the variability of both systolic and diastolic BP in the mid- and high band for each segment.
0.01). Post-hoc analysis showed that the variability in the flight and CMT segment was lower than in the flight or in the landing segments. 3.3.5. Respiration The spectral energy in the mid- and high band for the respiratory signal is presented in Fig. 4. The energy in both the mid- and high band was different between segments (F(5,65) = 16.79, p < 0.01; F(5,65) = 4.34, p < 0.01, respectively). The energy after landing in both the mid- and high band was larger
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respiration
0
rest 1
Fig. 4. Spectral
energy
flight
flight & landing CMT
of the respiration
after landing
rest 2
in the mid- and high band for each segment
than in all other segments. The increase in respiratory amplitude after landing was most pronounced for the mid-band. Subjects tended to breathe deeper and more slowly just after landing. There were no differences between the other segments. Fig. 5 presents the results of two subjects to illustrate the relation between respiratory activity and HRV in the mid-band. The first subject frequently demonstrated respiratory activity in the mid-band (corresponding to breathing cycles of between 7 and 14 s) in all segments. The second subject had respiratory activity after three of the four landings (touchdown). The respiratory activity always co-occurred with large HRV This figure clearly shows that respiration can seriously confound HRV. The coherence values between respiration and IBI for each segment are presented in Table 4. The largest values were found for the high band, because respiratory frequencies were most prominent in this range. The modulus between systolic BP and IBI in the mid-band are presented in Fig. 6. The modulus expresses the change in IBI per mmHg change in systolic blood pressure. The modulus differed significantly between the segments (F(5,65) = 6.05, p < 0.01). The modulus in both rest segments was larger than in the flight and CMT, landing and the segment after landing. The modulus during landing was lower than during flight.
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5
10
1.5
0
5
10
15
20
25
20
30
25
35
30
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35
45
40
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50
45
55
50
60
65
60
55
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70
65
75
70
time (min) Fig. 5. The data of two subjects, illustrating the relation between respiratory activity and HRV in the mid-band. Rest 1 is followed by the four scenarios and ends with rest 2. Table 4 Coherence values between respiration and IBI for each segment
Mid-band High band
Rest 1
Flight
Flight and CMT
Landing
After landine
Rest 2
0.39 0.65
0.34 0.67
0.32 0.65
0.33 0.68
0.38 0.53
0.42 0.60
3.3.6. Eye blinks
The duration and interval times of the eye blinks differed significantly between the segments (F(5,65) = 61.86, p < 0.01; F(5,65) = 13.75, p < 0.01. respectively) (see Fig. 7). The average duration in both rest segments was
15
systolic
BP + IBI (mid-band)
5 rest
1
flight
flight & CMT
landing
after landing
rest 2
Fig. 6. Modulus between systolic BP and IBI in the mid-band for each segment.
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Psychology 4.2 (1996) 3.23-342
blinks
140
z130 2 E ‘E 120 P 2 Y 110 c .3 100 rest 1
flight
fl$$&
landing
after landing
rest 2
Fig. 7. Interval between blinks and duration of eye blinks for each segment.
longer than in all other segments. The average iaterblink interval in the rest segments was shorter than in the landing. Both the duration and interval time differed significantly between the flight segments (F(2,26) = 9.99, p < 0.01; F(2,26) = 18.11, p < 0.01, respectively). During landing the interval time was longer and the duration shorter compared to the other flight segments.
4. Discussion 4.1. Performance
When subjects are sufficiently motivated, they will invest more effort with increasing task demands. Although this was not tested explicitly, we have good reason to assume that our subjects were well motivated. Most subjects asked to do the task another time at the end of the experiment because they found the task very interesting. Furthermore, the performance was observed by a second subject who was a colleague. Performance decrement during the CMT seems not to be caused by a lack of motivation to exert additional effort. The task
J.A. Veltman, A.W.K. Gaillard I Biological Psychology 42 (1996) 323-342
demands subject.
