In this chapter I review some of the historical and current evidence showing that Donald's concern for individual differences has been well founded. I emphasize how individual differences combine with situational manipulations to affect the availability and allocation of cognitive resources. More importantly, I argue that personality effects can be understood in terms of differences in the way and in the rate at which parameters of the cognitive control system are adjusted to cope with changes in a constantly varying world. I conclude with the suggestion that an analysis of the motivational states that result from the interaction of individuals with their environment improves models both of cognitive performance as well as theories of personality.[1]
When reviewing current research it is somewhat disheartening to realize that although many of the questions about individual differences were first raised in Perception and Communication (Broadbent, 1958) and discussed later in Decision and Stress (Broadbent, 1971), after three decades we have not made much progress on finding answers to these questions. There has been some progress, however, in determining the motivational states and individual differences most associated with efficient performance.[2]
Broadbent's primary observation about individual differences was that "It has been noticed many times that some individuals show larger decrements from prolonged work than others do." (Broadbent, 1958, p 140). Who are these people and what causes these decrements was and remains an important question. A subsequent question is whether there are reliable individual differences in performance decrements associated with other stressful conditions.
In general, decrements from optimal performance may be understood in terms of motivational effects (e.g., Anderson, 1990; Blodgett, 1929; Broadhurst, 1959; Hebb, 1955; Hockey, Gaillard & Coles, 1986; Humphreys and Revelle, 1984; Revelle, 1987, 1989; Sanders, 1983, 1986; Yerkes and Dodson, 1908). Motivation is the vital link between knowing and doing, between thinking and action, between competence and performance. Theories of motivation explain why rats solve mazes faster when hungry than well fed, why bricklayers lay more bricks when given harder goals than easier ones, why assistant professors write more articles just before tenure review than after, and why people choose to be fighter pilots rather than dentists. How to motivate employees to produce more widgets and how to motivate oneself to do onerous tasks are the subjects of many management and self help courses.
Fundamental questions of motivation are concerned with the direction, intensity, and duration of behavior. Within each of these broad categories are sub-questions such as the distinctions between quality and quantity, effort and arousal, and latency and persistence. Cutting across all these questions are the relative contributions of individual differences and situational constraints to the level of motivation and of subsequent performance.
Individual differences in motivation and performance may be analyzed at multiple, loosely coupled, levels of generality (Figure 1). These levels reflect the time frame over which behavior is sampled. Over short time periods (e.g. the milliseconds of an evoked potential study), situational constraints are extremely important. As the sampling frame is increased (e.g., to the seconds of a reaction time study), energetic components of motivation as well as strategic tradeoffs of speed for accuracy become more important. At somewhat longer sampling frames (e.g. the tens of minutes of a typical psychology experiment), individual differences and situational demands for sustaining performance take precedence. At even longer intervals, differential sensitivities to positive and negative feedback affect task persistence and choice. At much longer intervals, individual differences in preference affect occupational choice and the allocation of time between alternative activities. At all of these levels it is possible to distinguish between effects related to resource availability and to resource allocation. Although an adequate theory of motivation and performance should explain behavior at all of these levels, motivational effects at intermediate time frames have been most frequently examined. In particular, the focus of this chapter are those motivational effects that can affect the link between thinking and doing within periods of several minutes to several hours.
For psychologists concerned with linking cognition to action, it is essential
to consider how motivational variables affect the competence-performance
relationship. Ever since Blodgett's (1929) demonstration that well fed rats
will learn mazes but that only hungry rats will show their knowledge by running
rapidly through the maze, psychologists have been aware that competence is a
necessary but not sufficient determinate of performance. An even more
important study was Yerkes and Dodson's demonstration (1908) that motivational
intensity (induced by foot shock) has a non-monotonic affect upon rates of
learning a discrimination task and that task difficulty interacts with
intensity.
Unfortunately many cognitive psychologists pay only lip service to the
competence-performance distinction and will report that their subjects are well
motivated and thus it is not necessary to worry about motivation. For such
researchers, motivation is a nuisance variable that can be ignored by
increasing sample size. The possibility that individual differences in
personality might interact with situational manipulations in ways that can
completely obscure important relationships is so foreign as to not even be
considered.
