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Article in Per sonality and Individual Diff erenc es · Januar y 2004
DOI: 10.1016/S0191-8869(03)00108-9
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Eugenia F uenzalida
University of Oklahoma
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James B Hittner
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Luz-Eugenia Cox-Fuenzalida a,*, Rhonda Swickert b, James B. Hittner b
aDepartment of Psychology, University of Oklahoma, 455 W. Lindsey Street, DHT-705, Norman, OK 73019, USA bDepartment of Psychology, College of Charleston, 66 George Street, Charleston, SC 29424, USA
Received 25 July 2002; received in revised form 23 December 2002; accepted 7 February 2003
Abstract
This study manipulated workload levels to test Eysenck’s theory of neuroticism by examining indivi-
duals’ differential responses to the stress of sudden shifts in workload. A sample of 194 participants who
had completed an inventory to assess neuroticism were randomly assigned to either a High-to-Low or
Low-to-High testing condition. Participants performed an auditory vigilance task while reaction time and
correct responses were assessed. Multiple regression analyses were conducted and results indicated that
higher levels of neuroticism were associated with decreased reaction time in both conditions. In addition,
individuals higher in neuroticism evidenced fewer correct responses in the Low-to-High workload condi-
tion. Results of this study are generally consistent with Eysenck’s theory of neuroticism.
#2003 Elsevier Ltd. All rights reserved.
Keywords:Neuroticism; Workload history; Task performance; Stress
The concept of neuroticism has received considerable attention in the stress and coping litera-
ture as it is believed to be an important predictor of how people deal with stressful events.
Although many theories have been proposed to explain this dimension, one of the most widely
cited is Hans Eysenck’s theory.Eysenck (1967)defined neuroticism as a dimension that ranges
from emotional stability to instability and he suggested that individual differences in neuroticism
are a result of arousal differences that are mediated by the limbic system. The putative function of
the limbic system is to regulate autonomic and emotional reactions, in particular, reactions that
are cued by physically or psychologically stressful experiences (Eysenck, 1967). Consequently,
individuals who are high in neuroticism, compared with those who are low, are believed to evi-
dence greater activation of the limbic system when responding to environmental stressors. This
0191-8869/03/$ - see front matter#2003 Elsevier Ltd. All rights reserved.
doi:10.1016/S0191-8869(03)00108-9
Personality and Individual Differences 36 (2004) 447–456
www.elsevier.com/locate/paid
* Corresponding author. Tel.: +1-405-325-4511; fax: +1-405-325-4737.
E-mail address:[email protected] (L.-E. Cox-Fuenzalida). differential autonomic arousal level is believed to form the basis for the behavioral differences
observed between those scoring high and low in neuroticism.
Although behavioral differences across the neuroticism continuum have been examined using a
variety of approaches, one important strategy used to test Eysenck’s arousal-based theory
involves the examination of performance differences between high and low neurotics using com-
puter-based human performance tasks. Indeed, several studies have demonstrated that indivi-
duals scoring high in neuroticism (and trait anxiety) typically perform more poorly on a variety
of tasks compared with those low in neuroticism (Eysenck, 1983; Eysenck & Eysenck, 1985;
Spence & Spence, 1966). Typically, the detrimental effects of neuroticism are particularly evident
in studies using vigilance and monitoring tasks—tasks that require high levels of attention (for
reviews, seeEysenck, 1983; Matthews & Deary, 1998). For example,Newton, Slade, Butler, and
Murphy (1992)conducted a study with 123 participants that employed a visual search task
requiring participants to scan a random display of letters to determine the presence or absence of
a target letter. This study found neuroticism to be significantly related to slower reaction time in
the predicted direction, that is, high neurotics evidenced slower reaction times. Although some
studies have found an association between neuroticism and improved performance (Eysenck &
Calvo, 1992), generally, findings are consistent withNewton et al. (1992)suggesting that neuro-
ticism effectively predicts decrements in performance on vigilance tasks (Darke, 1988; Eysenck,
1992, 1997).
In addition to predicting simple vigilance and monitoring task performance, studies have also
demonstrated that neuroticism influences performance on other, more complex, types of tasks.
