Matthews, Gerald

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Matthews, Gerald
Gerald Matthews
Institute for Simulation and Training, University
of Central Florida, Orlando, FL, USA
Early Life and Educational Background
Gerald Matthews was born in Edinburgh, Scot-
land, on July 2, 1959. He attended the Edinburgh
Academy and Gordonstoun, at which he shared a
house but not a meeting of minds with Prince
Andrew. He won a scholarship to read Natural
Sciences at Clare College, University of Cam-
bridge, with the intention of becoming a physicist.
Finding diminishing intellectual returns in the
study of physics, he turned first to geology and
then to experimental psychology, in which he
obtained his BA degree (first class) in 1980. He
was awarded the department’s Passingham Prize.
Remaining at Cambridge for his PhD in experi-
mental psychology, he was supervised by para-
psychologist Carl Sargent, a collaborator of Hans
Eysenck’s, completing his doctorate in 1984.
Professional Career
Gerald’sfirst academic job was a postdoctoral
fellowship at the University of Wales Institute of
Science and Technology (1984–1985). He
worked with Dylan Jones who introduced him to
cognitive models of stress and performance. He
was appointed to a lecturer position in applied
psychology at the University of Aston
(1985–1989) where collaborations inspired new
interests in vigilance (Roy Davies) and driver
behavior (Ian Glendon). He then took a faculty
position in psychology at the University of Dun-
dee (1989–1999) where he was promoted to the
rank of reader. He moved to a faculty position at
the University of Cincinnati (1999–2013) to join a
human factors group in the department of psy-
chology led by Joel Warm. He was promoted to
full professor in 2002. Most recently (2013–), he
joined the Institute of Simulation and Training at
the University of Central Florida as a research
professor. He has held visiting faculty positions
at the University of Trondheim, Norway;
Al-Farabi Kazakh National University, Kazakh-
stan; and the University of Jinan, China.
Gerald is the author or coauthor of 19 books,
175 journal articles, and over 200 book and con-
ference proceedings chapters. His research has
been supported by the British research councils,
US federal agencies including the Department of
Defense, and corporations including Unilever and
Procter and Gamble. His coauthored book on
Attention and Emotion: A Clinical Perspective
(1994) won the 1998 British Psychological Soci-
ety Book Award. He is also a recipient of the
Association of American Publishers’2002 and
2009 Awards for Professional and Scholarly
Excellence, for his coauthored books on Emo-
tional Intelligence: Science and Myth (2002) and
#Springer International Publishing AG 2017
V. Zeigler-Hill, T.K. Shackelford (eds.), Encyclopedia of Personality and Individual Differences,
DOI 10.1007/978-3-319-28099-8_2226-1

What We Know about Emotional Intelligence
(2009). He is also co-winner of the 2007 Human
Factors and Ergonomics Society Jerome H. Ely
Award for Best Paper in Human Factors. He was
elected as secretary-treasurer of the International
Society for the Study of Individual Differences
(ISSID) for 2001–2005 and reelected for
2005–2009. He was the elected president of
ISSID from 2013 to 2015. He was also elected
president of Division 13 (Traffic and Transporta-
tion Psychology) of the International Association
for Applied Psychology (IAAP), for 2010–2014.
He has been an associate editor for Emotion and
Personality and Individual Differences, and he
has also served on the editorial boards of Human
Factors and Journal of Experimental Psychology:
Applied.
Research Interests
Gerald’s research interests center on using cogni-
tive science models to understand individual dif-
ferences in personality and stress. His early work
focused on relationships between personality
traits and individual differences in information
processing, leading eventually to a novel theory
of traits, the cognitive-adaptive theory of person-
ality. He also explored the utility of dimensional
models of mood in differential psychology, a line
of research that led to a more comprehensive
assessment of subjective states in the performance
context. He integrated his interests in personality
and emotional states in developing a theory of
interactions between cognition and emotion in
clinical disorders and in advancing critical per-
spectives on the new construct of emotional intel-
ligence. A final research strand concerned applied
studies of the role of personality and emotional
state in vehicle driving and unmanned system
operation.
