COGNITIVE STYLE AND LEARNING: PERFORMANCE OF
ADAPTORS AND INNOVATORS IN A NoveL DyNAMIC Task

The Federal Aviation Administration (FAA) is
charged with maintaining the U.S. airspace—a vast
and increasingly complex transportation system.
Highly trained air traffic control specialists (ATCSs)
form the crux of the web of radar, computers, and
communication facilities that ensure the safety and
efficiency of the system (Broach & Brecht-Clark,
1994). As part of ongoing development, new automa-
tion will continue to be implemented into the next
century to assist ATCSs, and human interaction with
technology is a vital component of this system.
Hollnagel, Cacciabue, and Hoc (1995) described the
combination of humans and technology to accom-
plish particular functions as joint systems.

Although both humans and technology are neces-
sary elements of joint systems, the person in charge of
controlling the system has responsibility for the
system’s performance (Hollnagel et al., 1995). This is
of little interest in well-bounded situations where
actual situations match expected conditions. In these
situations, rule-based technology handles the human
operator’s routine tasks extremely well. However,
Hollnagel et al. note that the true flexibility of joint
systems emerges from the performance of the human
operator, who shapes the response of the joint system
to real-time unexpected events.

Despite the criticality ofhuman performance in joint
systems, little research has been conducted to identify
desirable individual characteristics beyond knowledge of
the system itself (cf. Chidester, Kanki, Foushee,
Dickinson, & Bowles, 1990; Hoc, Cacciabue, &
Hollnagel, 1995; Zsambok & Klein, 1997). One area
receiving some attention in the technological environ-
ment of air traffic control is the relationship between
personality variables and successful human performance.

Personality

Collins, Schroeder, and Nye (1989) assessed the
role of anxiety in the prediction of successful perfor-
mance using the State-Trait Personality Inventory
(STPI; Spielberger, 1979). The STPI is a self-report

instrument measuring the personality dimensions of
Anxiety, Curiosity, and Anger; scores on the STPI
Anxiety subscale predicted individual training out-
comes. Specifically, for students tested upon entering
the en route air traffic control option at the FAA
Academy, higher Anxiety scores were related to in-
creased likelihood of Academy failures/withdrawals,
option switches, and field attrition. Also using the
STPI, Nye and Collins (1991) found that, for stu-
dents entering the Academy’s nonradar screen pro-
gram, higher pass rates were associated with those
students with higher levels of Curiosity and lower
levels of Anxiety and Anger.

Schroeder, Broach, and Young (1993) reviewed
several past studies that examined relationships be-
tween personality variables, ATCS training, and on-
the-job performance. The authors proposed that the
conflicting results demonstrated the need for research
based ona unifying, psychometrically sound model of
personality. Therefore, they employed the Five Factor
Model of Personality (NEO-PI; McCrae & Costa,
1990) to examine the personality characteristics re-
lated to the choice of ATCS as an occupation and
successful prediction of performance. The NEO-PI
Inventory is comprised of five primary subscales:
Neuroticism (e.g., calm vs. worrying), Extraversion
(e.g., reserved vs. affectionate), Openness to experi-
ence (e.g., uncurious vs. curious), Agreeableness (e.g.,
suspicious vs. trusting), and Conscientiousness (e.g.,
lazy vs. hardworking). Based on their analyses,
Schroeder and his associates concluded that air traffic
students enrolled in the Academy's ATCS Nonradar
Screen program had lower than average scores in
Neuroticism and higher than average scores in Extro-
version, Openness to Experience, and Conscientious-
ness, compared with normative samples. Further, they
demonstrated that incorporating personality variables
into the selection procedure would result in signifi-
cant savings to the agency’s training resources by
reducing the failure rate of trainees through better
initial selection.


Practice Trials

The practice sessions served to further familiarize
those participants who passed the screen with the rules
and procedures of the air traffic control task and to
accustom them to working in the three levels of
scenario difficulty, one of which they would be as-
signed to during the experimental session. The three
trials progressively increased levels of scenario diffi-
culty from low to high.

To determine whether cognitive style influenced
performance with practice and scenario difficulty, a
mixed design, general linear model analysis of vari-
ance was performed. Between-subjects variables were
Cognitive Style (Adaptor, Innovator), Shipley subscale
scores (Abstract, Verbal) and CGF score. The within-
subject variable was Level of scenario difficulty. Aver-
age individual performance during each practice
scenario was the repeated dependent measure.