For instance,Mayer (1977)conducted a study manipulating task difficulty while examining the
effect of trait anxiety on easy versus difficult tasks. He concluded that although high trait anxiety
facilitated performance on easy tasks (requiring simple, rote problems—e.g. searching for the
letter ‘‘a’’), it had a detrimental effect on difficult tasks (e.g. anagram solving).
Although the studies cited earlier have established a link between neuroticism and task perfor-
mance, to date little is known about the relationship between neuroticism and basic performance
dynamicssuch as variability in workload, or changes in task demand. In other words, while pre-
vious research seems to be generally consistent with Eysenck’s arousal-based theory of neuroti-
cism, most of these performance-based studies lack generalizability because they do not examine
parameters associated with real-world work environments, such as changing workload levels. The
study of workload history may help to address this problem.
The study of workload history typically involves the manipulation of workload levels over time
with individuals moving from either high-to-low, or low-to-high, workload levels. Because per-
formance is examined in this manner, the study of workload history might be considered more
ecologically valid than less dynamic approaches (e.g. fixed workload level studies). Currently,
little is known about the effects of neuroticism and workload history on performance, however,
there is a body of work that has examined the manner in which workload shifts influence general
performance. One consistent finding reported in the workload history literature suggests that
there is a significant decrement in performance following a sudden decrease in workload level
(Cumming & Croft, 1973; Goldberg & Stewart, 1980; Matthews, 1986). In a recent study, how-
ever,Cox-Fuenzalida (2000)found that either a sudden decrease or increase in workload level
results in a significant performance decrement. One explanation for the performance decrements
following both types of workload shift is that any change in workload conditions might serve as a
448L.-E. Cox-Fuenzalida et al. / Personality and Individual Differences 36 (2004) 447–456 stimulus that induces stress. In fact, it has been widely discussed in stress literature that one
essential component of stress is change in environmental conditions (Holmes & Rahe, 1967;
Selye, 1956). Therefore, one might argue that the study of workload history creates the necessary
conditions for directly studying stress responses and, in so doing, allows for a direct test of neu-
roticism theory. Moreover, by manipulating stress in this manner (i.e. by changing workload
levels), this approach may represent a more ecologically valid procedure for testing Eysenck’s
theory of neuroticism than has been utilized by previous studies. Therefore, the purpose of this
study was to examine the association between neuroticism and task performance across two dif-
ferent types of changing workload conditions (Low-to-High versus High-to-Low). Given that
higher levels of neuroticism are believed to be associated with greater stress-proneness, and that a
sudden change in workload constitutes a stressful event, it was hypothesized that neuroticism
would be significantly positively associated with performance decrements following either a sud-
den increase or decrease in workload demand.
1. Method
1.1. Participants
Participants were 194 undergraduate students (103 females, 91 males) from the University of
Oklahoma. Participants were randomly selected from a larger pool of 450 lower division psy-
chology students who had participated in a pretesting session in which neuroticism was assessed.
Following the random selection procedure, participants were contacted by telephone and invited
to participate in the study.
1.2. Materials
A computer-based version of theBakan Vigilance Task (1959)was utilized. The Bakan is an
auditory vigilance task comprised of a sequence of digits presented to participants by way of
earphones. Each training and test trial was 3 min in duration. During each trial, participants were
instructed to detect odd–even–odd sequences of digits (the signal, e.g. 7–8–3). Participants were
directed to press a specified key on the computer keyboard when they detected a signal. A total of
10 signals were presented in each 3-min period among a string of random digits for the high and
low workload conditions (225 and 90 digits total, respectively). Workload level was manipulated
by changing the speed of digit presentation (i.e. high workload consisted of 1 digit every 0.8 s and
low workload consisted of 1 digit every 2 s). In other words, the number of signals was the same
for both high and low workload conditions but the total number of digits, and the difficulty level,
varied due to digit rate presentation during the trial. Following signal presentation, the time out
period for participants’ responses was 4.8 s. An error was recorded if the participant did not
respond within this time frame.