Cognitive-adaptive theory of personality
traits. Gerald’s Ph.D. research investigated
whether subjective arousal states mediated asso-
ciations between intellectual performance and
traits including extraversion and neuroticism, as
predicted from Hans Eysenck’s personality the-
ory. In fact, arousal moderated rather than
mediated trait-performance correlations. Further
empirical studies and literature reviews led Gerald
to two conclusions. First, extraverts and introverts
appear to be adapted to function best in different
subjective states. These adaptations may in turn
correspond to specific external environments that
promote characteristic states. For example, posi-
tive, excited emotion elicited by social events
such as parties may enhance information pro-
cessing in extraverts but not introverts. Second,
traits are associated with cognitive patternings in
information processing, i.e., a set of sometimes
small biases in multiple component processes.
Thus, trait effects cannot be attributed to any
single master process, such as arousal response,
but are distributed across numerous correlates of
the trait. Gerald collaborated with Trevor Harley
in testing connectionist models that allowed traits
and states to be linked to specific operating param-
eters of neural net architectures.
Research on traits commonly aims to identify a
small number of neural structures or processes
that can be identified with specific traits, for exam-
ple, extraversion is identified with cortico-
reticular arousability in Eysenck’s theory. How-
ever, this neural reductionism is simplistic
because traits also correlate with cognitive pro-
cesses, including high-level processes of deriving
personal meaning from events, which cannot eas-
ily be reduced to a single neurological process.
Gerald suggested the need to use cognitive sci-
ence explanatory frameworks for understanding
personality traits, specifically Zenon Pylyshyn’s
trilevel framework. Different personality phe-
nomena may be explained in terms of (1) the
physics-based biological “hardware”of the
brain, (2) the virtual information-processing “soft-
ware”of the mind, and (3) the system’s adaptation
to real-world contingencies, including goals and
knowledge representations. Thus, the personality
traits that we measure emerge from multiple pro-
cesses at different levels of abstraction from the
neural substrate.
Gerald’s interests in applied psychology led
him to question existing views on the mechanisms
through which traits influence real-life adaptive
outcomes such as job success, health, and safety.
Typically, real-world outcomes depend on
2 Matthews, Gerald

declarative and procedural skills that are acquired
through extensive practice in a specific domain or
environment. Basic neural and information pro-
cessing correlates of traits influence outcomes
indirectly, via individual differences in skill acqui-
sition, rather than directly. For example, correlates
of extraversion such as effective divided attention,
verbal fluency, and reward sensitivity provide a
platform for social skill learning, which in turn
supports better outcomes in challenging social
situations. Conceptualizing traits as adaptive con-
structs, associated with aptitudes for skill acquisi-
tion, casts traits as counterparts to environmental
challenges. Thus, extraversion-introversion
reflects variation in strategies for managing
demanding social environments. Neuroticism rep-
resents a strategy for handling social threat
marked by anticipation and avoidance, whereas
emotional stability corresponds to more direct
coping strategies. Similarly, conscientiousness
can be linked to a choice between long-term effort
and opportunism, agreeableness to preferences for
cooperation vs. competition, and openness to reli-
ance on intellectual discovery vs. traditional
wisdom.
Cognitive-adaptive theory also specifies the
processes through which traits are expressed in
behavior, subjective experience, and real-world
outcomes. The adaptive triangle refers to three
key elements of person-situation interaction:
objective procedural and declarative skills, self-
knowledge, and behavioral engagement with
challenging situations. Figure 1illustrates interac-
tions in the case of extraversion. Skills and self-
knowledge tend to be congruent; for example,
extraversion is associated with both objective
social skill and self-efficacy, so that self-
regulatory processes enhance skill deployment
and execution. For extraverts, these qualities also
lead to greater exposure to demanding social sit-
uations, leading to further skill enhancement.
Thus, trait coherence derives not from some single
key process such as arousability or reward sensi-
tivity but from multiple cognitive processes work-
ing together to support the adaptation that is
central to the trait.
Dimensional models of subjective state.