Analysis showed a significant effect for Abstract
(F, .9= 32.07, MSe= 4.39, p<..0081) on performance.
The analysis revealed no significant effects for Cogni-
tive Style, Verbal, or CGF. Based on inspection of the
means, it appeared that Level of scenario difficulty
had an effect on performance. However, performance
scores showed a wide range of variability. Average
performance scores observed during the practice sce-
narios ranged from 0 to 12, with means (and standard

deviations) over the three increasing levels of scenario
difficulty of 1.14 (1.25), 1.98 (1.84), and 2.75 (2.22),
respectively. The within-subject variable of increasing
scenario difficulty showed no statistical effect on
individual performance (p<.23, ?*= .004). No interac-
tion effect appeared between Level of scenario diffi-
culty and Cognitive Style. These results suggest that
the practice sessions served the purpose of familiariz-
ing participants with the increasing levels of scenario

difficulty.

Experimental Trials

A mixed design, general linear model analysis of
variance was performed. Between-subjects variables
were Cognitive Style (Adaptor, Innovator) and Level
of scenario difficulty (low, medium, high). The inter-
action between Cognitive Style and Level was also
tested. The within-subject variable was Trial. Shipley
subscale scores (Abstract, Verbal) and CGF score were
included as covariates. Average individual perfor-
mance during each experimental trial scenario was the
repeated dependent variable.

Analysis revealed no effects of the variables of
Cognitive Style, Abstract, Verbal, CGF, or Level of
scenario difficulty on performance. Analysis showed
no interaction between Cognitive Style and Level of
scenario difficulty.

Scenario Difficulty
Low Medium High

0
:
5
= = =oe oT
g 4 }-------------------------------+-------
s
o
a

6

Figure 2. Performance During Practice With Varying Scenario Difficulty. Note
that lower scores represent better performance.


Cognitive Capacity

Cognitive style has often been confused with other
measures of cognitive capacity, such as intelligence
(Kirton, 1994). An individual’s capacity for thinking
has long been of interest to researchers in many fields
(i.e., intelligence, creativity, cognitive complexity). A
variety of testing instruments has been developed that
attempt to measure “how well” or “how much” a
person can think.

Intelligence is generally defined as the ability to
profit from experience, and tests to measure types of
intellectual capacity abound. A general characteristic
of these tests is that they categorize an individual
according to whether he or she performed better or
worse relative to others taking the test and use prede-
termined criteria. This is in contrast to Kirton’s
conceptualization of cognitive style, which does not
distinguish between which style is better or worse than
another.

Despite the long history of intelligence measure-
ment, however, the relationship between intelligence
and performance has not been completely specified
(Perkins, Farady, & Bushey, 1991; Wagner & Oliver,
1996), particularly for tasks such as air traffic control
which require situation modeling and updating, al-
though a minimum level of intelligence would be
expected to be necessary to manage complex cognitive
tasks (M. J. Kirton, personal communication, July
21,1998). In addition, other variables have also been
associated with performance outcomes, such as rea-
soning ability (Voss, Perkins, & Segal, 1991), famil-
iarity with the problem (Alba & Hutchinson, 1984;
Klein, 1993), and task load (Salas, Driskell, & Hughes,
1996).

Therefore, the purpose of this study was to examine
whether a person’s cognitive style would influence
performance outcomes in tasks resembling air traffic
situations as compared with cognitive capacity, prob-
lem familiarity, or task difficulty. A laboratory-based
study was conducted to identify whether cognitive
style was a concept likely to be fruitful for subsequent
research with incumbent air traffic personnel and
trainees and to reduce the initial organizational costs
associated with personnel selection and training.

RESEARCH QUESTION

This project tracked performance across several
learning stages to determine whether cognitive style
would influence performance outcomes in a complex

dynamic task. Air traffic control is a system of stipu-
lated control procedures and organization, with sys-
tem goals of safety and efficiency. In this environment,
human judgment can have a significant effect on
system goals. Kirton’s Adaptor-Innovator theory pre-
dicts that Adaptors would perform better than Inno-
vators within a structured, highly proceduralized
environment. Specifically, participants who score to-
ward the Adaptive end of the scale will perform better
(e.g., make fewer procedural errors) during a dynamic
air traffic scenario, relative to those participants who
score toward the Innovator end of the scale through-
out screening, practice, and task performance.

