The effect of secondary tasks on pilot describing functions in a compensatory tracking task

THE EFFECT OF SECONDARY TASKS ON PILOT DESCRIBING FUNCTIONS

..

IN A COMPENSATORY TRACKING TASK

I

B. L. Watson

•

THE EFFECT OF SEC ONDARY TASKS ON PILOT DESCRIBING FUNCTIONS IN A COMPENSATORY TRACKING TASK

by

B. L. Watson

SUbrnitted, May 1972.

Acknowledgement

The author would like to express his thanks to his supervisor Dr. L. D. Reid for his helpful advice in completing this report.

The author also recognizes the time and effort expended by his

.subjects who cooperated whole-heartedly with the project. A vote of thanks should also go tb the Canadian Forces Personnel Applied Research Unit who gave assistance of equipment and subjects. A special thanks to

Mr.

N. H. Drewell and Mr. A. Perrin who provided technical assistance at critical times and further to the National Research Council of Canada, who finan -cially supported the work under Grant Nurober A.7934.

Summary

This report explores the effect certain secondary tasks have on human pilot describing functions. The describing functions are generated from a compensatory tracking task with rate-control dynamies. The experi

-ment involved six well-trained subjects in a multi-task situation where the primary control task was tracking. The results are presented as amplitude and phase plots of measured describing functions, which are fitted by an eight-parameter theoretical pilot model. The effects are described in terms of both raw data and model parameters.

1. 2.

3

.

4.

5.

6.

TABLE OF CONTENTS INTRODUCTION PILOT MODELS

2.1 The Compensatory Tracking Task

2.2 The Calculation of Pilot Describing Function 2.3 Analytical Descriptions of the Pilot Model EXPERIMENTS

3.1

Equipment 3.2 Random Inputs 3.3 Sign Convention 3.4 Secondary Tasks

3.4.1 Visual Secondary Tasks 3.4.~ Audio Secondary Task 3.5 Training

3.6 Experimental Runs

3.7 Data Reduction Technique

3.8 Fitting Routing for the Pilot Model

THE IDENTIFICATION OF AN ANALOO PILOT EXPERIMENTAL RESULTS AND DISCUSSION 5.1 Scores

5.2 Experimental Measure of Pilot Describing Functions, Remnant Values and Curve Fits

5.3 Ratio Fits and Statistical Analysis 5.4 The Effect of the Visual Task - STl 5.5 The Effect of the Audio Task - ST2 5.6 The Effect of Training

CONCLUSIqN REFERENCES TABLES FIGURES PAGE 1 1 1 2

3

4

4

5

5

5

6 6

6

7

7

8

8

9

9

9

I I I I 12 13 13 15

A(s) e(t) i(t) j Kp m(t) n(t) o(t) p(t) Rab (or) s t T w n Y(s)

ex

2 p (w) cr T or m Notation

Transfer function of the vehicle control dynamics Error signal representing (i(t) - m(t))

Primary input signalor desired system output Complex number ~l

Pilot-model DC gain

Vehicle control dynamics output

Pilot's remnant injected at his output

Pilot' s st.ick output

That input to the vehicle control dynamics due to n(t) The cross-correlation between a(t) and b(t), called

auto-correlation if a(t)

=

b(t) The Laplace transform variabie Time, sec.

Sampling period; sec

Time constants in the pilot model, sec

Frequency in rad/sec

The natural frequency of the neuromuscular system model The compensatory pilot describing function

Low 1 -frequency eI>pp(w) eI> (w) 00

lag-lead parameter in the pilot model is the correlation coefficient

Standard deviation

Transport lag in the Pilot Model

Maximum time delay interval involved with correlation

calculations

Cross-power spectral density of a(t) and b(t) called

the auto-power spectral density of a(t)

=

b(t) The damping ratio of the neuromuscular system model

1. INTRODUCTION

A method of describing the human pilot in mathematical terms is essential

in the analytical treatment of manual control systems of aircraft today. To date,

the most successful technique has been the quasi-linear model which attempts to

encompass the enormously adaptive nature of the human pilot with parameters which change with the system variables. This servo-analysis approach has been success-fUlly applied to flight situations and has been well documented in Refs.

1,2,3,4.

Howeve~ the analogy of the human pilot describing functions to the real-life situation cinnot be fully justified. One of the reasons is th at the per

-formance and the strategy on a single control task, i.e., tracking alone, appears

particularly good because tae operator is allowed to concentrate all his attent ion

to the one task and is not required to share it among several tasks.

In this study, a'compensatory tracking display, with a rate-control

vehicle dynamics, and a random input are combined with two separate secondary

tasks to investigate the effects and interactions a subsiduary task has on the

parameters of the pilot model. The effects of these secondary taks on the pilot

dynamic characteristics are investigated in a four stage procedure: (1) Subjects

are trained; (2) Describing function and remnant measurements are taken in an

extensive experimental programme involving tracking with secondary tasks; (3)

Analytical descriptions are made by curve-fitting the raw data of amplitude and phase plots to a generalized pilot model; (4) The effects of the secondary tasks

are described in terms of both the raw data and the parameters of the model gained

fr om the curve-fitting.

The effects will ~e used to assess the validity of the present model but

also it is hoped that such information might be useful to determine factors in the design of equipment; such as how much attention is required to perform certain

secondary tasks, how compatible a certain task is with the control task or how well the operator may perform the primary control task whilst under additional

loading 0

Secondary tasks have been very useful in the investigation of many areaso In Ref.

5,

secondary tasks were used in assessing the effect of lag in simulated

aircraft dynamicso In Refs.

6,7,

they were used to investigate "spare mental

capacity of car driverslf

•

Overall, it has been thought that additional load should cause

signifi-cant interference in the primary tasks. It was found in Ref.

8,

that secondary

tasks resulted in !tmarked interference in the timing aspects of trackingil

• In Ref.

9,

the addition of secondary tasks to tracking, with highly trained subjects,

produced a regression in performance to a level of complexity analogous to that

found in the early stages of training.

All this would indicate that there is a possibility that the pilot model

might change so drastically when secondary tasks are added that it would require

an entirely different form.

20 PILOT MODELS

2.1

The Compensatory Tracking Task

tracking and its quasi-linear model, one must first realize how the human-operator and the model are made up.

The human displays an appreciable amount of non-linear and time-varying behaviour but at the same time exhibits, in the case of manual tracking,

con-siderable "linear-like" features. The concept of quasi-linearity describes the

human operator in such a way that the linear portion of his output can be extracted

and made up into a useful linear transfer function and the "non-linearity" can be

estimated in terms of the tonal output. This is done by introducing noise gen-erators into our model; wè will define noise as any part of the signal which is not linearly correlated with the target movement. Thus the entire output is taken

into account.

This "non-linearity" is called the remnant and it may be due to four main sources: (1) non-linear components in the pilot; (2) time variations in the pilot

control technique; (3) signal sampling by the pilot;

(4)

random noise injected

into the system by the pilot. In Ref. 1 it is pointed out that the major source

.may be from pilot injected random noise.