where so high that they exceeded
the processing
339
capacity of the
4.2. Physiological measures IBI and BP were sensitive to the different levels of task load. IBI was shorter, and BP was higher during the flight task, as compared to rest. When the CMT was added to the flight task, IBI reduced and BP increased further. Although the absolute differences in systolic BP were larger, the effects of diastolic BP were more stable. HRV in the mid- and the high band was not sensitive to the different levels of task load in the present experiment. Significant effects were only found between the segment ‘after landing’ and the other segments, with the strongest effects in the mid-band. The spectral energy was increased after landing, to a level that was even higher than during rest. In several laboratory studies HRV has been found to be a sensitive index for mental effort. In cognitive reaction-time tasks similar to the CMT used in the present experiment, HRV decreases as the memory load increases. The HRV decreases further as subjects in addition have to count the target letters (Aasman, Mulder & Mulder, 1987; Veltman, 1989). In field studies, HRV has been found to be less sensitive. Only differences between rest and task are reported (Jorna, 1992; Wilson, 1992). BPV was not sensitive to the different levels of task load. Like HRV, BPV increased only after landing. The same pattern of results has been found for the spectral energy of respiration, for both mid- and high band. After the landing the respiratory activity increased, especially in the mid-band. The modulus between systolic BP and IBI decreased during flight and decreased further during landing as compared to rest. After landing, the modulus was not increased, as was the case with HRV and BPV The reduced modulus, which indicates a reduced baroreceptor sensitivity, suggests that mental effort influences the central control of cardiovascular functions, rather than a general effect on peripheral physiological activation (Steptoe et al., 1993). Blink duration was decreased during flight and reduced further during landing. The addition of the CMT to the flight task did not affect blink duration. Blink interval was increased only during landing. The CMT does not impose additional visual demands. However, during landing the visual demands are very high because subjects constantly have to check the air speed, the roll, the position of the runway, and the vertical speed. These results, which have been observed in other studies (e.g. Goldstein et al., 1992; Stern, Boyer & Schroeder, 1994; Wilson et al., 1987) show that eye blinks are sensitive to visual load but not to cognitive load. When operators have to process a lot of visual information, they tend to blink less frequently and when they blink, it is shorter, because otherwise information may be missed. It seems that blink duration and blink interval provide valuable information and may be used as an index for variations in visual load.
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4.3. influence o,f respiration upon HRV The changes in BP and IBI are assumed to indicate that subjects spend more effort during the more difficult segments of the task. It is surprising that no changes in HRV were found, since HRV is assumed to be an index of mental effort (Mulder, 1986; Mulder & Mulder, 1987). The insensitivity of HRV appears to be caused by the intrusion of respiratory activity. Angelone and Coulter (1964) demonstrated that HRV is affected by respiratory frequency. To examine this relation, they asked subjects to breathe at different frequencies. HRV increased when respiratory frequency decreased. The effect was largest at a respiratory frequency of 0.10 Hz (the mid-band in the present study). Even within the high-band region, changes in respiratory frequency may have considerable effects on HRV (Grossman, 1992). These respiratory related changes in HR called respiratory sinus arrhythmia (RSA) are believed to reflect parasympathetic activity. Grossman (1992) warned against a straightforward use of RSA as an index of parasympathetic activity. RSA reflects only parasympathetic activity when corrected for changes in respiratory frequency and amplitude. Respiration can provide valuable information about mental effort. Wientjes (1992) demonstrated that the subjects change their respiratory pattern when the task (similar to the CMT) becomes more difficult. The tidal volume and the inspiratory flow increases. However, respiration can influence the HRV in a direction opposite to the effects of mental effort, as was the case in the present study. Subjects breathed slowly and deeply as was evident from the increased energy in both the mid- and high band of the respiration. High respiratory activity in the mid-band also occurred during other segments, as is shown by the time plots in Fig. 5. Changes in