METHOD

Participants

Two hundred high school graduates were recruited
through a local temporary help provider and were
compensated for their participation. The provider
screened for age, normal color vision, computer skills
(ability to use a mouse), high school degree, and
aviation experience. Participants were selected so that
males and females were equally represented.

Materials

Three pencil-and-paper self-report measures were
used: the KAI instrument as a measure of cognitive
style, the Shipley Institute of Living scale as a measure
of cognitive capacity, and a two-item assessment of
familiarity with computer games.

Measure of Cognitive Style. The 32-item Kirton
Adaptation-Innovation Inventory (KAI) was used as
the measure to locate respondents on the adaptiveness-
innovativeness continuum. The participants indicated
how difficult each described behavior was for them to
maintain consistently. Participants responded by put-
ting an X in a blocked area labeled as either: “Very
Hard,” “Hard,” “Easy,” or “Very Easy.” After adjust-
ing for reversed scoring of some items, high scores
reflected preference for innovativeness and low scores
a preference for adaptiveness.

Measure of Cognitive Capacity, General intellec-
tual functioning was measured by the Shipley Insti-
tute of Living Scale composed of two sub-scales: a
40-item test to assess vocabulary and a 20-item test of
abstract thinking (i.e., inference, pattern recogni-
tion). This scale has been demonstrated to be
uncorrelated with cognitive style as measured by the

KAI (Kirton, 1994).


These studies demonstrated that personality vari-
ables can be related to performance outcomes. How-
ever, because the real world is full of unexpected
events, it is important that the human operator in a
joint system have the ability to appropriately solve
problems and to adapt to changing situations. Thus,
desirable dimensions of ATCS performance might
also be described by one’s cognitive style, or one’s
preferred approach to problem solving.

Cognitive Style

Cognitive style describes one’s preferred “manner”
of problem solving. It is a stable preference linked to
one’s overall personality and is conceptually different
from one’s capacity for problem solving (“how much”).
However, the relationship between cognitive style
and performance in air traffic control tasks has re-
ceived little attention.

Kirton (1976) developed a theory of cognitive style
that focused on people and their changing environ-
ment. He proposed that one’s manner of dealing with
problems and uncertainty is distinct from one’s ca-
pacity to deal with problems (e.g., IQ, level of creativ-
ity, level of complexity) and from learned techniques,
such as the use of brainstorming or memory aids to
find solutions. That is, how one reacts to novel situ-
ations that require unique or creative solutions is
largely influenced by one’s preferred cognitive style.
Potentially, one’s style would influence performance
outcomes, including an individual’s efficiency and
effectiveness, interactions between individuals, and
the outcomes of those interactions in terms of safety
and efficiency.

Kirton’s theory defines a continuum of behaviors
ranging from extreme adaptation to extreme innova-
tion. The framework is value-free. No position can be
described as being better or worse than another. To

Table 1. General characteristics of Adaptors and Innovators

identify one’s cognitive style, Kirton developed the
Kirton Adaptation-Innovation Inventory (KAI), a
32-item inventory that produces an overall score for
the individual completing the inventory. The range of
possible scores yields a continuum on which no loca-
tion is praiseworthy or censurable. Each score is asso-
ciated with a range of characteristics that can be
perceived as advantageous or disadvantageous de-
pending upon the rater, the nature of the problem,
and the nature of the setting, group, or outcome. A
strength in one situation could be a weakness in
another. While both Adaptors and Innovators are ca-
pable of generating original, creative solutions, they will
usually do so in their own different, characteristic ways.
An important point to note is that Adaptors and
Innovators can be described either by their KAI score’s
position relative to the range of possible scores or by
their score’s position relative to the score of another
person. Thatis, relative to the range of possible scores,
a person scoring at the high end of the scale would be
classified as an Innovator and a person scoring toward
the low end of the scale would be labeled an Adaptor.
However, relative to another person’s score, someone
scoring towards the low end of the scale can still be a
relative Innovator, compared with someone whose
score was even lower on the scale. For the present
purpose, Adaptors and Innovators were conceptual-
ized in an absolute sense and categorized by their
relationship to the range of possible scale values. This
method, although creating an arbitrary dichotomy,
allows greater contrasts to be made. Table 1 contrasts
the characteristics of the two cognitive styles.
Kirton (1994) noted that those toward the Adap-
tors end of the continuum are more “at home” within
systems, valuing precision, reliability, and efficiency.
Thus, the person best suited to work within estab-
lished structures reduces conflict, minimizes risks,
and “solves problems by
proceeding at a disci-
plined pace ina predict-