Gerald’s early studies of associations between
personality and cognitive performance were
influenced by Robert Thayer’s work on subjective
arousal, which discriminated dimensions of ener-
getic and tense arousal. Measurement of these
dimensions following performance allowed tests
of the mediating influence of arousal. Finding that
existing mood scales were not fully satisfactory
for personality studies, Gerald developed the
UWIST Mood Adjective Checklist (UMACL),
based on a correlated three-factor model of
mood states. It assessed the two Thayer arousal
dimensions, as well as a new dimension of
hedonic tone, contrasting pleasant and unpleasant
mood. It thus distinguished excited mood from
pleasure and satisfaction that are detached from
arousal. Gerald showed that associations between
major traits and the UMACL were of modest
magnitude, so that state assessment can be clearly
separated from trait measurement. The UMACL
has provided useful for assessing the affective
impact of a variety of agents including demanding
tasks, drugs, and leisure activities.
In the 1990s, Gerald came to realize that mood
assessments provided only limited insight into
individual differences in subjective response to
demanding performance environments. The tradi-
tional trilogy of mind recognizes that people expe-
rience cognitive and motivational states as well as
affective ones. Gerald developed the Dundee
Stress State Questionnaire (DSSQ) to measure
subjective states in performance settings compre-
hensively. Target constructs were derived from a
review of the literature on stress and performance.
Data for initial psychometric analyses were
secured from studies of various performance
tasks in which state was measured prior to and
following performance. A multi-stratum factor
structure of states was consistent in pre-, post-,
and change-score data. At the first level were
factors defined by the domains of the trilogy of
mind. These included the three UMACL mood
dimensions (affect), a task motivation dimension,
and six cognitive dimensions: self-focus of atten-
tion, self-esteem, concentration, control and con-
fidence, and cognitive interference associated
with task and personal concerns. Subsequent
work split the motivation dimension into separate
Matthews, Gerald 3

factors associated with intrinsic interest and
achievement strivings.
The first-order dimensions were correlated; at
the second-order, three broader dimensions span-
ning the trilogy of mind were found: task engage-
ment, distress, and worry. Task engagement
brought together energetic arousal, task motiva-
tion, and concentration. At the opposite pole of
the dimensions, tiredness, demotivation, and dis-
tractibility define a prototypical fatigue state. Dis-
tress integrated tense arousal, unpleasant mood,
and lack of confidence and control. Worry was
associated with the remaining cognitive dimen-
sions of self-focus, cognitive interference, and
low self-esteem. Dimensions were appropriately
sensitive to task stressor manipulations and psy-
chometrically distinct from major traits. For
example, neuroticism was associated with higher
distress and worry, as expected, but correlation
magnitudes were small or moderate.
Subsequent validation studies, summarized in
Table 1, investigated both the antecedents and
consequences of variation in subjective state. Var-
ious task stressors were shown to influence state
response including task demands, evaluative
stress, environmental factors such as loud noise,
and prolonged, fatiguing work. The DSSQ has
also been applied to understanding stress response
to various complex real-world tasks, including
work assignments, vehicle driving, and operation
of unmanned air and ground vehicles. The influ-
ence of external factors on state may be under-
stood in terms of Richard Lazarus’transactional
theory of stress and emotion. Consistent with the
theory, state changes are associated with mean-
ingful patterns of appraisal and coping. For exam-
ple, distress response is associated with appraisals
of threat, overload, and loss of controllability and
with use of emotion-focused coping. Personality
influences on stress state response appear to be
mediated by appraisal and coping. The sets of
Low arousability
Reward sensitivity
Conversation skills
Rapid action
Overload handling
Stress tolerance
Appraisal of
challenge Social interest
Self-efficacy
Positive affect
Speech production
Divided attention
Fast retrieval
Low response
criterion
Task-focused coping
Choice of
activity Appraisal of
outcomes
Exposure/
practice
Expertise
Information
Processing
Neural Systems
Behavioral Adaptation
High-pressure jobs
Interacting with strangers
Social overload
Cognitive Skills Self-Regulation
Matthews, Gerald, Fig. 1 A cognitive-adaptive mode of extraversion
4 Matthews, Gerald

cognitive processes associated with each state
define transactional themes, i.e., the core meaning
of the person’s understanding of the task environ-
ment. Task engagement represents commitment to
effort, distress corresponds to management of
unavoidable overload, and worry signals mental
withdrawal from the task to evaluate its personal
significance.
