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Abstract

A sudden comprehension that solves a problem, reinterprets a situation, explains a joke, or resolves an ambiguous percept is called an insight (i.e., the “Aha! moment”). Psychologists have studied insight using behavioral methods for nearly a century. Recently, the tools of cognitive neuroscience have been applied to this phenomenon. A series of studies have used electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) to study the neural correlates of the “Aha! moment” and its antecedents. Although the experience of insight is sudden and can seem disconnected from the immediately preceding thought, these studies show that insight is the culmination of a series of brain states and processes operating at different time scales. Elucidation of these precursors suggests interventional opportunities for the facilitation of insight.
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Current Directions in Psychological
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DOI: 10.1111/j.1467-8721.2009.01638.x
2009 18: 210Current Directions in Psychological Science
John Kounios and Mark Beeman
Moment : The Cognitive Neuroscience of InsightAha!The
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The Aha! Moment
The Cognitive Neuroscience of Insight
John Kounios
1
and Mark Beeman
2
1
Drexel University and
2
Northwestern University
ABSTRACT—A sudden comprehension that solves a prob-
lem, reinterprets a situation, explains a joke, or resolves
an ambiguous percept is called an insight (i.e., the ‘‘Aha!
moment’’). Psychologists have studied insight using be-
havioral methods for nearly a century. Recently, the tools
of cognitive neuroscience have been applied to this phe-
nomenon. A series of studies have used electroencephalo-
graphy (EEG) and functional magnetic resonance imaging
(fMRI) to study the neural correlates of the ‘‘Aha! mo-
ment’’ and its antecedents. Although the experience of in-
sight is sudden and can seem disconnected from the
immediately preceding thought, these studies show that
insight is the culmination of a series of brain states and
processes operating at different time scales. Elucidation of
these precursors suggests interventional opportunities for
the facilitation of insight.
KEYWORDS—Aha! moment; creativity; EEG; fMRI; insight;
neuroimaging; problem solving
Insight is a sudden comprehension—colloquially called the
‘‘Aha! moment’’—that can result in a new interpretation of a
situation and that can point to the solution to a problem
(Sternberg & Davidson, 1995). Insights are often the result of the
reorganization or restructuring of the elements of a situation or
problem, though an insight may occur in the absence of any
preexisting interpretation.
For several reasons, insight is an important phenomenon.
First, it is a form of cognition that occurs in a number of domains.
For example, aside from yielding the solution to a problem, in-
sight can also yield the understanding of a joke or metaphor, the
identification of an object in an ambiguous or blurry picture, or a
realization about oneself. Second, insight contrasts with the
deliberate, conscious search strategies that have been the focus
of most research on problem solving (Ericsson & Simon, 1993);
instead, insights occur when a solution is computed uncon-
sciously and later emerges into awareness suddenly (Bowden &
Jung-Beeman, 2003a; Smith & Kounios, 1996). Third, because
insight involves a conceptual reorganization that results in a
new, nonobvious interpretation, it is often identified as a form of
creativity (Friedman & Fo
¨rster, 2005). Fourth, insights can re-
sult in important innovations. Understanding the mechanisms
that make insights possible may lead to methods for facilitating
innovation.
AN APPROACH TO STUDYING INSIGHT
In our studies, we have used electroencephalography (EEG) and
functional magnetic resonance imaging (fMRI) to examine pro-
cesses that would be difficult to detect using behavioral mea-
surements alone. EEG has the benefit of high temporal
resolution; fMRI complements EEG by affording the high spatial
resolution necessary for precise localization of brain activity.
We used a type of problem called compound remote associates
(Bowden & Jung-Beeman, 2003b) that affords two advantages.
When a participant solves one of these problems, he or she can
typically do so within 10 seconds; much longer time is often
needed to solve classic insight problems (Fleck & Weisberg,
2004). This relatively short solution time allowed us to produce
the large number of trials necessary for EEG and fMRI. In ad-
dition, compound-remote-associates problems can be solved
either with or without insight, enabling researchers to compare
insight and analytic solving without changing the type of prob-
lem. In our experiments, compound remote associates that were
solved by insight and by analytic processing were sorted ac-
cording to participants’ trial-by-trial judgments of how the so-
lution entered awareness—suddenly for insight, incrementally
for analytic processing.