With the compensatory display, the pilot is presented with the system

error only and attempts to minimize it. The most logical model for this type of tracking is the single input/output describing function as seen in Figs. 1 and2.

The physical si~uation is model~ed such that the linear portion is taken into

account by the pilot describing function yes) whilst the remnant is added as a

time signal net) as depicted inFigs.3 and

4.

2.2 The Calcu~ation of Pilot Describing Functions

In this study, the optimum pilot describing functions were calculated

through the use of power spectral density measurements which has been thoroughly

documented in Ref. 2.

From Ref. 1, the following expression is used in the calculation of the

pilot describing functions for compensatory tracking

Y(jW)

=

<P. (w) lO <P. (w)

le

From the resulting complex number, one acquires the raw data of amplitude and.

phase points for a range of frequencies. The power spectral density is defined

by

-jWT

where Rab(T) is the cross-correlation function of two time signals aCt) and bet)

lim

l

J

T

Rab(T)

=

T -HO T a(t)b(t+T)dt

o

1P

,(w) is employed as a measure of how well the describing function matches the pp

2

P (w) 1

-<1>pp(W) <1> (w)

00

For actual calculation this is rewritten, as explained in Ref. 1, since it is

easier to obtain the signal i(t~ than p(t). It becomes

2 p

(w)

1<1>.

(w)

12 10 = -:&"<1>-• • -r(-w~)<1>r-r( w-')~ l~ 00

2.3 Analytical Descriptions of the Pilot Model

Once the amplitude and phase points for an individual are experimentally

measured and processed, the resulting describing function is fitted to a linear model. This is done by fitting the means of the data to a model described as

"Extended Crossover Model" and documented in McRuer et al. in Ref. 2.

1

This form is used for compensatory tracking with rate control dynamics.

I t should be noted that this rnay be internally "adjusted" by the human operator

to accommodate for different situations. The model is valid in ~he frequency

domain (jw) only and makes no provision for transient effects, i.e., it exists

under stationary conditions only.

Each component of the model has been validated by previous studies

with regard to the measured effect and an attempt to identify these components

serve to further enhance the understanding of the human physêo-motor process. The third order term

1

represents the neuromuscular system of the arm and comes from experiments based on high frequency data employing step-function inputs. The dampening ratio

and the natural frequency

w

vary considerably from individual to individual.

n

With typical value being

SN

=

0.01 and W

N

=

17.0 rad/sec. Typical values of

the neuromuscular lag Tn is O.~ sec.

-jWT

The term e represent a pure time delay thought to describe the

processing activities in the central nervous system such as computation lags, sensory excitation, nerve conduction, etc. It is related to classical reaction

time. Inter and intra subject variability of this parameter occurs but it has

been consistently measured for this type of tracking, in the range of 0.1 sec to 0.2 sec.

The adaptive capability of the human operator is taken into account

wi th the term

combined with the gain Kp. The enables him to control numerous dynamic devices.

-a/w

The term e accounts for the phase lag at the very lowest frequencies.

3. EXPERIMENTS

3.1 Equipment

The equipment layout is depicted in the block diagram of Fig. 5a and the photo of Fig. 5b.

Prerecorded random inputs are fed into an analogue computer interfaced

wi th the displayelectronics of the work station. Thse input signals are

com-bined with the integrated pilot's stick output to produce the displayed error

signal in the cockpit. The integration of the pilot's stick output provides

the rate-control vehicle dynamics of the system. The signals necessary for the

calcula~ion of the pilot describing functions, i.e., the input, the output and the error are recorded on a digital tape recorder. The experimenter can monitor the input and the error signal via an external oscilloscope and can check at the end of each run the RMS value and DC compone~t via the analogue computer. The

secondary tasks can also be monitored by means of a coloured light display and

speaker system.

The cockpit (Fig. 6a, photo) is contained in a work station previously

designed at the UTIAS. The displayelectronics create the displayed error

(Fig. 7) seen on the Hewlett Packard 143A, 8" x 10" oscilloscope which has a

sensitivity of 0.58 ~/in. The target circle moves vertically and because of

the rate control dynamics, moves such that the circle velocity is proportional

to the control column's position. The diameter of the target circle is 0.12 in.

The various equipment dimensicns and related subject measurements can be seen

in Fig. 6b. The work station is air-conditioned, light controlled and noise

insulated and is linked by an intercom system to the experimenter. A separate

hi-fidelity system, a Sony stereo tape recorder model TC-500A employing a

sub-ject's headset, is used to perform the audio secondary tasks.

The control column consists of a low inertia hollow aluminum tube with

a balsa wood grip handle (total wt

= 6 oz, C.G.

=

12 in above pivot, the grip

18.0" from the pivot) and is connected directly to a linear continuous resolution

potentiometer. The control column travels in the fore and aft direction limited

to + 17.50• It is for all inte~ts and purposes a free stick although a light spring is employed for re-centering (spring constant 0.34 gms/degree of

deflec-tion. W = lt.8 rad/sec. damping ratio = 0.0067). The overall system gain

was set ~o give 0.94 in/sec circle velocity per degree of stick deflection.

Arevox 1102 is used to playback the input tape at 7.5 in/sec (frequency

range of 50 Hz to 15 KHz, + 1.5 dbs). The technique to obtain the lower fre-quency range is explained In Ref. 11.

- - - --- - - --- - - - .

A digital tape recorder, built by the Electron Engineering Co., of

California, employing a Kennedy DS-370 digital magnetic tape recorder interfaced

with a General Radio type 1151-digital time and frequency meter, records the raw

data of input, output and error signals.

A random number generator was specially built for the experiment and

consists of a drum programmer, DC power supply and a Boroughs B5031 Nixie tube.

The Nixie tube is 10 digit (0 tlthroughtl 9) numerical indicator tube with a cup

design providing a non-glare background and generating figures with heights of

0.61 in, Fig.

8.

3.2 Random Input

It is very important that the input signals being used are not predic-table, otherwise the human operator will quick+y adapt with training and follow the system nearly perfectly. Previous studies have indicated that good results

can be attained using Gaussian input signals (random-appearing inputs) which

also simulate certain real-wor~d inputs such as gust distur.bances.

The input tapes used in this experiment were basically of medium

frequency and were produced by passing the output of a random noise generator

through a set of carefullydesigned filters and recording the resultant signal on a two"",channel, tape recorder, one trFl-ck at Ç3. time. Thes,e have been used extensively in previous UTIAS studies and the procedure is thoroughly described

in Ref. land the input spp.ctrum is displayed in Fig.

9.

The basic requirement

was that the auto-power spectral density shape had to be specified, the RMS

level fixed and their mean values (DC component) made as close to zero as possible.