An exception to this rule is those who have worked with or been inspired by
Donald Broadbent. The best work on the effect on cognitive performance of
non-cognitive manipulations such as noise, time of day, distraction, and
incentives has been done by those who have followed the traditions established
at the Applied Psychology Unit in Cambridge and continued at Oxford.
Discussing motivational and stress effects before such a group is equivalent to
bringing coals to Newcastle.
The emphasis of much of the work at the APU has been how stressors combine to
affect performance[3]. Within this tradition,
there has been great concern with the similarity and differences between the
effects of different stressors. So, for example, while the effect of sleep
deprivation is to hinder certain tasks, and noise to hinder other tasks, the
combination of the two stressors can be shown to facilitate performance. An
explanation that subsumes both effects is then proposed, tested and accepted or
rejected (Broadbent, 1971).
This logic can equally well be applied to the combination of stressors with
dimensions of individual differences. By appropriate analysis of the
similarities and differences of effects due to experimental manipulations and
individual differences it is possible to evaluate the construct validity of
both. Certain individual differences seem to parallel certain stress
manipulations while other stressors seems to affect different individuals in
different ways. Both patterns of results are of theoretical importance:
Parallel effects of personality and situational manipulation allow individual
differences to be used to extend the effective range of experimental
manipulations; different patterns for different people produce better theory by
delineating the boundaries of effects of theoretical constructs.
Parallel effects of individual differences and situational stressors can
suggest that both reflect differences on the same latent construct. By
appropriate combinations of subject differences and of experimental
manipulations, it is then possible to achieve a much greater effective range on
the underlying latent construct than would be possible by manipulation or
subject selection alone..[4]
There are at least three possible reactions to the observation that what
improves the performance of one individual hinders the performance of another:
1) ignore that particular manipulation because it does not have consistent
effects; 2) run more subjects in the hope that error terms will be reduced; or
3) ask what are the special characteristics of the different kinds of subjects.
It is this third approach that is most useful. Understanding how manipulations
differ across people leads to better theories of those manipulations as well as
better theories of individual differences in personality.[5]
Before reviewing specific effects of personality, it is necessary to consider
what are the appropriate dimensions to discuss. Personality researchers can be
grouped into those who who study the effect of a single dimension versus those
who develop taxonomic models of multiple dimensions. The first approach results
in alphabetic organizations of personality traits (ranging from Type A
behavior, through Machievelianism to Sensation Seeking) with numerous studies
of convergent validity but few studies of discriminant validity. Within the
second, multivariate-taxonomic tradition are those most concerned with
description and those interested in causal (usually biological) theories. The
descriptive taxonomists have agreed that a set of five dimensions can be
identified consistently across methods. These "big five" dimensions of self
report and peer description have been labeled Surgency, Agreeableness,
Emotional Stability, Conscientiousness, and Culture, (Digman, 1990, Fiske,
1949, Goldberg, 1982; McCrae and Costa, 1987; Norman, 1963; and Wiggins, 1979).
For the more biologically minded, the theories of Hans Eysenck (1952, 1967,
1981, 1991), Jeffrey Gray (1972, 1981, 1982, 1987), or Jan Strelau (1983, 1985)
are appealing descriptions of three fundamental dimensions (Eysenck's
Extraversion, Neuroticism, and Psychoticism; Gray's Impulsivity, Anxiety, and
Aggression; Strelau's strength of the excitory and inhibitory processes, and
balance between these process) that fit within the five factor model [6]. Whether one prefers the three dimensional
biological models or the five dimensional semantic descriptions, it is clear
that all of these dimensions have substantial genetic loadings and that they
are moderately consistent from childhood throughout the life span.[7]
Perhaps because of a greater concern for causal theory among the biologically
oriented taxonomists, there has been more research relating
introversion-extraversion and stability-neuroticism to performance than there
has been for the other dimensions of the "big five". Both of these dimensions
may be associated with individual differences in motivational state. Although
staying within the two-space defined by Introversion-Extraversion and
Neuroticism-Stability, some of the more recent work has examined impulsivity, a
component of I/E and anxiety, a component of neuroticism.
Motivational states can be categorized in several different ways.
Conventionally, it has been useful to distinguish between the affective
direction and the energetic intensity of motivation (Humphreys and
Revelle, 1984). More recent work on affective states, however, has suggested
that direction may subdivided into positive and negative components (Watson and
Tellegen, 1985) and that intensity should be considered in terms of energetic
and tense arousal (Thayer, 1989). How these four constructs interrelate is far
from clear. Table 1 presents sample adjectives associated with each construct.