Each compound-remote-associates problem consists of three
words (e.g., crab,pine,sauce). Participants are instructed to
think of a single word that can form a compound or familiar two-
word phrase with each of the three problem words (e.g., apple can
join with crab,pine, and sauce to form pineapple,crabapple, and
applesauce). As soon as participants think of the solution word,
Address correspondence to John Kounios, Department of Psycho-
logy, Drexel University, 245 N. 15
th
Street,Mail Stop 626, Philadelphia,
PA 19102-1192, e-mail: john.kounios@gmail.com; or Mark Beeman,
Department of Psychology, Northwestern University, 2029 Sheridan
Road, Evanston, IL 60208-2710, e-mail: mjungbee@northwestern.edu.
CURRENT DIRECTIONS IN PSYCHOLOGICAL SCIENCE
210 Volume 18—Number 4Copyright r2009 Association for Psychological Science
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they press a button as quickly as possible. Participants are in-
structed to respond immediately and not take any time to verify
this solution. They are then prompted to verbalize the solution
and then to press a button to indicate whether that solution had
popped into awareness suddenly (insight) or whether the solu-
tion had resulted from a more methodical hypothesis-testing
approach. An example of a methodical strategy for solving the
problem would be to start with crab and generate associates of
this word, such as cake.Crabcake is an acceptable compound, as
is applecake.Butpinecake and cakepine are both unacceptable,
leading to the rejection of cake as a potential solution. One might
then try grass.Crabgrass is acceptable, but neither pinegrass nor
applegrass works—and so on. Participants in our studies im-
mediately and intuitively understood the distinction between
sudden insight and methodical solving.
NEURAL CORRELATES OF THE ‘‘AHA! MOMENT’’
Our first neuroimaging study included separate EEG and fMRI
experiments that examined brain activity during a time interval
beginning shortly before the derivation of the solution (Jung-
Beeman et al., 2004). Brain activity corresponding to analytic
Gamma Power
1.7e-10
1.3e-10
0.9e-10
–2.0 –1.0 1.0
R
R
I
NI
Time (sec)
A
BC
Fig. 1. High-frequency gamma-band (approximately 40 Hertz) electroencephalogram (EEG) activity
associated with problem solution. Panel A shows a plot of gamma-band activity as a function of time.
The y-axis represents EEG power (squared microvolts); the x-axis represents time (seconds) with the
yellow Rsignifying the point in time at which a subject presses the button to indicate that he or she had
just derived the problem solution. The blue line (NI) represents gamma activity for noninsight (an-
alytic) solutions; the red line (I) represents gamma activity for insight solutions. The burst of gamma
activity for insight solutions relative to noninsight solutions beginning approximately 300 milliseconds
prior to the button-press response (about the amount required to make a manual response) is hy-
pothesized to be the primary neural correlate of the ‘‘Aha!’’ experience. Panel B shows the topo-
graphic distribution of this gamma-band activity for the insight solutions minus the activity for the
noninsight solutions. The view is of the right side of the head, with each red dot signifying the location of
an EEG electrode. The yellow region over the right anterior temporal lobe (i.e., the area above the
right ear) is the spatial focus of the insight effect (i.e., insight solutions minus noninsight solutions).
Panel C shows the corresponding insight effect for the functional magnetic resonance imaging (fMRI)
experiment. This activation is in the right anterior superior temporal gyrus. From ‘‘Neural Activity
When People Solve Verbal Problems With Insight,’’ by M. Jung-Beeman, E.M. Bowden, J. Haber-
man, J.L. Frymiare, S. Arambel-Liu, R. Greenblatt, et al., 2004, PLoS Biology,2, pp. 502 and 505.
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John Kounios and Mark Beeman
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solutions was subtracted from activity corresponding to insight
solutions, to show brain areas whose level of activity differed
while solving problems with insight relative to solving problems
analytically. EEG showed that insight solutions were associated
with a burst of high-frequency (i.e., 40-Hertz gamma-band)
activity starting about 300 milliseconds before the button-press
signaling that a solution was derived. This burst of EEG activity
was detected at electrodes located over the right anterior tem-
poral lobe, just above the right ear (Fig. 1). The only insight
effect reliably detected with fMRI in this initial study occurred
in a brain region called the right anterior superior-temporal
gyrus, which was underneath the electrodes showing the corre-
sponding EEG effect.