The RMS and DC component were further controlled upon playback by a bias voltage set up in the anàlogue computer. Each of the 15 separate 150 second runs required

slightly different potentiometer settings which had to be re-adjusted from time

to time to keep within the decreed tolerance of a displayed DC level less than

0.010 in. The displayed RMS i~put level was 0.5 in.

3.3 Sign Convention

The following sign convention is used in this experiment: i(t) - positive when the target circle is driven above

the stationary reference line.

e(t) - positive wh en the target circle is above the

stationary reference line.

o(t) - positive when the control column is back which commands the vehicle to climb.

m(t) - positive when the controlled vehicle is above

the display centre.

3.4 Secondary Tasks

This experiment was run with a control, tracking only and two other

conditions; tracking with STl (Visual Secondary Task) and tracking with ST2

3.4.1 Visual Secondary Task

STl involves a motor response to visually presented random digits in the forward field of view. Figures 6a, 8 demonstrate exactly the configuration. Subjects can monitor both the tracking disPtay and the number display quasi-simultaneously without gross-switching from one to the other. A similar task had been used in an experiment Ref. 5"investigating the effect of lag in

simu-lated aircraft dynamics.

The task is to respond to "odd" or "even" digits by moving a throttle, located on ~he left-hand side, forward or backward respectively. Af ter each response, the subjects would then centralize the throttle. The rate of presenta-tion of the digins is controlled by an electrical drum programmer. Af ter some initial experimentation, this rate was set to 2.5 sec between digits with the digit display being left on for the same amount of time. The secondary task score is the percentage of correct responses which is recorded through a set of coloured lights at the monitoring station. The subject was instructed to do as well as he could on the secondary task without letting it interfere with the primary task of controlling. It was thought that this secondary task represented a task similar to what he might meet in the cockpit.

3.4.2 Audio Secondary Task

ST2 was particularly selected because of its similarity with the under-standing of an ATC clearance. It is a reasoning test based on gramrnatical

transformations described by Baddeley in Ref. 10 (1968).

The subject, wearing a headset, received a pre-recorded tape of a series of statements each of which is followed by the letters ".Aa" or "BA". The subject has to decide whether this statement is "spatially" true or false by verbally responding. Examples of thestatements are as follows:

Example Subject' s Correct Response A is followed by B - AB True

B is not followed by A - BA False A does not precede B - BA True

B precedes A - AB False

The task is force-paced in that the total time allowed for the pre-recorded statement and the subject's response is 10 sec, i.e.,

Pre-recorded Statement - 5 seconds Subjeet's Response -

5

seconds

The secondary task performance is measured by scoring the responses as a percentage correct.

3.5 Training

The subjects involved in this experiment went through 2-1/2 months of training prior to entering the main body of the experiment. The common denomi-nator of this group was their flying experinece; data can be seen in Table 1.

The System Tracking Error, a normalized mean square error was used as a performance measure, it is defined as

T.Eo =

lT

e2 dt

and a lower value indicates a better score. In this experiment the tracking

period was 150 secs. The normalization is necessary to take into account any

run to run variation in input RMS.

A close check was maintained on the tracking scores and a cont~nuous

learning curve was kept on each subject for trend analysis and subject motiva-tion. When the tracking scores levelled off and remained at a particular level, the subjects were run in the main body of the experiment; this meant not only the tracking alone, but tracking with secondary tasks. Figure 10 shows typical

learning curves for two subjects. There are 3 curves corresponding to the

"Tracking Only" condition and the two "Tracking with a Secondary Task" condi

-tions which although are plotted over the same 40 rms were not in actual fact

run concurrently but as separate runs in order to demonstrate the secondary

tasks effect. During the training phase the subject tracked and performed

secondary tasks in the same routine as used later in the main experiment. On

the average a total of 70 runs, 150 sec in length were given to each subject to bring him to asymptotic level. In both the training phase and the experiment, the subject was informed of his score at the end of each tracking run.

3.6

Experimental Runs

The main experiment investigated the effect of two secondary tasks separatelyon the primary task of tracking and all other controllable factors

were kept constant.

The main body was conducted with six subjects in a balanced design as

depicted in Table 2. This was chosen to determine later if any order effect

or interaction existed between the tasks. The subjects were given all the tasks

but in different order. The input tape runs (15) were randomized for each

subject.

Each subject was given one practice run and then three recorded rvns

of 150 sec. A two minute rest-period separated each run. Af ter a break of 1/2

hour the four runs were repeated with a different condition and again repeated

the same day until all the conditions had been run. The following week the

procedure was repeated as above. A total of six runs were made for each subject

and each condition. The training and the experimental runs were performed

Wednesday morfrings and a~l day Thursday.

The subject was started with 10 sec of the secondary task alone,followed

by 15 sec of unscored, unrecorded tracking with the secondary task and then

commencing with ~50 sec of scored, recorded tracking with the secondary task.

This allowed for any transient reaction that might be occuring during the initial

stages.

3.7

Data Reduction Technique

recorded on magnetic tape. The tape is then processed by a program developed at the UTIAS for an IBM system 7094. The program first makes a translation of this

information into ~seable form for a main programme which involves the calculation

of cross-correlation functions as an intermediate step and then power spectral densities. The output of this program gives punched cards, giving amplitude,

phase and correlation values over a range of frequencies of 0.315 rad/sec to

21.764 rad/sec. In all there are 35 measured points in intervals of 0.631

rad/sec. This is determined by the fact that the original sampling rate had

been 25/sec over a 150 sec tracking periode A suitable maximum time delay for cross-correlation calcula tion in this case turns out to be 9.96 sec. giving an

interval of 2V/T

=

0.631 rad/sec.

m

3.8 Fitting Routine for the Pilot Model

Once the measured amplitude and phase are plotted, a computer program is employed to fit this data with an 8 parameter theoretical pilot model. It is based on adjusting the 8 parameters to minimize the RMS difference between the theoretica+ curve and the data points. The program is developed and

des-cribed in Ref. 11; it is executed on an IBM System 1130 with an 1132 printer

and a 1627 plotter.

It is designed to take the means and standard deviations of the

ampli-tude and phase points plus the initia~ values of the 8 parameters as chosen by

the operator. It completes a fit when an increment of 0.0125% to a model para-meter causes an improvement of less than 0.01% in the defined goodness of fit

function; ~ the gain parameter is a speciaL case where less than 0.1% is

required. p

One problem that was thought to exist was the fact that the values

of the 8 parameters could be traded-off to attain the same fit. A check was

carried out to see if approximately the same parameters-were attained when the convergence was started with different initial values. It was found that

if the same "fitting error" for a particular curve was employed and minimized

over several runs, the results of the parameters were very nearly the same; the percent difference comparing the parameters are as follows:

= 3.1% T

=

6.ff/o

a=1.9%

W

n =

1.8%

sn =

1.%

4. THE IDENTIFICATION OF AN MALOO PILOT

A validity of the complete system including the software routine which

calculates the power spectral densities, was checked by inserting a suitable

analog pilot into the closed-loop system and measuring the resulting amplitude, phase and correlation points under simulated experimental conditions.