Subjective states were also shown to correlate
with objective performance indices. Broadly,
engagement correlates with focused attention, dis-
tress correlates negatively with divided attention,
and worry is associated with impairments on com-
plex verbal tasks. A longitudinal study used struc-
tural equation modeling to show that day-to-day
variation in distress was associated with day-to-
day variation in working memory, measured with
a task requiring concurrent arithmetic and verbal
retention. Structural equation modeling has also
been used to show that states mediate the perfor-
mance impacts of stressors including jet engine
noise and cold infection.
Studies conducted with Roy Davies in the
1990s showed that pre-task engagement predicted
perceptual sensitivity on vigilance tasks only
when the task was sufficiently attentionally
demanding to provoke loss of sensitivity over
time (the vigilance decrement). Similarly, engage-
ment was associated with controlled but not auto-
matic visual search. These findings suggested that
subjective engagement was a marker for atten-
tional resource availability, consistent with the
resource model developed by William Revelle
and Michael Humphreys. The resource interpre-
tation was further substantiated in work with Joel
Warm which showed that engagement was corre-
lated with cerebral blood flow velocity (CBFV) in
the middle cerebral arteries, a known psychophys-
iological marker for resources in vigilance stud-
ies. A structural equation modeling study showed
that engagement and CBFV were independently
predictive of subsequent vigilance, suggesting
that the two constructs function as explicit and
implicit indicators of resources.
Matthews, Gerald, Table 1 A summary of three higher-order subjective state dimensions
Task engagement Distress Worry
Key task
influences
Task interest, positive feedback,
teamwork
High cognitive load,
low-salience signals, negative
feedback
Failure, opportunity for
personal reflection, lack of
stimulation
Versus Versus
Monotony, long task duration,
system automation
High stimulus frequency,
task complexity
Personality
correlates
Conscientiousness, clarity,
mood repair
Neuroticism, anxiety,
pessimism, cognitive
disorganization
Neuroticism, pessimism,
evaluative anxiety,
dysfunctional metacognition
Versus Versus Versus
Cognitive disorganization,
fatigue proneness
Clarity, resilience, emotional
intelligence
Clarity, resilience, emotional
intelligence
Stress process
correlates
Challenge appraisal, task
focused coping
Threat appraisal, emotion-
focused coping, perceived
workload
Threat appraisal, emotion-
focused coping
Versus Versus Versus
Avoidance coping Perceived controllability Avoidance coping
Mechanisms
for
performance
effects
Attentional resource
mobilization, executive
processing, task-directed effort
Working memory, impaired
executive functioning,
exogenous attention
Mind wandering, withdrawal
of effort
Core relational
theme
Commitment to effort Management of cognitive
overload
Self-evaluation in the
performance context
Matthews, Gerald 5

Further studies showed that task engagement
was associated with superior performance on
additional demanding attentional tasks including
semantic category search, facial emotion pro-
cessing, and discrimination learning, as well as
skilled performance tasks such as simulated driv-
ing and operation of unmanned aerial vehicles
(UAVs). A study of executive processing explored
the role of task engagement in mediating person-
ality effects. Extraversion influenced executive
processing directly, but an effect of conscientious-
ness was fully mediated by task engagement. Cur-
rent theory sees task engagement as a metric for
prefrontal regulation of attention associated with
dopaminergic appetitive energization circuits, as
well as psychological influences.
Individual differences in cognition and emo-
tion. The cognitive science model of emotional
impacts on performance emphasizes that multiple
mechanisms may contribute, ranging from direct
effects of changes in neural functioning to high-
level strategic self-regulation. The association
between the task engagement state and attention
can be attributed to the relatively straightforward
neurocognitive processes that support attentional
resource availability. In this case, engagement
indexes one aspect of processing efficiency. How-
ever, the role of emotion is often more complex,
requiring a more dynamic perspective on its inter-
play with cognitive processes. Gerald initially
becomes interested in cognition-emotion interac-
tion in the context of modeling emotional distress.
His subsequent work turned to the new construct
of emotional intelligence and conceptualizations
of emotional competency.