There was one additional insight effect present in the EEG
data. Immediately prior to the burst of gamma-band EEG activity
was a burst of slower, alpha-band (approximately 10 Hertz),
activity measured over right occipital cortex (i.e., the right side
of the back of the head; see Fig. 2). Though this finding was
unexpected, the EEG literature, and common experience, sug-
gested an interpretation.
In everyday circumstances, when asked a difficult question, a
person often will look away from the questioner, or even close his
or her eyes, in order to avoid distractions and to concentrate on
thinking of the answer. In our EEG experiment, the participants
were instructed to keep their eyes open and focus on a fixation
marker in order to minimize electrical noise from eye movements
and blinks. These instructions strongly discouraged the ten-
dency to look away or close their eyes. This restriction on their
overt behavior apparently led to a type of covert compensation.
Alpha-band oscillations are the brain’s dominant rhythm and
are understood to reflect idling or inhibition of brain areas
(Kounios et al., 2006). In particular, such oscillations measured
over the occipital or visual cortex at the back of the head reflects
a reduction in the amount of visual information passed from
visual processing areas to higher areas that perform more ab-
stract computations (i.e., sensory gating; Payne & Kounios,
2009). This insight-related burst of alpha may represent the
brain’s covert alternative to closing the eyes or looking away.
Taken together, the alpha and gamma effects suggest that
when a weakly activated problem solution is present in the right
temporal lobe, a temporary reduction in interfering visual inputs
facilitates the retrieval of this solution, allowing the solution to
pop into awareness.
THE PREPARED MIND
Our initial study of the neural substrates of insight suggested that
the brain response associated with the Aha was the culmination
of a series of neural events, such as the alpha ‘‘brain blink.’’ A
subsequent study (Kounios et al., 2006) sought to trace the or-
igins of insight farther back in time to answer a more funda-
mental question—namely, why are problems sometimes solved
with insight and sometimes analytically? Inspired by Louis
Pasteur’s famous comment ‘‘Chance favors only the prepared
mind,’’ we examined brain activity immediately preceding the
display of each problem. The logic of this study was that the
pattern of brain activity already present when a problem is
displayed may bias cognitive processing, increasing the chances
of either insight or analytic solving. On each trial of the EEG
experiment, participants signaled readiness for the next problem
with a button-press. One second later, the three words of a
problem were displayed on the monitor in front of them. We
examined the 2-second interval preceding the presentation of
each problem (i.e., starting 1 second before the button-press and
ending the instant the problem was displayed) and sorted the
trials into those that were subsequently solved with insight and
those subsequently solved analytically. (Because of the techni-
cal requirements of fMRI, the onset of the problem display was a
random rest interval not controlled by the participants in the
fMRI experiment.)
We found distinct patterns of brain activity preceding prob-
lems solved with insight versus those solved analytically (Fig. 3).
Before the presentation of problems to be solved with insight,
EEG revealed greater neural activity over the temporal lobes of
both cerebral hemispheres (i.e., around the ears) and over mid-
frontal cortex. Before the presentation of problems to be solved
analytically, there was more neural activity measured over
posterior (visual) cortex. The results of the fMRI experiment
closely mirrored those of the EEG experiment. The activations of
both the right and left temporal lobes suggest priming of brain
areas that process lexical and semantic information. fMRI
showed that the mid-frontal activity originated in the anterior
cingulate, a brain area that a number of neuroimaging studies
have implicated in the control of cognitive processes like de-
tection of inconsistent or competing activity, attention switching,
and so on (e.g., Botvinick, Cohen, & Carter, 2004). We therefore
hypothesized that, in this situation, the anterior cingulate may be
involved in the readiness to detect weakly activated, subcon-
scious solutions and to switch attention to them when they are
detected. The greater neural activity measured by EEG over
visual cortex preceding problems solved analytically was hy-
pothesized to reflect the amount of visual information passed
along to higher cortical areas. In this case, preceding the display
of problems to be solved analytically, the increase in neural
activity suggests that participants were preparing for analytical
solving, in part, by directing attention outwardly—that is, to the
monitor on which the next problem was about to be displayed.