Prior and during the experiment a number of runs were made with the analog pilot depicted in Fig. 11. The measured data of four typical runs is plotted, Fig. 12, and can he verified by comparing this to the calculated

values for the analog pilot system. The equation representing this system is

It can be seen from Fig. 11 that the overall identification perfor-mance is very good. The only exceptions happens to be the lowest frequency point and two of the highest frequency points where the correlation drops off; also the variability of amplitude and phase increases on the last nine frequency points. This is ,chiefly due to low signal to noise ratio in the system at the higher frequencies leading to poor power spectral density estimates.

5. EXPERI.MENTAL RESULTS AND DISCUSSION

5.1 Scores

The average tracking scores of the individual subjects over six runs for each conditions, can be seen in Table

3.

It clearly shows that the STl task, which was considered to be more difficult by all but subject PZ (Table 1) has interfered as far as the tracking performance is concerned. The ST2 task, on the other hand, displays a similar de~ree of performance to "tracking only". This was not so during the early stages of training where the subject exper-ienced a degredation in tracking score much the same as the STl effect, as demonstrated by the learning curve of Fig. 10.

It can be said that the tracking error appears to be a sensit ive measure in terms of secondary task loading although as demonstrated by ST2, efficient encoding by the svbject allows him to practically duplicate his "tracking only" efforts. All subjects agreed that tracking with ST2 (audio) was more difficult than tracking a10ne but found that they cou1d separate the information more readily and handle it accordingly.

It should be noted also that the ranking of the score between sUQ-jects is the same between tracking àlplfJlànGL trà.Ckgng· with ST2 except for

o

rl

e

pair switch, while there is &ubstantial variation with ST1.

The other scores to consider are those of the secondary tasks them

-selves. In all subjects and considering both secondary tasks, the scores were 99-100% correct. They indicate that af ter substantial training no

significant errors were being made on either of the secondary tasks. Although the instructions were explicit concerning which task was the primary task, it would appear that the subjects were regarding the secondary task with more importance than the primary. The reason for this could be that the secondary tasks themse1ves might have been more compelling since their success was

self-evident. That is, the subject knew at the end of the trial just how well tpey had done on the secondary task while they had to be told the score on the tracking task.

However, additiona1 measurement might have been profitab1e, to determine to what degree time-sharing of the tasks existed. This could be done with an X-Y plotter, displayingthe error of the control task, and by studying his trace while he is making a decision on the secondary task. 5.2 Experimental Measure of Pilot Describing Function: Remnant Values

and Curve Fi tE

The describing functions of the main body of the experiment are plotted in Figs. 12 to 18. It should be not~d that these effectively model the overall dynamics of the pilot, the,jjoystick and the display; no technique has been

employed to separate each component. This is of no real c~nsequence since the stick and display can be described as a pure gain term.

First, each subject is plotted under the three experimental condi-tions with values averaged over six runs and then all the subjects are plotted

together averaged over all 36 runs. Included in this plot are measured + 1

standard deviation bars. The scales of the plot consist of amplitude measured in degrees of stick movement generated per inch of displayed error versus frequency; and of phase measured in stick output relative to the error signal in degrees versus frequency. A phase lag is represented by a negative quantity.

The correlation expresses the goodness of fit between the measured describing function data and the actual pilot. A perfect fit would be a correlation value of 1.0 whilst a non-linear element would produce a value

less than l.O. Values above 0.5 still represent a workable model. In the case

of the plot representing all the subjects, the standard deviation bars at the higher frequencies points are off-scale below, indicating that the value of the standard deviation is greater than the mean.

The results in terms of the describing function of this experiment can readily be compared with previous work (1) done at the UTIAS on

compen-satory tracking with rate control dynamics. The amplitude and phase plots

are basically the same although the difference in overall gain of the system changes the amplitude magnitude by a constant factor.

A greater stress has been put o~ the individual describing functions

because of the inter-subject variability. The remaining variability displayed in individual plots can qe accounted for by the intra-subject variability

during the time period ofl the experiment and, in addition, the inherent experi~

mental error.

From the visual evidence of the three plots it is not readily apparent what effect each secondary task has on tracking but there are some general

conclusions that can be drawn. First, at low to medium frequencies in all

cases, a small degree of variability is displayed lending evidence to the fact that the subjects are well-trained. This can be compared with the effects of training which are discussed later. At the higher frequencies, the greatest inter and intra subject variability existed as demonstrated by Figs. 12 to 18; this is chiefly due to the selection of control technique th at the subject

employed. In one extreme case, KK, Fig. 17 the subject's amplitude has

re-mained practically constant showing no sign of the normal resonant peak, yet his tracking scores, variability and gain would indicate that he is well-trained.

The correlation appears to take on a characteristic curve which of

course decreases at the higher frequencies. There also appear to be a

relation-ship between the tracking scores and the general correlation trend. For i:ostance, in all cases it can be seen that the correlation is less for the "STl" which also has the poorest tracking scores. The "ST2" and "Tracking Only" tasks have very similar correlation values as were their scores.

Each of the measured describing functions were fitted with the eight parameter theoretical pilot model by the computer programme described

pre-viously. It was decided that only the first 27 points out of the 35 measured

points would be used in tbe fitting. This decision was made, based on three factors: (1) the analog pilot identification showed a slightly poorer fitting

performance at the higher frequencies; (2) the measured correlation values for

individual runs fell off at the 27th point indicating a poor fit to the human

pilot (Fig. 13); (3) and in some cases a double resonance appeared at the higher

frequency, which was not consistent wi th the chosen model. This can be seen

in the raw data of Fig. 13 where two resonant peaks appear to exist at 18.3

rad/ sec a.l1d 20.4 rad/sec .in the "Tracking Only" case. This caused

majo-r--fitting problems with certain parameters mf the model, in particular the

damp-ing ratio and the natural frequency of the neuromuscular system. On all

dia-grams a sma1l arrowon the amplitude and phase plots indicate the last fitted

point and an example of a complete fit (all 35 points) is demonstrated in

Figs.12 and 13.

The value of the parameters are listed in Table 4. Generally these

values correlate quite closely with values found in previous experimental

work (1). Although in comparing the damping ratio, it can be seen to be con

-sistently lower in magnitude than found in other studies. This can be attri

-buted to the fact that by fitting only to the 27th data po~nt, the resonant frequency range has not qui te been reached and therefore the actual cürve 'of the

high frequency points is : different, Figs. 13 and 14. A fit of all the 35

points is demonstrated in Fig. 12, by the dotted curve, where it can be seen that damping ratio parameter increases to that value which has been found in other studies.