Matthews, Jones & Chamberlain (1989) report three mood dimensions that are
sensitive to external stressors: energetic arousal, tense arousal, and hedonic
tone (positive versus negative). They show that energetic arousal is decreased
by the administration of Chlorpromazine, Diazepam or sleep deprivation. Tense
arousal is increased by pain, or watching TV violence, but is reduced by muscle
relaxation.
Watson and Tellegen (1985) have shown that positive and negative affect are
independent of each other and can be used in combination to describe many
psychopathological conditions. Clark and Watson (1991) recently proposed that
differences in positive affect and somatic arousal account for the important
distinction between two affective conditions represented by high negative
affect, anxiety and depression. They suggest that while depression and
anxiety share high negative affect, anxiety also reflects high somatic arousal,
and depression represents lack of positive affect.
Even though Corcoran's (1965) definition of arousal as the "inverse probability
of falling asleep" is immediately understandable, it is clear that the use of
arousal as a construct is problematic. Indeed, some prefer to avoid discussing
arousal and use a broader term, energetics, that subsumes many different
constructs of motivational intensity and the effects of many environmental
stressors (Hockey, Gaillard & Coles, 1986).
Most simply, arousal is a hypothetical construct used to organize the common
behavioral effects of exercise, stimulant drugs, sleep deprivation
(negatively), time of day, time on task and impulsivity (Anderson, 1990). Each
of these separate variables has both a common and specific effect on behavior.
Caffeine and amphetamine both make one more alert and able to respond more
rapidly and for longer periods of time. Caffeine differs from amphetamine in
the locus of its action (post-synaptically versus synaptically) as well as in
some peripheral effects (e.g. caffeine induces hand tremor). It is not
difficult to demonstrate that different manipulations of arousal have somewhat
different effects on the patterning of responses. As an example of a
behavioral dissociation, simple reaction time is facilitated by caffeine but is
also faster for high impulsives (thought to be less aroused than low
impulsives). High impulsives differ from low impulsives in terms of their
speed accuracy tradeoff (Dickman and Meyer, 1988) as well as in terms of
arousal level.
Sanders (1983) discussed the multiple approaches to the study of stress. One
can manipulate the antecedent conditions or examine the physiological
consequences. Similarly, there are at least three ways to study the
relationship of arousal to performance: 1) by varying the situational demands
thought to lead to arousal; 2) by correlating psychophysiological measures to
performance; and 3) by correlating self report measures of arousal with
performance.
The first approach, manipulations of arousal by the use of stressors such as
stimulant drugs, noise, time on task, or time of day, is more commonly used by
experimental psychologists. Broadbent's 1971 review suggested that there were
common effects for some of these manipulations, but also showed that at least
two levels of control processes needed to be invoked to understand all of the
effects. A lower level of control associated with executing well learned
responses was thought to be sensitive to noise or sleep deprivation and an
upper level control process responsible for monitoring the state of the lower
level process was thought to be sensitive to alcohol, extraversion, and time on
task.
Hockey (1986) has proposed that each manipulation produces its own
idiosyncratic state, and that it is a mistake to look for a holy Grail of
unified arousal. Several energetics theorists (Gopher, 1986; Mulder, 1986;
Sanders, 1983, 1986) have made use of Pribram and McGuinness' (1975)
distinction between (phasic) arousal as affecting input processes, (tonic)
activation as affecting motor outputs, and effort as an integrative resource
allocation mechanism (Figure 2).
2) Levels of control associated with reaction time. Different stages of
information processing are affected by different control processes. Modified
from Mulder's (1986) revision of Sanders' (1983) model of reaction time.
After reviewing the parallels and differences between physiological measures
and psychological manipulations, Broadbent (1971) concluded that "We have
therefore no satisfactory physiological reference for the general state which
we are discussing, and which we have revealed purely from behavioral studies.
In some ways it might have been better therefore to avoid using the term
'arousal' for this behaviorally defined concept, but this would probably do too
much violence to the common usage in the literature. The reader should
remember however that we are working solely on a psychological level, and that
the existence of a physiological concept of arousal is merely an interesting
parallel, with no direct contact at present." (p 413). Later he added that "in
complicating the theory of arousal we shall need to know more about the
functions involved in various tasks; behavioral studies and the physiological
attack upon the brain must go hand in hand." (p 447).