Conversely, preparation for solving an upcoming problem with
insight involved directing attention inwardly—priming for lex-
ical-semantic processing and the detection and retrieval of
weakly activated potential solutions rather than focusing at-
tention outwardly toward the monitor.
It is not yet clear to what extent these forms of preparation may
be conscious and volitional or automatic and anticipatory. The
fact that subjects in the EEG experiment initiated the display of
each problem when ready suggests a volitional substrate. An
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1.0E-10
A
B
6.0E-11
2.0E-11
–2.0E-11
–6.0E-11
–1.0E-10
Alpha Insight Effect
Alpha Insight Effect
–2.0
Time (sec)
R
R
–1.5 –1.0 –0.5
0
2.5E-11
1.5E-11
5.0E-12
–5.0E-12
–1.5-11
–2.5E-11
Gamma Insight Effect
Gamma Insight Effect
0
Fig. 2. The alpha-band (10 Hertz) insight effect (i.e., insight solutions minus noninsight solutions).
Panel A shows a plot of electroencephalogram (EEG) alpha power for the insight effect in relation tothe
gamma insight effect (see Fig. 1). The x-axis represents time (seconds) leading up to the button-press
response (the yellow R) that indicated that a participant had derived a solution to the problem. The y-
axis represents EEG power (squared microvolts–note the different scales for alpha and gamma). The
purple line represents the alpha insight effect (measured at a right posterior electrode); the green line
represents the gamma insight effect (measured at a right temporal electrode). Note that the alpha burst
precedes the gamma burst. Panel B shows the topographic distribution of the alpha insight effect (back
view of the head). This panel uses the same conventions as Figure 1, Panel B.
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alternative hypothesis that we explored is that there are slow
fluctuations in resting-state brain activity that are associated
with insight versus analytic processing; perhaps participants
press the button to indicate readiness for the next problem when
these fluctuations put them in a target state for tackling the
problem. However, examination of possible sequential depen-
dencies in solving strategies across trials yielded no evidence of
clusters of insight or noninsight solutions at any time-scale,
weighing against the notion that subjects initiated trials to co-
incide with uncontrolled variation in brain states.
THE CREATIVE BRAIN AT REST
The study just described shows how problem-solving strategy
can be influenced by the prior preparatory state. This, however,
naturally raises the question of what determines the preparatory
state. A recent study examined the possibility that the adoption
of problem-solving strategies may have its origins in yet more
fundamental processes—specifically, in individual differences
in resting-state brain activity (Kounios et al., 2008). In this
study, we recorded participants’ EEG while they sat comfortably
with no task to perform and no specific expectation of what would
happen next. After this resting-state activity was recorded, they
were given a series of anagrams to solve, using the same insight-
judgment procedure used in our compound-remote-associates
studies. We divided the participants into two groups based on the
proportion of their anagram solutions that resulted from insight
versus analytic processing. We then analyzed their initial rest-
ing-state EEG activity (collected before they even knew what
task they would be performing) separately for these high-insight
and high-analytic groups.
Based on prior research, we predicted two general differences
between these groups. One prominent view of creativity is that it
is based on the processing of remote or loose associations be-
tween ideas (Mednick, 1962). Recent research implicates the
brain’s right hemisphere in the processing of remote associates
and the left hemisphere in the processing of close or tight as-
sociations (for a review, Jung-Beeman, 2005). We therefore
predicted greater activity in right-hemisphere regions associ-
ated with lexical and semantic processing. Second, based on
previous findings suggesting that individuals high in creativity
habitually deploy their attention in a diffuse rather than a fo-
I-NI
–3.25
+3.25
Fig. 3. Areas involved in preparation for insight versus those involved in analytic solving. The to-
pographic maps of electroencephalogram (EEG) alpha power (8–10 Hz) show a display head fromfour
angles. Yellow-orange regions are areas in which neural activity during the 2 seconds immediately
preceding presentation of the compound-remote-associate problems was greater for trials on which
the subsequently presented problem would be solved with insight. Blue regions reflect areas in which
the neural activity during the same preparatory phase was greater for trials on which the subsequently
presented problem would be solved without insight (i.e., analytically). The color scale reflects scalp
regions yielding significant t-scores.