The Eysenck Personality Inventory (EPI, Form A;Eysenck & Eysenck, 1968) was used to
assess the dimension of neuroticism. Items on the Neuroticism Scale of the EPI measure an
individual’s level of emotional stability versus emotional reactivity. Higher scores indicate greater
levels of neuroticism. Reliability estimates for the EPI neuroticism scale range from 0.84 to 0.92,
L.-E. Cox-Fuenzalida et al. / Personality and Individual Differences 36 (2004) 447–456449 while internal consistency coefficients range from 0.89 to 0.95. Construct validity for this test is
reported in the EPI Manual (Eysenck & Eysenck, 1968). In the current study the mean of neu-
roticism was 11.08 with a standard deviation of 4.51 which is generally consistent with normative
data reported in the EPI.
1.3. Procedure
When participants arrived at the laboratory they were seated at individual workstations and
asked to complete informed consent forms. Workstation partitions minimized distraction from
other participants yet permitted individuals to view the experimenter during instruction admin-
istration.
1.4. Training and baseline data collection
Participants performed the Bakan Vigilance Task during three phases: training, baseline, and
the experimental testing session.Table 1presents the counterbalanced orderings for all training,
baseline, and test trials. Each of the 194 participants was block randomized to one of the four
training/baseline/testing sequences inTable 1.
Each participant was first familiarized with the location and operation of the computer
response keys relevant for performing the monitoring task. Participants then completed an initial
feedback trial followed by an 18-min training session (three, 3-min trials at high difficulty and
three, 3-min trials at low difficulty, counterbalanced in their order of presentation) to ensure
understanding of task instructions and to lessen the likelihood of the test trials being con-
taminated by learning effects. The training regimen was patterned after that used bySchlegel and
Gilliland (1990), who found that thorough instructions followed by five to six, 3-min trials were
sufficient to produce reasonably asymptotic performance on a variety of human performance
tasks. Training session trials were followed by an 18-min baseline session (three, 3-min trials at
high difficulty and three, 3-min trials at low difficulty, in counterbalanced order) to establish
baseline data for later comparisons. To minimize fatigue, 5-min rest breaks were given between
each series of three training and three baseline trials. During these breaks, participants were
required to engage in a low-demand distracter task (i.e. completion of participant demographic
survey). The three trials of either training or baseline were each 3 min long and were presented
with no discernable break between trials (i.e. the task appeared to be nine continuous minutes).
Following the second series of three baseline trials and prior to the testing session, participants
were given a 15-min break.
1.5. Testing session
A participant’s assignment to the High-to-Low or Low-to-High testing condition was based on
the training/baseline/testing protocol sequence to which the participant was originally block
randomly assigned (seeTable 1). During the test session, participants in the High-to-Low condi-
tion engaged in three, 3-min trials at high task difficulty followed immediately by a 3-min trial at
low task difficulty. In contrast, participants in the Low-to-High test condition engaged in three, 3-
min trials at low task difficulty followed immediately by a 3-min trial at high task difficulty. These
450L.-E. Cox-Fuenzalida et al. / Personality and Individual Differences 36 (2004) 447–456 Table 1
Counterbalanced orderings for training, baseline and test trials for Low-to-High and High-to-Low conditions
L.-E. Cox-Fuenzalida et al. / Personality and Individual Differences 36 (2004) 447–456451 two test conditions created a situation where participants developed a workload history at one
workload level and then moved immediately to a dramatically different workload level. Thus,
transitions between workload levels during the High-to-Low and Low-to-High test sessions were
uninterrupted by rest periods and consequently were perceived by participants as 12 min of con-
tinuous work. Participants were tested between the hours of 9.00 a.m. and 4.00 p.m. to control
for time-of-day effects (Revelle, Humphreys, Simon, & Gilliland, 1980).
2. Results
The two dependent variables, reaction time and number of correct responses, were analyzed
separately. In addition, for each dependent variable, the Low-to-High and High-to-Low work-
load training conditions were first examined independently before being compared. Finally, for
each dependent variable, only the last 3-min trial of the 12-min testing trial was considered in the
analyses. Analyses were limited to this trial because only the last trial reflects the effects of chan-
ging workload level. Means and standard deviations for the reaction time and correct response
variables, for both the baseline and testing conditions, are presented inTable 2.