The second approach, that of psychophysiological correlates, has proven to be
the most difficult. Partly this is due to confusing a within subject concept
with between subjects measurement (Venables, 1984). It is also partly due to
variations in the time course of different physiological measures. Just
as broad motivational constructs affect behavior at different time courses, so
do narrow constructs of motivational intensity, (e.g., arousal) have different
temporal parameters. EEG measures of arousal have latencies measured in
milliseconds while autonomic measures such as Skin Conductance have latencies
measured in seconds, and body temperature reflects average levels of metabolic
demands during the previous several hours. The disassociations and specific
patterning of responses associated with reactions of the hand, the heart and
the head (Lacey, 1967) make physiologists particularly cautious whenever they
discuss a construct such as generalized arousal.
The third approach is to use self reports of arousal. Thayer (1989) has argued
that subjective estimates of energetic arousal are the most likely to be
associated with performance. He has also reported that self ratings correlate
more with psychophysiological measures than the measures do themselves. This
is what would expect if each psychophysiological measure had specific as well
as general effects, and if subjective awareness of arousal reflected the
general effects.
Matthews (1989) and his colleagues (Matthews, et al., 1989) have done some of
the most extensive work examining the relationship between self reported mood
and performance. They have found consistent, although complicated,
relationships between self reports of energetic arousal and performance on a
variety of simple and complex detection tasks. In addition, they have found
that state measures of self reported arousal interact with trait measures of
individual differences in introversion-extraversion to affect performance on
these tasks.
The use of the term arousal to encompass phenomena ranging across many orders
of temporal magnitude from the milliseconds of the early stages of the evoked
potential (Mulder, 1986) to the effects of 10 minute brisk walks (Thayer, 1989)
to the tendency to seek out stimulation throughout a lifetime (Zuckerman, 1991)
is thought by many to be a mistake. I disagree. I believe that the concept
that changes in resource availability are associated with changes in arousal
allows one to integrate the effect on cognitive performance of stable
personality traits with those of variety of environmental manipulations. This
model has great heuristic value, for it allows an integration of seemingly
unrelated phenomena. Such broad lumping together of disparate effects does
indeed mask differences, however. Each task and each measure has its own
unique variance as well as common variance. What is important is to try to
distinguish the unique from the shared variance. But this is the fundamental
challenge of any theory.
3) A conceptual organization of the stages of processing that are affected by
individual differences in motivation. Environmental inputs are first detected,
then encoded, stored, and integrated with prior expectancies before responses
are selected and executed. Behavioral acts, through feedback, lead to new
environmental input. Storage and retrieval processes are represented as arrows
to and from the memory system.
Motivation affects each of these stages. In terms of tasks we have examined, we
believe that vigilance-like tasks relate to the detection and response stages
and are affected by variations in arousal; individual differences in the
learning of affectively valenced material occur at the encoding stage and are
related to differential sensitivities to rewards and punishments; memory
storage and retrieval and the effect of retention interval are affected by
variations in arousal: arousal facilitates storage but hinders retrieval; and
the information integration stage is curvilinearly related to arousal because
it reflects two components--a beneficial effect due to the speed of input and a
detrimental effect due to unavailability of recent events.
On a larger time scale, as the information processing loop continues to be
executed, resources vary in their availability and in their allocation.
Knowledge structures in memory change, affective reactions to the outcomes bias
expectancies of future reinforcement and strategic decision processes are used.
The encoding of environmental demands reflect differences in biological
sensitivities to cues for rewards and punishment (Gray, 1981) as well as the
prior contents of memory. Emotional reactions to feedback reflect the
interaction of expectancies and outcomes. Positive affective states result from
reward following expectancies of reward or non-punishment following
expectancies of punishment. Negative affective states result from punishment
following expectancies of reward and from punishment following expectancies of
punishment (Rolls,1990). Positive affect facilitates approach behavior,
negative affect facilitates avoidance behavior. Approach and avoidance
tendencies are mutually inhibitory. Increased arousal facilitates the detection
and storage of information as well as the execution of the dominant response
tendency. This leads to a much more complex model (Figure 4), but one that is
probably necessary if the interdependent effects of cognitive and affective
processes are to be understood. This model is an attempt to sketch out the
systems that are involved in actively processing valenced information in an
ongoing system responding to environmental demands and environmental
reinforcements[9].