Epes

able direction” by using

Make it better

Use existing rules, methodical

Very efficient & disciplined

Seek consensus

Prefer defined problems

Work inside constraints

Provide balance for Innovators’ wild ideas
Can cling to the paradigm too long

Provide balance for Adaptors’ reliance on structure
‘Can abandon the paradigm too soon

a set of reliable strate-

Make it different | _ gies to handle recurring

Challenge rules problems (pg. 13).

u Unconventional | However, a weakness of
nooner me consensus Adaptors is that they can
Disregard traditicnal ways fail to recognize when

their reliable strategies
are not appropriate for
a new problem.



RESULTS

Data from 184 participants who passed the screen
were analyzed. Observations with missing values were
not included in the analyses. The relationship between
cognitive style and cognitive capacity was to determine
if prior findings of no relationship were replicated in this
sample. No relationship was found between KAI scores
and scores on the Shipley Institute of Living Scale (r=
.035, p <.64, n= 181). Moreover, no relationship was
found between KAI scores and either of the Shipley’s
subscale scores. This result supports Kirton’s (1976)
premise that cognitive style is orthogonal to (i.e., distinct
from) cognitive capacity.

Two individual performance scores that were mea-
sured during each session (losses of separation and num-
ber of procedure-following errors) were used as dependent
measures in analyses of the screening, practice, and
experimental trials. Each participant’s number of separa-
tion losses and procedure-following errors were averaged
for each trial to generate an overall individual perfor-
mance score for that trial. For all analyses, higher nu-
merical scores represented poorer performance. The
distribution of the KAI scores was trisected. Scores in the
lowest third were nominally labeled the Adaptor group
(n=58) and those in the highest third were labeled as the
Innovator group (n=G60). A significance level of p<.05
was adopted for all analyses.

Screen

To determine whether cognitive style influenced
performance during the screening, a mixed design,
general linear model analysis of variance was per-
formed. Between-subjects variables were Cognitive
Style (Adaptor, Innovator), Shipley subscale score
(Abstract, Verbal) and CGF score. The within-subject
variable was Trials; average individual performance
during each screening scenario was the repeated de-
pendent variable. Average performance scores ob-
served during the screening session ranged from 0 to
8, with means (and standard deviations) over the three
screening Trials of 1.91 (1.54), .86 (1.11), and .56
(.93) respectively.

Independent variables Cognitive Style and CGF
score did not have significant effects on performance
in the screening session, while the Shipley Verbal (F,
101 = 10.56, MSe = 2.29, p<.0016) and Abstract Fist
= 6.73, MSe = 2.29, p<.0109) subscale scores were
significantly related to performance during this task.

Performance over Trials was significant (Fy, 302 =
12.88, MSe = 1.10, p<.0001), as well as the Verbal x

Trials (F, 49, 7-10, MSe = 1.10, p<.001) and CGF
score x Trials (F, ,,,= 4.32, MSe = 1.10, p<.015)

interactions. The Cognitive Style x Trials and Ab-
stractx Trials interactions were not significant. Figure
1 shows the general improvement in performance for
both Adaptors and Innovators.

Screening Trials

Mean Performance
we

= +o =]

Figure 1. Screen Performance and Cognitive Style. Note that lower scores

represent better performance.


DOT/FAA/AM-99/12

Office of Aviation Medicine
Washington, D.C. 20591

Cognitive Style and Learning:
Performance of Adaptors and
Innovators in a Novel

Dynamic Task

Julia Pounds
Larry L. Bailey

Civil Aeromedical Institute

Federal Aviation Administration
Oklahoma City, Oklahoma 73125

April 1999

Final Report

This document is available to the public
through the National Technical Information
Service, Springfield, Virginia 22161.