respiration corresponded with an increased HRV and BPV, in particular in the mid-band. The individual data show that some subjects have higher respiratory activity during rest and others during task segments. We have considered several ways to correct the HRV for respiratory effects. One possibility is to compute the modulus between respiration and HR. The modulus however, can only be used when there is a high coherence between the two signals. In the present study the coherence (computed for each time window) was generally very low. This does not mean however, that the HRV was not affected by respiratory activity. The HRV can be affected when there is only one respiratory cycle inside the mid-band. The coherence is based on the average resemblance in the time window and is relatively insensitive to abrupt changes. Another problem with this correction is that respiratory changes outside the mid-band may also effect the mid-band of HRV. Any effect on the BP regulation system will result in changes in the amplitude of the resonance frequency of the system. For example, a sudden increase in respiratory volume, will increase the HRV in the mid-band, even when the frequency of the respiratory cycle is outside this band. Thus, the paradox with the mid-band is that it is the most sensitive band to mental effort but it is also sensitive to other systems, like respiration. When HRV is used as a measure of mental effort, it is
J.A. Veltman, A.W.K. Gaillard I Biological Psychology 42 (1996) 323-342
341
important to measure respiration too, because it gives more insight into the factors that determine HRV changes. Another possibile way to correct for respiratory activity is to compute the modulus between systolic BP and IBI in the mid-band (HRV divided by BPV). This modulus was decreased during flight and reduced further during landing. The modulus did not increase after landing, as was the case with HRV and BPV. The modulus is not affected by respiration, because respiration affects both HRV and BPV The modulus will only be affected by respiration, when the influence upon HRV is different from that upon BPV, or when the influence varies over experimental conditions. However, in both cases the effect of respiration upon the modulus will be far less than the effect upon HRV. Thus, the modulus is a better measure of mental effort than HRV A disadvantage of using the modulus instead of HRV is that BP should be measured from beat to beat. The system (Finapres) we have used measures the BP from the finger, which is not practical when subjects have to use both hands intensively. The present study demonstrates that mental workload can be evaluated by a combination of physiological measures. In a complex task environment, respiratory activity is likely to disturb HRV, especially in the mid-band of the frequency spectrum. The modulus between systolic BP and IBI is hardly influenced by respiration and is therefore a better measure than HRV BP provides information about the increase of sympathetic activity. The modulus between BP and IBI provides information about sympathetic and parasympathetic branches of the autonomic nervous system. Eye blinks can provide information about the visual information load independent of the total workload. References Aasman, J., Mulder, G.. & Mulder, L.J.M. (1987). Operator effort and the measurement of heart rate variability. Human Factors, 29, 161-170. Adams, M.J., Tenney, Y.J., & Pew, R.W. (1991). Strategic workload and the cognitive management of advanced multi-task systems. Report, SOAR, CSERIAC 91-96. Akselrod, S., Gordeon, D., Madwew, J.B., Snidman, N.C., Shannon, C., & Cohen, A. (1985). Hemodynamic regulation: investigation by spectral analysis. American Journal of Physiology, 249, H867-H875. Angelone. A., & Coulter, N.A. (1964). Respiratory-sinus arrhytmia: a frequency dependent phenomenon. Journal of Applied Physiology, 19, 479-482. Bauer, L.O.. Goldstein. R., & Stern, J.A. (1987). Effects of information-processing demands on physiological response patterns. Human Factors, 29, 213-234. van Dellen, H.J., Aasman, J., Mulder, L.J.M., & Mulder, G. (1983). Time domain versus frequency domain measures of heart rate. In J.F. Orlebeke, G. Mulder, & L.J.P van Doornen (Eds.). Psychophysiology of cardiovascular control: models, methods and data. (pp. 353-374) New York: Plenum Press. Fogarty, C.. & Stern, J.A. (1989). Eye movements and blinks: their relationship to higher cognitive processes. International Journal of Psychophysiology, 8, 35-42. Gaillard, A.W.K., & Wientjes, C.J.E. ( 1994). Mental load and work stress as two types of energy mobilization. Work & Stress, 8, 141-152. Gawron, VJ., Schiflett, S.G., & Miller, J.C. (1989). Measures of in-flight workload. In R.S. Jensen (Ed.). Aviafion psychology (pp. 240-287). Aldershot: Brooktield.
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