4) Affective and cognitive reactions as part of an ongoing behavioral system.
The encoding of environmental demands reflect differences in biological
sensitivities to cues for rewards and punishment (Gray, 1981) as well as the
prior contents of memory. Reactions to feedback reflect the interaction of
expectancies and outcomes. Positive affective states result from reward
following expectancies of reward or non-punishment following expectancies of
punishment. Negative affective states result from punishment following
expectancies of reward and from punishment following expectancies of punishment
(Rolls,1990). Positive affect facilitates approach behavior, negative affect
facilitates avoidance behavior. Approach and avoidance tendencies are mutually
inhibitory. Increased arousal facilitates the detection and storage of
information as well as the execution of the dominant response. Adapted from
Clark and Watson (1991), Gray (1981), Larsen (1991), Rolls (1990), and Thayer
(1989).
In the following section I discuss the immediate motivational effects upon
performance of various combinations of individual differences in personality
and situational stressors. In the final section I suggest how an adequate
theory of individual differences and cognitive performance needs to examine
motivational effects on the stages of processing as well as consider the larger
temporal variations in affect, cognition and behavior that occur as the
information processing loop continues over time.
After an extensive discussion demonstrating that performance decrements
generalize across several types of continuous performance tasks, Broadbent
(1958) presented evidence suggesting that extraverts were more likely to show
such decrements than were introverts. By 1971, the evidence supporting this
position was much stronger. Extravert performance deteriorates more rapidly in
terms of detecting infrequent signals (Bakan, Belton & Toth, 1963; Keister
and McLaughlin, 1972), in terms of variability and speed of continuous reaction
time (Thackray, Jones & Touchstone, 1974), and in the ability to stay awake
on long distance drives (Fagerström & Lisper, 1977).
Matthews (1989) and Matthews, Davies and Lees (1990) have shown that this
decrement in performance can occur very rapidly and that self reported high
arousal is associated with the ability to maintain performance. They used a
rapidly paced discrimination task introduced by Neuchterlein, Parasuraman and
Jiang (1983) with an inter stimulus interval (ISI) of 1 second and a priority
stimulus frequency of 25% (i.e. a response was required on the average every 4
seconds) with two levels of stimulus degradation. For the degraded stimuli,
performance of low aroused subjects deteriorated within 12 minutes, but did not
for high aroused subjects. Neither Neuchterlein et al. nor Matthews et al.
found a decrement on this task with non-degraded stimuli. Neuchterlein et al.
interpret the detection of the degraded stimuli as requiring substantial
"effortful" processing as compared to the non-degraded stimuli which may
detected in an "automatic fashion". Matthews et al. argue that the degraded
stimuli lead to the kind of resource limited attention task that Humphreys and
Revelle (1984) suggest should benefit from high arousal.
Impulsivity at the adult level has frequently been claimed to be related to the
impulsivity associated with hyperactivity or what has come to be called
Attention Deficit Disorder (ADD) with (or without) Hyperactivity. ADD children
are particularly susceptible to decrements on continuous performance tasks.
Sergeant and van der Meere (1990) have reviewed the application of Sanders'
model of energetic effects on reaction time to the case of individual
differences associated with attention deficit disorders. Their review is an
excellent example of the wealth of information that comes from combining
sophisticated experimental procedures with the study of important individual
differences.
Revelle, Rosenberg & Anderson (in preparation) have recently completed
three studies with an even simpler task than the Neuchterlein et al. task, but
one that still shows pronounced decrements within a few minutes. Because of
our interest in the dynamics of behavior, we examined performance as a function
of time on task. The task we used (variable fore-period reaction time with an
inter stimulus interval of 1-11 seconds) lasts for just a few minutes (12-15)
and is typical of the demands placed upon subjects doing many monotonous real
world (or experimental) tasks. The subjects task is to respond as rapidly as
possible whenever a series of X's appears on the monitor of a computer. The
targets remain until the subject responds. The fastest reaction times of our
subjects tend to be of the order of 220-250 msec, with most responses being
less than 400 msec. We discard all trials in which the subject took more than
1000 msecs to respond, although we have observed at least one subject who was
taking 7-8 seconds on some trials. That is, our task succeeds in putting some
subjects to sleep. More objectively, self reports of energetic arousal decay
reliably across the 12 minutes of the task.