@

U.S. Department
of Transportation

Federal Aviation
Administration


in response to varying levels of difficulty. However,
the practice trials did not reveal this, possibly due to
the tutorial nature of the task.

In the two 28-minute experimental trials which
required participants to repeat and sustain perfor-
mance for longer periods, performance appeared to be
influenced by cognitive style. Adaptors improved their
performance in the second trial, while Innovators’
performance declined.

Surprisingly, the level of scenario difficulty did not
show a statistically significant effect on performance
in either practice or experimental trials. At first, these
results seemed at odds with results of the scenario
calibration study conducted by Bailey et al., (in press).
However, examination of the means for the levels of
scenario difficulty revealed a pattern of performance
similar to the one demonstrated in the Bailey et al.
study, (i.e., increasing difficulty resulted in poorer
performance). Although differences might be attrib-
uted to the variability of performance, an alternative
explanation would be the differences in the types of
scores used to represent performance in the two stud-
ies. The present study used only the two individual
performance scores (i.e., separation violations and
procedure-following errors) to represent performance,
whereas Bailey et al. used a combination of individual
and team scores to represent overall performance.

In sum, these results suggest that people who score
as high Adaptors might be expected to perform better
than high Innovators in this type of procedure-bound
task, although the Adaptors did not dominate Innova-
tors throughout the entire continuum of learning.
Innovators appeared to initially outperform Adaptors
in each task. Further research might be able to clarify
this observation. Whether Adaptors would continue
to outperform Innovators over time deserves further
attention.

These analyses examined only individual perfor-
mance measures. However, research in other domains
has demonstrated that teams that were created based
on Adaptor-Innovator dimensions resulted in team
performance differences (Hammerschmidt, 1996).
Therefore, future analyses will use team performance
scores to examine whether cognitive style differences
in overall teams also hold for this type of dynamic
procedure-bound scenario.

Although the primary emphasis of this study was to
examine the influence of cognitive style on perfor-
mance in a dynamic task under varying levels of

difficulty, participants also provided subjective work-
load ratings. Future analyses will examine the rela-
tionship between scenario difficulty and subjective
workload ratings for Adaptors and Innovators. To
conclude, the question of whether Adaptors would
continue to outperform Innovators over time merits
additional attention relative to issues of maintaining
performance levels, workload, and team performance.

REFERENCES

Alba, J. W., & Hutchinson, J. W. (1984). Dimensions
of consumer expertise. Journal of Consumer Re-

search, 13, 411-54.

Bailey, L. L. (March, 1998). A look inside group dy-
namics: The role of group feedback. Presentation
at the Oklahoma-Kansas Judgment and Decision
Making Conference. Stillwater, Oklahoma.

Bailey, L. L., Broach, D. M., Thompson, R. C., & Enos,
R. J. (in press). Controller teamwork evaluation
and assessment methodology (CTEAM): A sce-
nario calibration study. Washington, DC: Federal
Aviation Administration Office of Aviation

Medicine.
Broach, D., & Brecht-Clark, J. (1994). Validation of

the Federal Aviation Administration air traffic
control specialist pre-training screen. (DOT/FAA/
AM-94/4). Washington, DC: Federal Aviation
Administration Office of Aviation Medicine. Avail-
able from: National Technical Information Ser-
vice, Springfield, VA 22161. Order#ADA277549.

Chidester, T. R., Kanki, B. G., Foushee, H. C.,
Dickinson, C. L., & Bowles, S. V. (1990). Person-
ality factors in flight operations: Volume 1. Leader
characteristics and crew performance in a full-
mission air transport simulation (NASA Tech.
Mem. No. 102259). Moffett Field, CA: NASA-
Ames Research Center.

Collins, W. E., Schroeder, D. J., & Nye, L. G. (1989).
Relationships of anxiety scores to academy and
field training performance of air traffic control
specialists. (DOT/FAA/AM-89/7). Washington,
DC: Federal Aviation Administration Office of
Aviation Medicine. Available from: National Tech-
nical Information Service, Springfield, VA 22161.
Order #ADA209326.


Trial

Mean Performance
Ww

—o—A

= «o8 =|

Figure 3. Cognitive Style by Trial Interaction. Note that lower scores represent better

performance.