Prior to addressing the primary hypotheses, the univariate distributions for the two dependent
variables, across both workload training conditions, and the independent variable of neuroticism,
were examined for departures from normality. Due to the significant negative skewness of the
correct response variables in both conditions, and the significant positive skewness of the reaction
time data in both conditions, these variables were transformed toward normality usingManly’s
(1976)single parameter exponential transformation. These transformed variables, in turn, were
used in all inferential data analyses.
Given that all of the variables in the study were continuously scaled, a multiple regression
approach was used to analyze the data. In particular, prior to examining the influence of neuro-
ticism on testing performance, the baseline level of performance was controlled for by regressing
the testing variable on the baseline variable, and then saving the standardized residuals, or
residualized change scores, as a new variable. The residualized change scores—free from the
influence of baseline training performance—were then analyzed as the dependent variable of
interest. To screen for bivariate outliers, scatterplots were examined (xaxis=neuroticism;y
axis=residualized scores) and all extreme outliers that deviated from the swarm of data points
were identified. Next, a regression analysis was conducted, excluding the bivariate outliers, in
which the residualized scores were regressed onto neuroticism. Results for the reaction time con-
ditions are as follows. For the Low-to-High condition (n=89 after removing 2 bivariate outliers),
Table 2
Means and standard deviations by testing condition
Low-to-High
reaction timeHigh-to-Low
reaction timeLow-to-High
correct responseHigh-to-Low
correct response
Baseline 0.81 (S.D.=0.37) 0.82 (S.D.=0.15) 6.80 (S.D.=1.97) 9.23 (S.D.=1.54)
Testing 0.94 (S.D.=0.45) 0.87 (S.D.=0.24) 6.15 (S.D.=2.08) 8.78 (S.D.=1.70)
Note. Reaction time is measured in seconds and correct response scores can range from 0 to 10. 452L.-E. Cox-Fuenzalida et al. / Personality and Individual Differences 36 (2004) 447–456 neuroticism was a significant predictor of testing performance accounting for 5.7% of the var-
iance [t(87)=2.29,P=0.024, =0.24]. In the High-to-Low condition (n=103, no bivariate out-
liers), neuroticism also significantly predicted testing performance accounting for 4.2% of the
variance [t(101)=2.10,P=0.038, =0.21]. To determine whether the strength of association
between neuroticism and reaction time performance differed across the two workload conditions
(Low-to-High versus High-to-Low), a Fisher’sz-test for independent regression coefficients was
conducted. The result of thez-test was statistically nonsignificant (z=0.15,P>0.05).
In analyzing the correct response variables, the same approach as described above for the
reaction time data was utilized. Regarding the Low-to-High condition (n=84 after removing 7
bivariate outliers), neuroticism was a significant predictor of testing performance accounting for
6.6% of the variance [t(82)= 2.42,P=0.018, = 0.26]. However, in the High-to-Low condi-
tion (n=96 after removing 7 bivariate outliers), neuroticism was not a significant predictor of
testing performance [t(94)= 1.14,P=0.259, = 0.12]. Despite this difference across the two
workload conditions, the result of a Fisher’sz-test comparing the two regression coefficients was
not statistically significant (z=0.80,P>0.05).
3. Discussion
This study is the first to suggest that the examination of workload history might provide a
systematic method for testing Eysenck’s theory of neuroticism. It was hypothesized that higher
levels of neuroticism would be associated with significant decrements in performance following
changes in workload history. Results revealed significant findings confirming this prediction for
reaction time in both conditions (High-to-Low and Low-to-High). Indeed, it appears that at least
in terms of reaction time, either a sudden increase or decrease in workload can result in a sig-
nificant performance decrement for those scoring higher in neuroticism. Regarding the correct
response data, individuals scoring higher in neuroticism showed a significant performance decre-
ment only in the Low-to-High workload condition. There was no significant association between
neuroticism and performance in the High-to-Low condition, although the data did show a trend
in the expected direction.