5.3 Ratio Fits and Statistical Analysis

In order to present graphically the effects of the secondary tasks,

a "ratio" of the "tracking onf-y" to the "secondary task" describing functions

was plotted. This we have defined in terms of amplitude ratios, phase

dif-ferences and correlat,ion changes. A calculation was made for lIeach subject",

and "all" comparing the "secondary task" to the "tracking only" in Figs. 19

and 25. If the "tracking only" amplitude has a greater value, the ratio will

appear above the 1.0 line; if the "tracking only" phase lag is less it will

show a positive value; and if the "tracking only" correlation is greater i t

will show a positive value.

It has been demonstrated in previous UTIAS studies Ref. 1 that the

underlying statistical distribution of the measured data could be considered

to COIre from a Gaussian distribution. A hypothesis that the amplitude, phase

and correlation vallies for each frequency point of "tracking only" were the

same as those for tracking with a secondary task, was tested. The particular

statistical check employed here was the Aspen and Welch t-test Ref. 12

con-sidering a 95% confidence level. This was used since there was no indication

that the distributions being compared had the same variance.

In the graphs of "ratio fits", the signific.antly different values

are shown as darker points with aflag. The tracking scores were also

com-pared and significant differences are indicated in Table 3 against the

appropriate secondary task by an asterisk.

5.4 The Effect of Secondary Task l(Visual)

Distinct effects took place in this dual task situation which re

-sulted in significant differences in certain areas. The most prominent is

the correlation value which in all subjects has significantly decreased.

to be more specific, to the selective-attention character of the task; both the primary task and the secondary task employ visual inputs and both tasks evoke motor responses. Thus employing the same sensory modalities has an effect of increasing the "system noise" •

The tracking performance appears to be affected in the same manner in that it is significantly poorer. Yet the amplitude and phase remains relatively undisturbed with a few significant differences existing in a greater phase lag, and in some cases a smaller amplitude. This would appear to demonstrate the difference if comparing this effect with learning; ob-viously the secondary task has been well encoded and can be done effectively without disrupting the tracking technQque yet still creating a decrement in tracking performance and a reduction of the correlation coefficient.

The model parameters have changed only slightly since the amplitude and phase have been hardly affected. When the parameters of the model

describing "all" subjects is considered Figs. 18, 25 the increase in the

phase lag with STl is signified by a larger T value. This serves to illustrate

that the parameters might be useful in final analysis of a displ?y and control system.

5.5 The Effect of Secondary Task 2 (Audio)

The results of this particular task are far more difficult to inter-pret. It should be emphasized that this dual-task situation employed different sensory modalities, between the primary and secondary task, with the input of ST2 being auditory and its response being verbal.

Generally there were few differences that existed between the "Tracking Only" model and the "Tracking with ST2", including the tracking scores. In fact, this was further complicated by the noticeable effect of a slight improvement in score with 3 of the 6 subjects; the three concerned being the best trackers.

The results of Figs.

19

to 25 show that only a few significant differences were realized in the describing functions. It follows that the parameters of the theoretical model are not too different either. The three subjects that displayed lower tracking scores with ST2 were fitted with models with either a lower T parameter or equal; this might mean that the time delay

I

affecting men~al processing of tracking did not appear to be disrupted by the ST2 and that the dual tasks:a;be being shared by some parallel process. This hypothesis requires further investigation and experimentation. Another answer might be that the subjects are in a higher state of "arousal" when employed on the two tasks.

In any case when the primary task and secondary task occupy separate modalities, it would appear to facilitate a more efficient processing and allow both tasks to be done wi th less interference. It would seem eas±èr 'to separate the two taks and follow any guidelines which could improve the overall strategy. This ideas has been demonstrated by other experimenters. Glucksberg

(1963)

Ref.

13

showed that in a visual pursuit tracking task, the addition of secondary tasks in auditory or cutaneous modes did not degrade performance on the primary task yet a visual secondary task did. In a similar experiment hy Cliff (Ref.

14),

a dual-task situation existed, employing a shadowing task with an audio input and verbal output. It was fourid that with

, ,

slow shadowing the same result was demonstrated'; yet with fast shadowing there was "consistent performance decrement" with this audio secondary task. The latter effect could be interpreted as the subject hav~ng reached his

'~oad capacity' and, or, that efficient encoding had-not yèt been achieved.

" .

..,..--5.6

The Effect of Training '

During the training phase, a careful record was kept of each subject's progress and certain ,o'f the runs Vler~ recorded and processed. Figure

26

re-presents a typical subject, BM at very ear'ly stages whose tracking score averaged over six runs was

66.7.

The middle stage of training of the same subject is shown in Fig.

'

26 (6

runs) and was at a time when the subject achieved a .considerable drop in his tracking score to-

48.8.

The last stage

(6

runs) of this subject can be seen in Fig.

26

and represents the same man as a well-trained subject with a mean tracking score of

35.3.

All of these stages were fitted with the theoretical pilot model and each of the earlier stages, i.

e.,

"mid" and "early" are compared to the final stage in a ratio of the "final" to "mid" or "early" describing function in Fig.

27.

It can be seen quite readily that the amplitude increased and phase lag decreased with training, since theearlier stages showed significant differences in nearly all the points as compared to the final stages, indi-cating an increase in open loop bandwidth and a decrease in phase margin. This contrasted with Ref. 1 where no such trend for pursuit tracking existed. From tpe curve fitting, one can conclude a substantial increase in Kp, the gain and a decrease in the T parameter, transport lag., once past the inter-mediate stage. The actual shape of the curve changes as the subject becomes more proficient and in particular the low frequency lead-lag effect takes a definite form at the later stages and therefore the

a

parameter has increased. Also quite noticeable is the lack of neuromuscular resonance at the early

stages. The dampening ratio is seen to decrease as the resonant peak occurs, as his training progresses. This is generally what occured in all but one subject KK who increased his gain with traini~g but remained unresonant at the higher frequency; see Fig.

17.

It seems apparent that modelling displays sensitive measures for detecting the learning effect.

6.

CONCLUSIONS

The pilot/vehicle interface is the last remalnlng unsolved problem in the development of the display, control and sensory components. Manned systems should be designed to faci+'itate performance requirements:

This experiment has demonstrated that the Human Pilot Describing Function technique is applicable to tracking tasks with a secondary task present and the describing functions appear to remain basically unchanged.

The other conclusions drawn are:

1. In the STl task, the remnant and tracking score

si~ificantly increase. The amplitude and phase are only slightly affected with a trend showing an increase in phase lag.

2. The ST2 demonstrates no significant interference although considered "difficult".

3.

Displaying the task by different modalities could

be considered to improve the overall performance and allow both tasks to be handled with iess interference.

4.

The effects of secondary tasks, although fairly

similar to learning effects are not the same. In learning effects one can detect changes in

the desctiö~ag funçtion itself i.e., ~he

ampli-tude and phase, besides the correlation measure. Whilst the secondary tasks effect the correla-tion measure only.