Tests of the within-subject effects revealed a main
effect for Trial (F, ,, 10.38, MSe = 5.68, p<.0018).
Performance generally improved from Trial 1 to Trial
2. Average performance scores observed during the
experimental scenarios ranged from 0 to 17.5, with
means (and standard deviations) over the two trials of
5.41 (2.93), and 4.98 (3.19) respectively.

Cognitive Style interacted with Trial (F, ,,= 6.82,
MSe = 5.68, p<.0107). Performance of Adaptors
improved over trials while Innovators’ performance
worsened (Figure 3). Trial x Level of scenario diffi-
culty and Trial x Cognitive Style x Level of scenario
difficulty were not significant.

CONCLUSION

Kirton’s (1976) theory of Adaptors and Innovators
suggested cognitive style would influence performance
and that Adaptors would outperform Innovators on
procedure-bound tasks. Thus, the purpose of this
study was to examine theoretical predictions about
the influence of cognitive style on general performance
when learning a complex, dynamic task. Participants’
overall performance was followed through screening,
practice, and experimental trials.

The hypothesis that cognitive style would influ-
ence performance was partially supported. Differ-
ences in cognitive style were statistically evident only
during the experimental trials. The general pattern of

results suggests that Innovators might have had an
initial advantage, although Adaptors eventually sur-
passed them in performance, either because Adaptors
generally benefited more from practice or because
Innovators were unable to maintain their initial level
of performance under continuing conditions.

During screening, while learning rudimentary task
skills, high Innovators were expected to have an initial
advantage. According to Kirton’s theory, novel situa-
tions favor Innovators. However, cognitive style did
not influence performance outcomes during this exer-
cise. Rather, performance appeared to be more strongly
influenced by participants’ familiarity with computer
games and by their abstract and verbal reasoning
skills. This suggests that, although Innovators may
have had some initial advantage because of the novelty
of the task, both Adaptors and Innovators developed
strategies for the task as they gained experience with
the basic procedures. Thus, although one’s cognitive
style might influence one’s strategies for performance,
in this situation, there was little differential impact on
performance during initial learning of the fundamen-
tals of the task.

In the practice sessions, abstract reasoning skill,
rather than cognitive style, influenced performance.
The practice session allowed everyone who passed the
screen to rehearse the rules of the scenarios under
increasing levels of scenario difficulty. It is possible
that Adaptors and Innovators performed differently


Assessment of Computer Game Familiarity (CGF).
To assess task familiarity, participants’ experience with
computer games was measured. Young, Broach, and
Farmer (1997) found that performance on the Air Traf-
fic Scenario Test, a computer administered low-fidelity
radar simulation, was correlated with computer game
experience. Specifically, people who had more experi-
ence with computer games also were better at routing and
handing off aircraft. Based on these results, the general
level of participants’ familiarity with computer games
was measured by a pencil-and-paper test. To assess both
attitude and frequency dimensions of familiarity (Alba
& Hutchison, 1984), participants were asked two ques-
tions: “How much do you like to play computer games
and other similar forms of entertainment?” and “How
often do you play computer games or other similar forms
of entertainment?” Participants responded on a Likert
scale anchored by 1 (not at all) to 6 (very much). ACGF
score was computed for each participant by using the
average of these two responses.

Scenarios

The Controller Teamwork Evaluation and Assess-
ment Methodology (CTEAM) research platform was
used to present the dynamic air traffic scenario tasks.
The device consists of four computer networked work-
stations. CTEAM was developed to capture impor-
tant characteristics of a four-sector radar-based air
traffic control environment, such as definite rules and
procedures (e.g., correct speed for landings), multiple
competing tasks, perceptual and cognitive require-
ments, and communications (Bailey, Broach, Th-
ompson, & Enos, in press). Each sector has two
airports and four exit gates, of which only two gates
connect with other CTEAM sectors.