We have done three studies with this task. The first examined the effects of
impulsivity, anxiety (neuroticism) and time of day (0900 versus 1930 hours) the
second added caffeine as a factor; the third was run just in the morning and
examined the effects of an incentive (half of the subjects were offered $10 if
they could score in the top 33% of the subjects, the other half were not told
about the incentive). Dependent measures were simple reaction time, as well as
the change in reaction time as a function of trials.
When the results from all three studies are compared they clearly show a
difference between the effects of (caffeine induced or diurnally varying)
arousal versus (monetary incentive induced) effort. Although both arousal and
effort manipulations improve performance, only the arousal manipulation was
able to sustain performance. The change across time clearly demonstrated the
effects of arousal as well as impulsivity and neuroticism. Impulsivity was
positively correlated with decay of RT in the morning but negatively in the
evening, and high neurotics were unable to maintain their performance from the
first to the last part of the experiment. These results bring to mind
Broadbent's (1971) two levels of control. For although effort facilitated
reaction time (Broadbent's lower level) arousal facilitated the long term
maintenance of reaction time (Broadbent's higher level).
1) Levels of analysis and the psychological spectrum. Psychological phenomena
occur across at least 12 orders of temporal magnitude. Cognitive and
motivational theories at each frequency make use of directional and energetic
constructs. Outcome measures may be organized in terms of their temporal
resolution as well as their physiological emphasis. (Adapted from Revelle,
1989).
Individual
differences in motivation and performance
Two dimensions of personality discussed by Broadbent (1958) as important
sources of variation in performance were introversion-extraversion and
stability-neuroticism. Extraversion was associated with decrements in
performance over time and neuroticism was associated with greater decrements
following stress. Although it is tempting to propose a single model to
account for these effects, what has become clear is that the effects of
personality upon performance require multiple levels of explanation. The broad
dimensions of personality that are consistently identified from investigator to
investigator and shown to be important in different cultures and different
times affect behavior in many different ways.
Motivational
states: Affective valence and intensity
A common assumption when studying human performance is that subjects are alert
and optimally motivated. It is also assumed that the experimenter's task at
hand is by far the most important thing the subject has to do at that time.
Thus, although individual differences in cognitive ability are assumed to
exist, differences in motivation are ignored. For compliant college students
participating in one of only a few psychology experiments, this assumption
might well be true. It is probably less true for psychiatric patients, oil
platform workers at the end of their shift, or deep sea divers under several
hundred feet of water. Indeed, for almost any subject population of interest
it is difficult to believe that the specific experimental task used has an
equally powerful motivation effect upon all subjects. In fact, it is possible,
even with college students, to show that variations in motivational state are
important sources of between subject variation in performance.
Table 1: Adjectives associated with the measurement of affect and arousal
Thayer's dimensions of arousal Watson and Tellegen dimensions of affect
Energetic Arousal Tense Arousal Positive Affect Negative Affect
energetic fearful alert nervous
full-of-pep jittery active jittery
active tense excited afraid
wakeful clutched-up enthusiastic scared
lively intense attentive guilty
vigorous (not) quiescent interested hostile
wide-awake (not) quiet inspired distressed
(not) sleepy (not) placid determined ashamed
(not) drowsy (not) still proud upset
(not) tired (not) at-rest strong irritable
(not) calm
An alternative four dimensional model of affect and arousal
High Energetic Low Energy/Tension High Depression High Tension
alert drowsy unhappy nervous
full-of-pep dull gloomy jittery
active placid blue afraid
wakeful quiet sad tense
lively serene depressed scared
aroused sleepy angry guilty
excited calm irritable surprised
Affective
States
Thayer (1967, 1978, 1989) has discussed four uni-polar dimensions that he
groups into two higher order constructs of energetic and tense arousal. He
associates energetic arousal with approach behavior and tense arousal with
avoidance behavior. Energetic arousal is increased by mild exercise and varies
diurnally. Thayer (1989) adopts Gray's hypothesis that approach motivation
reflects a sensitivity to cues for reward and that avoidance behavior reflects
a sensitivity to cues for punishment. (See also Fowles, 1980).