1. Reid, L. D. 2. McRuer, D. Graham, D. Krendal, E. Reisener, W. Jr. , 3. Weir, D. H. 4. Wingrove, R. C. 5. HUddleston, H. F. 6. Brown, I. D. Tichner, A. H. Simmonds

,I;l.

C •

v .

7. Brown, I.D. 8. Trumbo, D. Noble, M. SWink, J. 9. Garvey,

w.

·

D • 10. Baddeley, A. D. 11. Drewell, N. H. 12. Winer, B. J. 13. Glucksberg, S. 14. Cliff, R. C. REFERENCES

flThe Measurement of Human Pilot Dynamics in a Pursui t-Plus-Disturbance Tracking Taskfl • urIAS Report No. 138, 1969.

flHuman Pilot Dynamics in Compensatory Systems fl •

flThe Measurement and Analysis of Pilot Scanning and Control Behaviour During Simulated Instrument Approaches fI • NASA CR 1535, 1970.

flA Comparison of Methods for Identifying Pilot Describing Functions from Closed-Loop Operating Records fI • NASA TN D-6235, 1971.

An Evaluation of the Usefulness of Four Secondary Tasks in Assessing the Effect of a Lag in Simulated Aircraft Dynamies. Ergonomies, Vol. 14, No. 3, 371- 380, 1971.

flInterference Between Concurrent Tasks of Dri ving and Telephoning fl • J. of Appl. Psychology, Vol. 53, No.5, 419- 424, 1969.

flMeasuring the 'Spare Mental Capacity' of Car Drivers by a Subsidiary Audi tory Taskfl • Ergonomics ,

Vol. 5, No.l. p.247, 1962 •

. flSecondary Task Interference in the Performance of Tracking Tasks fl • J. of Exp. Psy., Vol. 73, No. 2,

pp. 232-240, 1967.

flA Comparison of the Effects of Training and Secon-dary Tasks on Tracking Behaviour fl • J. of Appl. Psy., Vol. 44, No.6, 1960.

flA 3 in. Reasoning Test Based on Grammatical

Transformations fl • Psychon. Sci., Vol. 10 (10), 1968. flThe Effects of Preview on Pilot Describing Functions in a Simple Tracking Taskfl • UTIAS Tech.Note No.176, 1972.

flStatistical Principles in Experimental Design". Chapt. 2, p.36, 1962.

flRotary Pursuit Tracking with Divided Attention of Cutaneous, Visual and Auditory Signals fl • J.Eng. Psychol., 1963.

flA Single Channel Model of Attention-Sharing in a Dynamic Dual Task Environment f I . AnnuSrl Conference on Manual Control., May 1972.

TABLE 1

SUBJECT DATA

3ubJect üge vJeight Height F'lying Vision Profession Rated Most

Experiel1.ce

Lbs. _{'I'otal Hours} Difficult

BM

33 160

5

18'1 3000 20/20 CF-Pilot Visu<:' .. l (3'1'1) lJN 21 135 516" 265 20/20 YU\SC/Stud- _{Vlsual (3Tl)} ent D'r 24 138 513" 70 20/20 L _{PhD-Stud- V isual (ST1)} 20/60 R _ent Kt( ₃₀ 188 5111" 2100 20/20 CF-Pilot Visual (STl

DS

43 1'77

6

10" 5300 20/20 CF-Pilot Visual (s'n) ?l ₃₄ 170 5'110" 3500 20/20 CF-Pilot Audio (ST2) .... ..- ~N.,"'''''''·''~·'-_ ... ~_ ...

DT

,

X

P

E

_TR.ST~·

R

I

M

~

T

A

L

C

0

N

TR+STl

D

I

T

2

TR ONLY

TABLE 2

EXPERIMENTAL DESIGN

DM

PZ

BM

TR~STl

TR+ STl

TR ONLY

TRt5Te

TR ONLY TR ....

511

-IR ONLY

IR"*' sT2

TR",sT~

KK

DS

'tH·

STa

TR ONL'

TR ONLY

TR+STZ,

TR'" sTl

TRtsn

65 60 T 55 R A ₅₀ C K I 45 N G S 40 C 0 ₃₅ R E 30 25 20 15 TRACKING ONLY BM DM KK DT DS PZ TRACKING + TRACKING +

SECONDARY TASK 1 (VISUAL) SECONDARY TASK 2 (AUDIO)

DM DT BM PZ KK DS BM DM DT KK DS PZ

Subjects AVERAGE TRACKING SCORE

SUBJEcij

( ALL PZ DM BM DT ! KK DS

TABLE 4

PILOT MODEL PARAMETERS

Y (jw) = K

e-j~T

+ ;) TL

~w

+ 1 P P TI JW + 1 (T_N jw + 1) 1

«~)2

+

2~n

jw + 1) ExPT

I.l

K _{'t' ex} I/TL. OND ITION' P

ONLY

2.51026 0.17821 0.21604 0.51155

ST 1

2.98563 0.19051 0.12999 0.63268

ST 2

1.22368 0.15866 0.16961 0.04324

ONLY

2.55123 0.16192 0.28898 0.62020

ST 1

3.34051 0.15742 0.19313 0.67898

- -

-

-ST 2

2.16394 0.16803 0.26714 0.67783

ONLY

3.28163 0.10297 0.29037 0.68221

ST

1

4.49887 0.11453 0.02297 0.92719

ST 2

3.46801 0.10724 0.29121 0.73454

ONLY

3.72190 0.11755 0.28256 0.90154

-ST 1

3.99343 0.12265 0.25990 1.03137

ST 2

4.43811 0.10773 0.09685 0.97932

QNLY

2.00696 0.14959 0.26497 0.55537

ST 1

2.13857 0.13664 0.32069 0.59689

ST 2

2.35723 0.14635 0.24641 0.65655

ONLY

2.33152 0.13604 0.21430 0.73216

ST 1

2.17978 0.13180 0.13862

_-

_{_}

_.

_{_--}

0.77548

ST 2

2.39662 0.13688 0.21575 0.82744

ONLY

1.93829 0.15131 0.26841 0.69754

ST 1

1.80465 0.15998 0.27354 0.68469

ST 2

1.8451'1 10.17470 0.23221 0.67032 W N WN

I/TI 1/'1',.