From the 1990s onward, Gerald collaborated
with a clinical psychologist, Adrian Wells, on a
new model of cognition-emotion interaction in
emotional distress, the self-regulatory executive
functioning (S-REF) model. They published a
1994 monograph on Attention and Emotion:
A Clinical Perspective. The S-REF model
assumes a three-level cognitive architecture as
shown in Fig. 2: (1) an automatic-processing
level that generates implicit responses to threat
stimuli, (2) an executive system that monitors
outputs from the automatic level and initiates
self-regulating and coping, and (3) a database of
self-knowledge that the executive accesses in
interpreting and managing threats. Personality
trait factors such as neuroticism and anxiety that
dispose the person to clinical emotional disorder
are associated with dysfunctional self-knowledge
that supports attentional and interpretive biases
that serve to exaggerate the level of threat in the
environment and to minimize the person’s capa-
bilities for direct management of threat. State
response reflects the operation of self-referent
processing and integrates appraisal and coping
according to the relevant transactional theme.
The S-REF model introduced several novel
features into theory. First, it emphasized atten-
tional processes over the content of beliefs. Per-
sonality factors such as neuroticism are associated
with negative content in thinking, including low
self-esteem and self-efficacy. However, excessive
attention to negative content, as well as additional
processing such as elaborating negative self-
beliefs, may be more harmful than the content
itself. Thus, therapy for disorder should facilitate
the person’s attentional control in environments
perceived as threatening. Second, consistent with
the cognitive-adaptive theory of personality, mal-
adaptive attention reflects learnt, procedural skills
that are activated by self-referent processing
under trigger conditions that vary in different anx-
iety patients. Much experimental work on selec-
tive attention bias in anxiety focuses on it as an
automatic, stimulus-driven response to threat. By
contrast, the S-REF model views attentional bias
as driven by maladaptive top-down strategies for
searching the environment for threat. Such strate-
gies can be driven by proceduralized self-
knowledge and are not necessarily accessible to
consciousness. Third, metacognitive beliefs con-
stitute a key form of self-belief that drives mal-
adaptive executive processing. Patients with
emotional disorders often believe that unpleasant
thoughts and images must be controlled or
suppressed and that failure to do so is directly
harmful. Such metacognitions paradoxically
enhance awareness of negative ideation, leading
to perseverative cycles of worry and rumination
that contribute to vulnerability to clinical disorder.
Fourth, the S-REF model emphasizes dynamic
maladaptive processes in emotional disorder.
6 Matthews, Gerald

Clinical anxiety and depression are not a direct
consequence of negative self-beliefs or even dys-
functional metacognitions. Instead, the processing
cycles driven by harmful metacognitions prevent
successful resolution of the challenges posed by
the threats of everyday life, such as criticism from
others, and symptoms of ill health. Perseverative
worry interferes with effective problem-solving
and decision-making. It also encourages the
avoidance of feared situations so that the person
lacks the opportunity to acquire constructive
problem-solving skills and to disconfirm unreal-
istic negative beliefs. Worry also tends to elabo-
rate and reinforce the self-knowledge that drives
maladaptive S-REF activity. Thus, the clinician
needs to consider not only abnormalities in infor-
mation processing but how processing shapes cli-
ents’interactions with their specific social
environment in detrimental ways.
Cognition and emotion also interact in more
benign fashion, as explored in positive psychol-
ogy. In the early 2000s, Gerald became interested
in the new construct of emotional intelligence,
defined as an array of aptitudes, competencies,
and skills for identifying, understanding, and
managing emotion. Initial interest in the construct
was fueled by Daniel Goleman’s best-selling pop-
ular book, which made a number of grandiose and
unsubstantiated claims for the real-world impor-
tance of being emotionally intelligent. Gerald
took a critical stance and worked with Moshe
Zeidner and Richard Roberts on the first in-depth
critique of emotional intelligence, published as a
book on Emotional Intelligence: Science and
Myth (2002).