Energetic
arousal
Energetic arousal is a non-directional component of motivation in all of these
models of affect. It is also a construct that has been found to be of great
heuristic importance in theories of motivation and cognition ever since
Broadbent (1958). More importantly, in that individuals seem to differ
systematically in their level of energetic arousal, it is a way to link
theories of individual differences to theories of behavior.
Personality,
motivation, and performance
Over the past 17 years, my colleagues and I have examined how personality
traits combine with situational manipulations to produce motivational states
that in turn affect cognitive performance. For organizational purposes, these
effects can be conceived as affecting information processing at several
different, possibly overlapping, stages (Figure 3).[8] The conceptual stage model I present is
obviously derived from Broadbent's filter model (1958) and the latter
distinctions between filtering and pigeonholing (1971), and even more from his
Maltese cross model of memory and attention (1984) as well as Sanders' (1983)
stage model of reaction time. I show it merely to distinguish between the
types of demands placed upon the subject. Stimuli must first be detected, then
encoded, before this new information is able to be stored in memory. Based
upon the incoming stimuli, further information needs to be retrieved from
memory, information needs to integrated, and some response needs to be
executed. This is a continuous loop, in that as a consequence of each
response, environmental feedback occurs that partly determine the next stimulus
that is to be detected. Storage and retrieval processes are shown as arrows
between the encoding, integrating, and memory systems.
Personality,
vigilance and continuous performance
Differences in the ability to sustain performance across time have been noticed
in dogs, sonar operators, train engineers, and faculty listening to colloquia.
What is particularly interesting to those interested in coherent descriptions
of personality is that several of the basic dimensions of personality are
related to performance decrements across time.
Personality
and non vigilance increments and decrements
Learning valenced material. Humans as well as other animate organisms need to
learn sources of reward and punishment within their environment to survive.
This fundamental observation has long been ignored by many cognitive theorists
concerned with memory. Although a great deal of research on human learning has
been done on affectively neutral material (e.g., nonsense syllables), much of
the animal learning literature has examined the effects of rewards and
punishments upon learning. Jeffrey Gray (1972, 1982) has generalized from an
animal model of rat learning to propose a neuropsychological basis of anxiety
and to propose a revision of Hans Eysenck's theory of introversion-extraversion
and neuroticism. In brief, Gray has proposed that individuals differ in their
sensitivities to cues for reward and for cues for punishment. Furthermore,
Gray associates the sensitivity to cues for reward with a behavioral activation
system (BAS) and the sensitivity to cues for punishment with a behavioral
inhibition system (BIS). He associates impulsivity with the BAS, anxiety with
the BIS.
The evidence for this hypothesis is mixed. Richard Zinbarg and I have shown that when subjects learn a go-no go discrimination task to achieve rewards or to avoid punishments, impulsivity interacts with anxiety to affect rates of learning (Zinbarg and Revelle, 1989). High impulsives who are low on anxiety rapidly learn to make responses to achieve rewards but have difficulty learning to inhibit responses in order to avoid punishment. Highly anxious subjects who are also less impulsive rapidly learn to inhibit their responses in order to avoid punishment. High anxiety when combined with high impulsivity leads to poorer learning, as does low anxiety and low impulsivity. Further support for Gray's model comes from work of Fowles (1980, 1987) and Newman and his associates (Nichols and Newman, 1986; Newman, 1987). Failures to support Gray's hypothesis have been reported by Diaz, Gray & Pickering, (1991) and Pickering (1991).
Kathy Nugent and I extended Gray's model and examined the effect of affective manipulations on the interpretation of stimuli and resulting effects upon memory (Nugent and Revelle, 1991). We examined whether variations in affect (situationally induced by positive and negative feedback) or stable personality traits (impulsivity and neuroticism) affect the memory for neutral stimuli. The results are partly consistent with Gray's model, in that the high impulsives were more likely to remember words following reward rather than punishment, but were inconsistent in that the low impulsives remembered words better following punishment rather than reward (rather than the predicted no effect) and there was no effect of anxiety (we had predicted that more anxious subjects would have better memory for stimuli followed by punishment).