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cused manner (Ansburg & Hill, 2003), we predicted greater
diffuse activation of the visual system in high-insight partici-
pants (corresponding to less posterior alpha-band and beta-band
EEG activity). Both of these predictions were supported by the
data, demonstrating that task-related problem-solving strategies
have their origins in individual differences in resting-state brain
activity.
As interesting as these findings are, they raise as many
questions as they answer. For example, this experiment did not
ascertain whether these insight-related differences in resting-
state activity are stable. It is possible that the relevant aspects of
resting-state activity vary over a time-course of hours or days,
resulting in slow fluctuations of cognitive style. And if the in-
sight-related aspects of resting-state EEG are relatively stable,
the question arises whether this stability has a genetic basis
(though stability could result from nongenetic causes, such as
practice). In general, individual differences in resting-state
EEG are fairly stable and have been shown to have a genetic
basis (Smit, Posthuma, Boomsma, & de Geus, 2005), though it is
not yet known whether these insight-related differences are a
subset of the stable, genetically related individual differences in
resting-state EEG. This question is important, because it sug-
gests the possibility of genetically determined individual
differences in cognitive style.
Nevertheless, even if the tendency to have insights is genet-
ically influenced, the fact that preparation for insight or analytic
processing can vary from problem to problem shows that insight
is not a fixed ability. Our research has begun to examine how
various factors can influence this tendency. For example, a re-
cent fMRI study showed that people are more likely to solve
problems with insight if they are in a positive mood when they
arrive at the lab than if they are in a neutral or negative one
(Subramaniam, Kounios, Parrish, & Jung-Beeman, 2009).
Moreover, positive mood was associated with greater activity in
the anterior cingulate during the preparation period prior to each
problem, suggesting that positive mood biases cognitive control
mechanisms in ways that facilitate insight, with anxiety having
the opposite effect. Similarly, work in progress suggests that
when positive mood is induced by having participants watch
comedy videos, they solve more problems, and solve more of
them with insight, than they do after they watch neutral or
anxiety-inducing films.
FUTURE DIRECTIONS
These research findings raise many issues to be tackled by future
studies. One question is to what extent these results are specific
to verbal-reasoning problems or whether insight in other do-
mains (e.g., visual-object identification) involves somewhat
different neurocognitive mechanisms. Another question is
whether differences in resting-state brain activity between high-
insight and high-analytic participants are relatively stable over
time, possibly being influenced by genetics, or whether these
differences reflect transitory, though slowly changing, states.
Finally, the real-world implications of these findings are po-
tentially of substantial importance. The fact that the tendency to
solve problems with insight is influenced by multiple processes
operating at varying time-scales suggests that there are a number
of ‘‘vulnerable’’ points in the cascade of processes that result in
an insight. These points potentially represent opportunities for
influencing the course of reasoning. We expect research will
eventually result in a systematic technology for facilitating or
entraining creative insight.
Recommended Reading
Jung-Beeman, M., Bowden, E.M., Haberman, J., Frymiare, J.L., Ar-
ambel-Liu, S., Greenblatt, R., et al. (2004). (See References). A
study using EEG and fMRI to isolate the main neural correlate of
the ‘‘Aha! moment.’’
Kounios, J., Fleck, J.I., Green, D.L., Payne, L., Stevenson, J.L., Bowden,
M., & Jung-Beeman, M. (2008). (See References). A study showing
that resting-state (EEG) brain activity predicts whether partici-
pants will subsequently tend to solve problems with insight or
analytically.
Kounios, J., Frymiare, J.L., Bowden, E.M., Fleck, J.I., Subramaniam,
K., Parrish, T.B., & Jung-Beeman, M.J. (2006). (See References).
A study combining EEG and fMRI to demonstrate that the
neural antecedents of insight begin even before a problem is
presented.
Smith, R.W., & Kounios, J. (1996). (See References). A study using
quantitative analyses of reaction time and error data to show that
the solving of anagrams, which are considered to be insight-like
problems, occurs in a discrete, all-or-none, fashion—in contrast to
other cognitive tasks, which yield solutions in an incremental
fashion.
Subramaniam, K., Kounios, J., Parrish, T.B., & Jung-Beeman, M.
(2009). (See References). A study showing that participants’ mood
upon entering the laboratory predicts whether they will subse-
quently solve problems with insight or analytically.