The book criticized the conceptualization,
measurement, and application of emotional intel-
ligence. Definitions of the construct were often
vague and over-inclusive, being laundry lists of
miscellaneous desirable personal characteristics
(other than standard intelligence). There was little
use of the theory of cognition and emotion to
guide conceptualization. Measurement
approaches were split between those that aimed
Emotion
regulation Intrusions - performance feedback
- awareness of discomfort
- success/failure images
PERFORMANCE
Goal state
Feedback (as encounter develops dynamically)
Emotion-focus
SUPERVISORY EXECUTIVE
SELF-KNOWLEDGE
Self-referent beliefs, motivations and procedures
-personal achievement
-social knowledge
-task-specific knowledge
-metacognitive knowledge
LOWER-LEVEL NETWORKS
Representations of task stimuli,
and body sensations
Task-focus
- Mobilize effort
-Change strategy
Response
selection
Avoidance: Lower goal
Coping
Worry
cycles
Update self-
knowledge
Access performance
motivations
Appraisal
Retrieve coping
procedures
Resource-limited
supervisory control
States
Personality
CONTEXTUAL
TASK AND
STIMULI
Matthews, Gerald, Fig. 2 The S-REF model of stress processes during task performance
Matthews, Gerald 7

to develop ability tests akin to conventional intel-
ligence tests and those based on self-report. Dif-
ferent tests failed to converge, and each
measurement strategy had its own limitations. It
is difficult to score test items for emotional com-
petency objectively, as the “correct”answer may
depend on the context. It transpired too that self-
report measures were often highly correlated with
existing personality dimensions such as emotional
stability. Self-reports of abilities are of notoriously
poor validity. Finally, there was little evidence for
the predictive validity of the measures in real-
world settings; they added little to standard ability
and personality measures in predicting occupa-
tional and educational success, as well as mental
health and stress.
Subsequently, Gerald and his colleagues
focused on identifying strands of emotional intel-
ligence that offered something novel, as stronger
evidence for incremental validity of measures in
some contexts started to emerge. They argued that
although emotional intelligence cannot be com-
pared to general cognitive ability as a major axis
of individual differences, it can function as an
umbrella term for a variety of loosely related
constructs and processes that may not be fully
captured by existing personality and ability mea-
sures. For example, personality dimensions
describing styles of emotion-regulation predict
mood and electroencephalographic (EEG)
response beyond standard personality traits. Situ-
ational judgment tests for emotional intelligence
may pick up contextualized skills for emotion
management. With Moshe Zeidner, Gerald has
explored associations between emotional intelli-
gence and social support, leading to a suggestion
that emotional competency in part reflects partic-
ipation in supportive social networks rather than
being an atomic individual characteristic.
Applications of personality research. Recent
perspectives on personality emphasize the impor-
tance of consequential validity, i.e., personality
assessment should support prediction of real-
world outcomes and guide effective practical
implications. Gerald’s textbook on Personality
Traits, coauthored with Ian Deary and Martha
Whiteman, emphasized that the advancing psy-
chological science of traits increasingly supports
application. In his empirical research, Gerald has
been concerned especially with personality in the
context of vehicle driving. It is not uncommon for
drivers to display traits such as aggression or
sensation seeking that is associated with danger-
ous driving behaviors and loss of safety.
Gerald was part of a team at Aston University
that developed one of the first validated scales for
dispositional driver stress dimensions. He subse-
quently refined the initial scale to develop the
Driver Stress Inventory (DSI), which measures
dislike of driving, aggression, sensation seeking,
hazard monitoring, and fatigue proneness. The
scale has been validated in laboratory studies
using a driving simulator and in field studies
using samples of commercial and noncommercial
drivers. Studies using the DSSQ and other scales
have confirmed that the DSI appropriately pre-
dicts subjective state responses to driving. For
example, dislike of driving is associated with
higher distress and worry, aggression relates to
anger, and hazard monitoring and fatigue prone-
ness correlate with higher and lower task engage-
ment, respectively. Scales also predict driving
errors and objective performance in simulator
studies. Dislike of driving correlates with atten-
tional impairments, for example, but only in
low-demand conditions, suggesting a deficit in
effort regulation rather than a lack of attentional
resources.
From a theoretical standpoint, driver stress
traits can be understood as contextualized adapta-
tions to the key demands of driving, within the
framework of the cognitive-adaptive model of
personality. For example, dislike of driving mod-
erates response to threats to physical safety, and
aggression moderates the impacts of perceived
obstructiveness and hostility of other drivers. In
Gerald’s transactional model of driver stress, dis-
like of driving is associated with biased percep-
tions of personal competence and a tendency to
use emotion-focused coping. Its safety implica-
tions are mixed because greater vulnerability to
distraction is offset by greater caution in driving
behaviors, such as lower preferred speed. By con-
trast, aggression is associated with biased percep-
tions of other drivers, including false attributions
of hostility, as well as a preference for confrontive
8 Matthews, Gerald

coping strategies that may mediate its adverse
safety impacts.