These findings add to a growing body of research suggesting that individuals high in neuroti-
cism adapt poorly to changing environmental conditions. For instance, researchers have noted
that individuals higher in neuroticism report greater levels of psychological distress when coping
with job relocation (Moyle & Parkes, 1999), and they adapt more poorly to shift work changes
(Akerstedt and Theorell, 1976). Additionally, it has also been found that individuals higher in
neuroticism are less likely to change work environments compared to those lower in neuroticism,
perhaps because of the aversiveness of changing work environments (for a review, seeTokar,
Fischer, & Subich, 1998). The findings of the present investigation complement the aforemen-
tioned studies in that unlike previous research that has focused on psychological and physi-
ological outcome measures, the present inquiry documented the interactive effects of neuroticism
and changing workload conditions on behavioral performance indices.
While the findings of this study complement previous research and appear supportive of
Eysenck’s theory of neuroticism, it should be noted that other factors might also account for
these effects. For example, one alternative interpretation of these results is that a vigilance
L.-E. Cox-Fuenzalida et al. / Personality and Individual Differences 36 (2004) 447–456453 decrement due to either fatigue or time-on-task, might explain the reduction in performance.
However, it is well-documented in the human performance literature that the vigilance
decrement is especially sensitive to event rate (Davies & Parasuraman, 1982; Parasuraman &
Davies, 1977; See, Howe, Warm & Dember, 1995). That is, according to previous research,
an increase in event presentation (e.g. the Low-to High shift) should reduce perceptual sen-
sitivity, resulting in degraded performance (Matthews, Davies, Westerman, & Stammers,
2000). Therefore, if time-on-task or fatigue were significant factors in this experiment, a
stronger decrement in performance would be expected moving from a Low-to-High than a
High-to-Low workload level due to the difference in event rate between the two conditions.
Contrary to this prediction, in the present study the performance decrement moving from base-
line to testing was generally consistent across the two workload conditions (High-to-Low and
Low-to-High) for both reaction time and correct responses (seeTable 2). Consequently, this
alternative interpretation based on a vigilance decrement due to time-on-task or fatigue does not
appear applicable.
Another interpretation of the present findings can be derived from research on cognitive
appraisal mechanisms. This literature suggests that an individual’s appraisal of a situation can
strongly influence the subjective experience of stress (Lazarus, 1980; Lazarus & Folkman, 1984).
In addition, studies in personality research suggest that neuroticism may be associated with
maladaptive appraisal styles. While some individuals are able to handle significant disturbances in
stride, others may be anxious when faced with relatively minor challenges (for a review of
appraisal and emotion, seeWells & Matthews, 1994). Therefore, individuals who score high in
neuroticism, compared with those who score low, might interpret the shift (or change) in work-
load more negatively. Such a negative appraisal might adversely affect mood, attention and con-
centration, which could then negatively impact performance. Future studies might include a
subjective stress questionnaire to evaluate how stressful the sudden change in workload is per-
ceived by participants.
A final alternative explanation for these results draws upon the relationship between neuroti-
cism and coping. To illustrate, a change or shift in workload requires a coping response. The lit-
erature suggests that in response to stressful events, individuals scoring higher in neuroticism
cope less adaptively (e.g., are less task-focused and more emotion-focused) than those scoring
lower in neuroticism (Dorn & Matthews, 1992; Endler & Parker, 1990; Matthews et al., 2000;
McCrae & Costa, 1986). In fact, participants high in neuroticism often lack confidence in their
abilities, and tend to employ coping strategies (e.g. worry) that are likely to impair performance
(Wells & Matthews, 1994). In other words, in response to the sudden workload shift, individuals
high in neuroticism might use less adaptive coping strategies thereby resulting in poorer perfor-
mance. Future studies could employ a measure to investigate differential coping responses to the
workload shifts.
In conclusion, results of this study suggest that individuals across the neuroticism continuum
respond differently to sudden changes in workload level, though more studies are necessary
before any firm conclusions can be drawn. While these findings are consistent with Eysenck’s
theoretical model of neuroticism, they might also be accounted for by cognitive-behavioral pro-
cesses (e.g. cognitive appraisal, coping strategies). Additional work in this area might begin by
utilizing a workload history approach to examine these alternative explanations for the effects of
neuroticism on performance.
454L.-E. Cox-Fuenzalida et al. / Personality and Individual Differences 36 (2004) 447–456 Acknowledgements
The authors express their gratitude to Dr. Gerald Matthews and Dr. Kirby Gilliland for their
many helpful suggestions.
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