Immediate and delayed retrieval. Honey bees as well as humans need to learn affectively important information and can not afford to waste cognitive resources on trivia. James McGaugh (1990) has reviewed evidence that stimulation following a particular cue enhances the long term memory for that cue. Debra Loftus and I have reviewed 25 years of findings showing that a variety of arousal inductions and measures interact with retention interval to affect memory (Revelle and Loftus, 1990, in press). Experiments using a surprising number of manipulations and measures of arousal have shown similar results: high arousal at learning inhibits immediate retrieval of the information presented but facilitates later recall of that information. Whether this is due to different effects on different stores, or to an arousal induced decrement at retrieval, or to some other explanation remains uncertain. What is certain, however, is that a consideration of individual differences is important. Puchalski (1988) replicated earlier work by Folkard, Monk, Bradbury & Rosenthal (1977) on the effects of time of day on immediate versus delayed retention and found that the pattern reverses for high and low impulsives. Immediate memory of high impulsives was superior in the morning to the afternoon, although recall after one week was superior for information learned in the afternoon rather than the morning. This was essentially Folkard's finding. However, for low impulsives, immediate memory was better in the afternoon than in the morning and delayed recall was equal for information learned at both times of day. Loftus (1990) found that impulsivity and self reported arousal interacted with retention interval to affect the probability of recall. Some of the confusion relating the effects of mood to memory is likely due to ignoring these relationships between individual differences in arousal and the effect of retention interval.
Complex tasks When information needs to be integrated and complex decisions need to be made, there seems to be an optimal level of arousal. Performance on complex reasoning tasks similar to the Graduate Record Exam is an interactive effect of impulsivity, caffeine, and time of day. Specifically, the performance of individuals thought to be less aroused (e.g. high impulsives in the morning, low impulsives in the evening) is facilitated by increases in arousal (e.g. caffeine) while that of individuals thought to be more aroused (e.g. low impulsives in the morning, high impulsives in the evening) is actually hindered (Revelle, Humphreys, Simon, and Gilliland, 1980). This result is large and is replicable. Matthews (1985) has found a similar pattern of results for extraversion and self reported arousal. Although Revelle et al., (1980) suggested that their pattern of results was consistent with an inverted U relationship between arousal and performance they did not have an unambiguous means of ordering the conditions in the between subjects design they used. Gilliland (1980), in a between groups design with three levels of caffeine did find a curvilinear (inverted U) relationship between caffeine dose and GRE performance for low impulsives and a monotonically increasing function for the high impulsives. Stronger evidence has been reported by Anderson (1990) who, in a within subjects design with multiple levels of caffeine, found a reliable number of subjects showing an inverted U relationship between GRE performance and caffeine.
I like to explain the arousal effects on the rate of information transfer as well as on memory by analogy to increasing the internal "tick rate" of a computer. A faster clock speed will lead to more samples of the environment taken per unit time, which will in turn lead to faster reaction times. However, increasing the tick rate (taking more samples of the environment) also will function to change the background context more rapidly. This will lead to greater difficulties in immediate recall, but will facilitate delayed recall.
Consider the results from our three reaction time studies. All subjects could do the task most of the time. Increased incentive or caffeine induced arousal improved performance. As the task continued, although the fastest responses remained about the same, some responses were much slower, reflecting an occasional lapse of attention. High impulsives in the morning and high neurotics throughout the day were particularly sensitive to this loss of attention. Incentives were unable to inhibit the decay across time, but caffeine was able to inhibit the decay. We interpret this result as suggesting that while effort can improve immediate performance, effort alone is unable to sustain performance. That is, in a constrained situation, one is unable to will oneself awake. But at a higher level, effort can increase alertness. As anyone knows who has struggled to overcome jetlag, drive long distances, or write an overdue paper by staying up all night, given the proper incentives one chooses activities that lead to alertness (e.g., stands up, takes brisk walks, or consumes large doses of caffeine). Thus, we are forced to add a higher level control process (Figure 5) to the two proposed by Broadbent (1971) or the hierarchy of resource pools proposed by Mulder (1986) and Sanders (1983, 1986).
5) Broadbent's two levels revisited. Higher order controls adjust the level of arousal. Although effort can not directly overcome the effect of inappropriate arousal without the ability to engage in behaviors that modify arousal, a higher order control process can recognize inappropriate arousal levels and strategically seek out or avoid arousal inducing behavior. Adapted from Broadbent, 1971
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