Gerald’s recent research is concerned with
individual differences in workload response to
task factors such as multitasking, measured with
subjective and psychophysiological instruments.
Psychometric analyses have shown that alternate
measures often fail to converge, suggesting the
need for a multivariate approach to workload
assessment that distinguishes explicit and implicit
responses. Furthermore, personality scales for
resilience and anxious metacognitions predict dif-
ferent elements of the response. Multivariate
workload assessment may provide a means for
specifying the performance vulnerabilities of the
individual and for driving adaptive automation
that mitigates those vulnerabilities.
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Huggins, J., Gilliland, K., Grier, R., & Warm, J. S.
(2002a). Fundamental dimensions of subjective state
in performance settings: Task engagement, distress and
worry. Emotion, 2, 315–340.
Matthews, G., Zeidner, M., & Roberts, R. (2002b). Emo-
tional intelligence: Science and myth. Cambridge, MA:
MIT Press.
Matthews, G., Zeidner, M., & Roberts, R. D. (2004). Seven
myths of emotional intelligence. Psychological
Inquiry, 15, 179–196.
Matthews, G., Emo, A. K., Funke, G., Zeidner, M., Rob-
erts, R. D., Costa Jr., P. T., & Schulze, R. (2006).
Emotional intelligence, personality, and task-induced
stress. Journal of Experimental Psychology: Applied,
12,96–107.
Matthews, G., Deary, I. J., & Whiteman, M. C. (2009).
Personality traits (3rd ed.). Cambridge: Cambridge
University Press.
Matthews, G., Warm, J. S., Reinerman, L. E., Langheim,
L., Washburn, D. A., & Tripp, L. (2010a). Task engage-
ment, cerebral blood flow velocity, and diagnostic mon-
itoring for sustained attention. Journal of Experimental
Psychology: Applied, 16, 187–203.
Matthews, G., Warm, J. S., Reinerman, L. E., Langheim,
L. K., & Saxby, D. J. (2010b). Task engagement, atten-
tion and executive control. In A. Gruszka,
G. Matthews, & B. Szymura (Eds.), Handbook of indi-
vidual differences in cognition: Attention, memory and
executive control (pp. 205–230). New York: Springer.
Matthews, G., Pérez-González, J.-C., Fellner, A. N.,
Funke, G. J., Emo, A. K., Zeidner, M., & Roberts,
R. D. (2015a). Individual differences in facial emotion
processing: Trait emotional intelligence, cognitive abil-
ity or transient stress? Journal of Psychoeducational
Assessment, 33,68–82.
Matthews, G., Reinerman-Jones, L. E., Barber, D. J., &
Abich, J. (2015b). The psychometrics of mental work-
load: Multiple measures are sensitive but divergent.
Human Factors, 57, 125–143.
Roberts, R. D., Zeidner, M., & Matthews, G. (2001). Does
emotional intelligence meet traditional standards for an
Matthews, Gerald 9

intelligence? Some new data and conclusions. Emotion,
1, 196–231.
Rowden, P., Matthews, G., Watson, B., & Briggs,
H. (2011). The relative impact of occupational stress,
life stress, and driving environment stress on driving
outcomes. Accident Analysis and Prevention, 44,
1332–1340.
Wells, A., & Matthews, G. (1996). Modelling cognition in
emotional disorder: The S-REF model. Behaviour
Research and Therapy, 34, 881–888.
Wells, A., & Matthews, G. (2015). Attention and emotion:
A clinical perspective (Classic ed.). New York: Psy-
chology Press.
Zeidner, M., & Matthews, G. (2016). Ability emotional
intelligence and mental health: Social support as a
mediator. Personality and Individual Differences, 99,
196–199.
Zeidner, M., Matthews, G., & Roberts, R. D. (2012). The
emotional intelligence, health, and well-being nexus:
What have we learned and what have we missed?
Applied Psychology. Health and Well-Being, 4,1–30.
Gerald Matthews is a faculty member at the Institute for
Simulation and Training, University of Central Florida. He
is an experimental psychologist whose research focuses on
using cognitive science to understanding the interrelation-
ships between personality traits and cognitive and emotional
processes. His work includes both basic laboratory studies
as well as applied research.
10 Matthews, Gerald