_WIl

_Çn Fitting Trackin;J Averagel

Error Score 0.75546 67.6769 18.5841 0.08672 0.11410 41.338 0.72942 83 .5703 18.3297 0.07712 0.11231 49.604 ~ 0.12903 29.6464 17.4978 0.07981 0.12189 42.410 0.94215 27.3886 17.7042 0.02609 0.35271 47.526 0.76489 31.2776 17.5783 0.04693 0.29573 49.563 1.07469 29.568.2 16.5070 0.03535 0.31142 49.754 0.98181 12.1486 17.3804 0.00071 0.44354 37.969 0.84878 13.4525 17.2951 0.00645 0.33103 43 .884 ~ 0.92872 13.4541 17.6611 0.00059 0.31512 36.291 0.99700 16.5563 18.3589 0 .• 00090 0.22537 35.376 1.03320 15.7718 18.4522 0.00177 0.27315 48.443 ~ 0.92670 14.1163 18.4813 0.00088 0.28663 34.818 1.02473 16.1280 16.0616 0.10362 0.19665 41.458 1.07423 11.8836 16.2326 0.08042 0.26442 45.834 ~ 1.11122 13.8662 16.4177 0.07939 0.22945 40.498 1.17339 12.7098 2j .4983 _q~)D980 0.20752 40.273 f-- -1.24120 11.0819 19.2708 0.26845 0.21855 ~ ... -- ._--_._- 51.126 I' 1.34347 10.3020 21.6343 0.23870 0.18819 44.966

I,

1.26910 32.5538 15.6004 0.23481 0.20311 45.424 1.22845 38.6611 14.0797 0.23075 0.30454 63.362 -

...

1.31164 65.9913 14.7315 0.22328 0.22874 48.131

:ft

SIGNIFICANT DIFFERENCE AT 95% LEVEL

Fo

Fu

i

Ior;;LAY

I

,

I

,-I

I

- -

-HUMAN OPERATOR

I

Equalizat~on

Characteristics

-1

I

I

I

'

t ·

I

~tkk

, S t

Error

f

~lmb ~ynamics

Limb

______

+-~r\.--4_e~(-t~)~~.~

Applled

Fo~ce

I

Position

• r,f"" _ I

I ,.

--:...

-I

~

,

I

I

Operator

Vehlcle

outputl

Control

I

I

Dynamics

Dynamics

1

_ _ _

I

,

t

j~

c

I

~

I

I

· '

I

'.

:

.

,

I

I

'

.

.

L

,

I

<"

...:..._---~

;~

"

Figure 1

Single-Loop Manual Control System

". .

i (t)

..

p j

iet)

_...

..

~ Y (s)

St' k

~c

A(s)

e (t)

_{Output o (t)}

VEHICLE

met)

..

Pilot

&

..

..

-

_Stick

DYNAMICS

.

Dynamics

Figure 2

Compensatory Task: physical Situation

n (t)

e (t)

_1"

o(t)

_{m (t)}

Y (s)

-

-

_A(s)

'

..

-

'

..

_•

Figure 3

Linear Model for the Compensatory Task

SYSTEM

OUTPUT

i(t)

...

-..

~

~-..

...

-~ Figure 4

TWO PART LINEAR MODEL OF

THE COMPENSATO~Y TASK

o

Jet)

..

y (s)

..

A( s) -,

t

n (t) ~

,

~p (t)

-

A(S) y (s) •

..

..

.

-STICK

RANDOM NUMBER DISPLAY AUDIO TASK

GENERATOR r

..

~

«

,-6-

TAPE RECORDER

ÇJ

Di5PL \Y EI..El: h"Rott I (.!)

"

AID C0q.NVERTER _~

,

DIGITAL TAPE , ...

-

""

IINPUT TAPE RECORDKR

ANALOG COMPUTER

--

~ RECORDER ,

-...

-..

..

". MONITORING STATION EQUIPMENT LAYOUT

Figure Sa

I -:::> 0 >- ..0

:5

ll"I Q) l - 1-1 :z:: ::l w tT> :E OM 0... ~ :::> Ol w

Average Eye Position

'\

Ad.iustable Seat

Fore and Aft Up and Dm-m Floor __ -4~----/Throttle Control

p

. . . Stick Fo:-ward '>"

.

,

..-..

Stick Aft .q":t4

Bearing and Potentiometer Housing

~/r!

55:.J

f

" 11 ~O

•

Ij

f. ~ ~~!~,-'---~~~ COCKPIT SCHEHATIC Figure 6b Nixie Tube ~lll ' Scope Face "

'

36

T

+., 5 vn.

Error1

f3\

N'?-

Nixie

Tube-~

Random Number Display

~

o

Target Circle - 0.12 in

Aircraft Position Indicator

B.ln

.

Stationary Display

1

OU\L· - - - . . -...

Compensatory Display Figure 7

, ) .. ,.040

1

±

.040 .315 NO.!"'. OEP1't{ Ot IJ U t<\E.RÁ LS. Figu+,e, 8 .

1.0r---~~--_r---~----ï

-

o "':::t

.et

_...

-3-

_....

....

t9t 0.1~---+_---_4---~----~

M

0.001~----~--~~~~~~~----~---L~~~_L~~

__

_ L _ J

0.1

1.0

10.0

~

radiansl sec.

Input Power Spectra

Input (EEC02)

iet)

Error (EEC01) Output (EEC03)

r---~.--_,.

I

Linear System

I

t

to be Identified

I

I

e(t)

10

oCt)

I

I 1

10

I

r

,

I

I

.3

:

I

_ _ _ _ _ _ _ _ -1

ANALOG PILOT

Figure lla

Figure llb

ANALOG PILOT IDENTIFICATION

soa

1

r1EASURED VALUE

100 - ACTUAL Y = ~ DEG.I'N. S+3 10 100

~a

_ " f t C _ _I T I I T T T

o

-100 ~ ~ 6

Q-§

~

-200 -; -3]0 -400

-soa

0 5 10 15 20 ~C 1·0 _{f 0:" ...} --n-.I".I' ~ 2'.1' q 't

q

~

ft

f

±

I

I

tI

1'.1'.1' 0·8

î

~ H f- _0·5

~

0·4 0·2 0·0 0 5 10 15 20 RA~

Figure 12

HUMAN PILOT DESCRIBING FUNCTIONS

WITH SECONDARY TASK

~ Z H

~

100

~

₁₀ H

~

>-~ ₁ 100 0 _ -100

§

'=tv _ -q:y ~ -dX) ':" -DJ r l - - -1'1'",11 T -400 I ="'<.11 _~ LI~~~~~~~~~~~~~~~~~~~ 1-0 0-8 t:i

~

0-6

~

0-4 0-2 0-0 o o 5 ~ 5 ~ 10 15 20 TRACKING ONLY • I • i , 10 15 20

1

MEASURED DATA tI" AVERAGED

1 . i rOVER 6 RUNS

FITTED PILOT MODEL-21po,."ts

-"Spo'''''''

o

5 10 15 20 ~ SEaHJAR'( TPSK 1 (v19.W.J I

i"

l

i

I' , , , , , , , , , , , , , , , , , ---'- ___ -' i • • • • • • • • • , • • • o 5 ~ 10 15 20 o o I· I I I 5 RAOI!E 5 ~ 10 15 20 !WJ~ TPSK 2 (fW(() 10 15 20 SUBJECT - OT - 6 runs

Figure 13

HUMAN PILOT DESCRIBING FUNCTIONS

WITH SECONDARY

TASK

_ 5CX) .1 . . . I Z H ~100 "T~ ~ )1

~

i

10 l h""'''' 1 1 'l,f-j ~ 1 ~ I J 100 o rl ---~ - -100 I1 ~ ... b .