Performance Metrics

Participants operated as part of a four-person team
and coordinated their activities with adjacent sectors to
negotiate the transfer of aircraft from one sector to the
next. CTEAM software automatically recorded the num-
ber of times separation was lost between two aircraft,
number of crashes, number of procedure-following er-
rors (e.g., not landing at the correct speed), and percent
of aircraft reaching their destination. A loss of separation
was recorded when two aircraft violated a “5 horizontal
miles” inter-aircraft or aircraft-sector boundary distance
rules. The software determined “miles” using screen
coordinates. A crash was recorded when two aircraft
collided or when one aircraft collided with a sector

boundary or an airport. Because separation was lost
between two aircraft before they crashed, number of
crashes was confounded with loss of separation. Further,
individual scores on percent of aircraft reaching destina-
tions were dependent on the other team members’ per-
formance, since the aircraft activated in one’s own sector
had to fly through the three other sectors to reach its
destination airport. Because crashes and percent reach-
ing destination were unclear measures of individual
performance, the two scores used to represent individual
performance were separation violations and procedure-
following errors. The average of these two measures was
used to represent individual performance in that scenario.

PROCEDURE

Screen

The screening procedure acquainted participants with
the basic procedures for successfully performing the task.
The screening procedure also eliminated from further
participation those candidates who could not grasp a
basic understanding of the procedures required to carry
out the air traffic control task (e.g., make appropriate
changes in aircraft direction, speed, and altitude). Par-
ticipants were given an orientation to the task and were
allowed time for practice to familiarize themselves with
use of the equipment, the airspace, and the command
procedures. Participants then completed three, ten-
minute practice scenarios and completed the Shipley
Institute for Living Scale. Participants who met specified
performance criteria were asked to return for the practice
and experimental sessions.

Practice Trials

One hundred and eighty-four participants who
passed the screen returned in groups of four for the
practice session. They reviewed the procedures and
completed three 10-minute sessions to practice the
procedures under three levels of scenario difficulty.
Levels of scenario difficulty were calibrated a priori by
Bailey et al. (in press).

Experimental Trials

Each group of four was randomly assigned to one of
three experimental conditions based on scenario dif-
ficulty (low, medium, high). Each team completed
two 28-minute experimental trials. Level of scenario
difficulty for each team was determined a priori,
remained the same for both trials, and was random-
ized across teams.




Technical Report Documentation Page

1. Report No.
DOT/FAA/AM-99/12

2. Government Accession No.

3. Recipient's Catalog No.

4. Title and Subtitle

Novel Dynamic Task

Cognitive Style and Learning: Performance of Adaptors and Innovators in a

5. Report Date

April 1999

6. _ Performing Organization Code

7. Author(s)
Pounds, J., and Bailey, L.L.

8. Performing Organization Report No.

FAA Civil Aeromedical Institute
P.O. Box 25082
Oklahoma City, OK 73125

9. Performing Organization Name and Address

10. Work Unit No. (TRAIS)

11, Contract or Grant No.

12, Sponsoring Agency name and Address
FAA Office of Aviation Medicine
Federal Aviation Administration
800 Independence Avenue, SW.
Washington, DC 20591

13. Type of Report and Period Covered

14. Sponsoring Agency Code

15. Supplemental Notes

16. Abstract

task.

This research investigated whether cognitive style (Kirton, 1976) influenced performance in a novel dynamic task
modeled on the task of controlling air traffic. It was hypothesized that participants who preferred a more adaptive style
of processing would show better performance than those who preferred a more innovative style. Problems were
presented to participants using computer-based scenarios in screening, practice, and experimental trials. Based on earlier
research, better performance was more likely when the participant adhered to the rules of the scenario. Performance was
measured by the number of times the participant violated two types of scenario rules. Screening, practice, and
experimental performance were analyzed separately. Cognitive style had a significant effect on performance only during
experimental trials. Performance of Adaptors improved across trials while that of Innovators did not. Results suggest
that Innovators, although perhaps having some initial advantage, may not be able to sustain performance in this type of

17. Key Words

Cognitive Style, Performance, Learning, Innovation,

18, Distribution Statement
Document is available to the public through

Reasoning, Practice, Air Traffic Control

the National Technical Information Service,
Springfield, Virginia 22161

19. Security Classif. (of this report)
Unclassified

20. Security Classif. (of this page)
Unclassified

21

No. of Pages

22. Price

12

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NOTICE

This document is disseminated under the sponsorship of
the U.S. Department of Transportation in the interest of
information exchange. The United States Government
assumes no liability for the contents thereof.


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