~ -~

~ -3)) I I IlT T I -400 I +1 -5CX) o 1·0 0·8

Ei

~ 0·5

!3

0·4 0·2 0·0 I I

I

I I 5 RAD.l9::C 10 15 TRACKING ONLY _---L_---'-_----'-_-'- ~ ~ • • • • • • • • • , , • 20 o I! h 5 RIIOI9I

1

MEASURED DATA tI" AVERAGED

rOVER 6 RUNS

FITTED PILOT MODEL-21","t.

- :\S pe"'t~

10 15 20

SECONDARY TASK 1 (VISUAL>

o I 1·1 1

1

5 RIIOI9I 10 15 20 ~(lJ1)W( TAS!< 2 (fjJ)!o)

500 Z H

§

100

~

₁₀ H

~

>-L ₁ 100 0 - -100

~

~ -axJ ,.!- -3Xl -400 -500 1·0 0·8 ti H I - _0·5

i

0·4 0·2 0·0 0 0 5 RAIY.IT lil!!! 5 RAIY.IT 10 15 a:J TRACKING ONl Y 10 15 a:J Figure 14

HUMAN PILOT DESCRIBING FUNCTIONS

WITH SECONDARY TASK

11

î

MEASURED DATA t 1<5 AVERAGED OVER 6 RUNS

- - FITTED PILOT MODEL

o 5 10 15 a:J RAIY.IT SEaID1RY TfSl< 1 <V19W) o 5 RAIY.IT l i l

I

11111 o 5 RAIY.IT 10 15 a:J o 5 RAIY.IT 10 15 a:J SEDJD'RY TPSl< 2 (lWIO) 10 15 a:J SUBJECT - DM - 6 RUNS

=a:J Z

@

100

~

₁₀ H

i

~ ₁ 100 0 _ -100

~

~ -al] ~ -:DJ -400 -=a:J 0 1·0 i 0·8 25 H I - _0·6

!

0·4 0·2 0·0 5 RAIJ/!:IT ,

!

I ! I I 10 15 T1WJ(HI; lJt.y Figure 15

HUMAN PILOT DESCRIBING FUNCTIONS WITH SECONDARY TASK

20 0

f

MEASURED DATA OVER 6 RUNS ± lOS AVERAGED

- - FITTED PILOT MODEL

5 10 15

RAIJ/!:IT SE~ TASK 1 <VIruv

al o 5

~

10 15 20

9)() Z H

@

100

~

i

10 ,:- 1 100 o _ -100

§

~

-200 ,:- -3X) -400 -500 1·0 o 5 RAC0.ll: 10 15 TIWl<ltlj IJllY

i

ti.

I 0.8 I

-

_IT~--

I

0·5 11 • I i \1 ! \1 0.4 I i • tl i I T I Figure 16

HUMAN PILOT DESCRIBING FUNCTIONS

WITH SECONDARY TASK

20 o

II

h

5

î

MEASURED DATA! 1 ~ AVERAGED

OVER 6 RUNS

- - FITTED PILOT MODEL

10 15 20

RAC0.ll: SECONDARY TASK 1 (VISUALl

o 0·2 I \ I 11111 I I i TI 0.0 M o 5 RAC0.ll:. 10 15 20 o 5 RAC0.ll: 10 15 20 o I i I 5 10 15 20

~L SECONDARY TASK 2 (AUDIO)

5

RAC0.ll:

10 15 20

:m

z

H

§

100

~

₁₀ H Figure 17

HUMAN PILOT DESCRIBING FUNCTIONS

WITH SECONDARY TASK

+

-

,

~

~

..

r

• • ! § ! • ~ • , • f r! tel ~Ï i

!1

!

I

t i

I

~ .; ₁ 100 0 _ -100 hl"....

~

~ -200 .; -300 I 1 -rt~ -400 I - - - I -:m L I ~~~~~~~~~~~~~~~~ o 1·0 f I!. I 0·8 t5 H

i:::

0·2 5 RAOI9I 10

TRACKING ONLY

15 20

, • , • , , , , • • ---'- I • • • • ,

o

! f I

I

I

MEASURED DATA! 16 AVERAGED

rOVER 6 RUNS

FITTED PILOT MODEL

5 10 15

RAOI9I SEC!JmR'( TPS< 1 (VI9WJ

20 o 5 RAOI9I IH

§!

11

10 15 20 ~:mlMRY TPS< 2 <tWIO)

::00 Z H

~

100

~

i

10 >-L 1 100 0 - -100

~

~ -~ >-L -3X) -400 -::00 0 1·0. 0·8 ~ H

i

0·6 0·4 0·2 0·0 0 I IJ I 5 RAOI$: I 5 RAOI$: 10 15 20 lWOOrti (JU 10 15 20 Figure 18

HUMAN PILOT DESCRIBING FUNCTIONS WITH SECONDARY TASK

o

tI!

o

I

MEASURED DATA i 16 AVERAGED

r

OVER3"6 RUNS

- - FITTED PILOT MODEL

5 10 15

RAOI$: SECONDARY TASK 1 (VISUAU

5 RAOI$: 10 15 20 20 o o

•

5 10 15 20

RAOI$:SECONDARY TASK 2 <AUDIO)

5 RAOI$:

10 15 20

Figure 19

RATIO FITS AND STATISTICAL ANÀLYSIS

9:)·0 9:)·0 0 H _10·0 f-~ 0 H 10·0

~

~

_1·0 H

~

I) 1;1 Q g .... ~ f-1·0 H

~

~ >=' 0·1 '< >=' O·i 200 200 lOC 100 f- 0 U- 0 0 1;1 1;1 co Da H Gi f- a u-H Gi 0 0 0 0 0 0 0 0 0 0 °

~

-100

~

-100 ~ _-200 >=' '< -200 >=' 0 RATIO OF TR ONL V/ TR+ ST

-3JO f - It' SIGNIFICANT DIFFERENCE AT 957. LEVEL _ _-:DJ

-400 -4JO 0 5 10 15 20 0 5 10 15 20 RAO/~C RA[].,qI 0·4 0·4

~

0·2 ~ 0·0 . . 0 o 0 o 0 ° H f- .... o ... a ....

~

-0·2 -0·4

~

0·2

..

....

.-..

or .- 0".-or ° .r ~ 0·0 "' ... '" 0 ° H

f-~

-0·2 -0·4 -O·G -0·5 0 5 10 15 20 0 5 10 15 20

RAD.I'H: RArv.oE"C SUBJECT - DT - 6 RUNS SECONDARY TASK 2 (AUDIO)

Figure 20

RATIO FITS AND STATISTlCAL ANALYSIS

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Figure 21

RATIO FITS AND STATISTICAL ANALYSIS

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RAlv.TI: ~ SUBJECT - DM - 6 RUNS

SECONDARY TASK 2 (AUDIO)