Acknowledgments—The authors would like to thank Edward
Bowden, Jennifer Stevenson, Stella Arambel-Liu, Deborah
Green, Lisa Payne, Jessica Fleck, Roderick W. Smith, Richard
Greenblatt, Todd Parrish, and Karuna Subramaniam for their
collaboration on the research described in this article.
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... This effect is corroborated by studies showing that moments of insight evoke intense positive feelings (Shen et al., 2016;Webb et al., 2018). With regard to problem solving or verbal comprehension, the cognitive process of insight is regarded as an unexpected solution for a problem (Subramaniam et al., 2009) and a moment of sudden comprehension (Bowden et al., 2005;Kounios and Beeman, 2009). In one study investigating verbal problemsolving, insight moments were shown to be reflected in an increase in synchronous gamma-band oscillatory brain activity in the right anterior superior temporal gyrus, that was preceded by an increase in alpha-band activity at the right occipital cortex (Jung-Beeman et al., 2004). ...
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... Breakthrough behavior is suggestive of "aha! moments" in psychology (Kounios & Beeman, 2009), where humans experience sudden understanding all at once. 2 It is an interesting question whether aha! moments in humans are similarly associated with smooth underlying improvements in understanding. ...
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Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyond the Imitation Game benchmark (BIG-bench). BIG-bench currently consists of 204 tasks, contributed by 442 authors across 132 institutions. Task topics are diverse, drawing problems from linguistics, childhood development, math, common-sense reasoning, biology, physics, social bias, software development, and beyond. BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models. We evaluate the behavior of OpenAI's GPT models, Google-internal dense transformer architectures, and Switch-style sparse transformers on BIG-bench, across model sizes spanning millions to hundreds of billions of parameters. In addition, a team of human expert raters performed all tasks in order to provide a strong baseline. Findings include: model performance and calibration both improve with scale, but are poor in absolute terms (and when compared with rater performance); performance is remarkably similar across model classes, though with benefits from sparsity; tasks that improve gradually and predictably commonly involve a large knowledge or memorization component, whereas tasks that exhibit "breakthrough" behavior at a critical scale often involve multiple steps or components, or brittle metrics; social bias typically increases with scale in settings with ambiguous context, but this can be improved with prompting.
... Possible generators of frontal Theta oscillations have been suggested to reside in the medial prefrontal cortex (PFC)/anterior cingulate complex (ACC) (Pizzagalli et al., 2002;Mulert et al., 2007a,b). However, although the ACC is notably one of the areas most associated with insight (Dietrich and Kanso, 2010), it is thought to prompt weak, subconscious solutions (Kounios and Beeman, 2009). ...
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Two experiments examined hemispheric differences in information processing that may contribute to solving insight problems. We propose that right-hemisphere (RH) coarse semantic coding is more likely than left-hemisphere (LH) fine semantic coding to activate distantly related information or unusual interpretations of words, and thus more likely to activate solution-relevant information for insight problems. In Experiment 1, after trying to solve insight problems, participants read aloud solution or unrelated target words presented to the left visual field (lvf) or right visual field (rvf). Participants showed greater lvf-RH than rvf-LH priming for solutions for solved problems and priming only in the lvf-RH for unsolved problems. In Experiment 2, participants showed an lvf-RH advantage for recognizing solutions to unsolved problems. These results demonstrate that in a problem-solving context, there was greater activation of solution-relevant information in the RH than in the LH. This activation is useful for recognizing, and perhaps producing, solutions to insight problems.
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There have been many attempts to account theoretically for the effects of anxiety on cognitive performance. This article focuses on two theories based on insights from cognitive psychology. The more recent is the attentional control theory (Eysenck, Derakshan, Santos, & Calvo, 2007), which developed from the earlier processing efficiency theory (Eysenck & Calvo, 1992). Both theories assume there is a fundamental distinction between performance effectiveness (quality of performance) and processing efficiency (the relationship between performance effectiveness and use of processing resources), and that anxiety impairs processing efficiency more than performance effectiveness. Both theories also assume that anxiety impairs the efficiency of the central executive component of the working memory system. In addition, attentional control theory assumes that anxiety impairs the efficiency of two types of attentional control: (1) negative attentional control (involved in inhibiting attention to task-irrelevant stimuli); and (2) positive attentional control (involved in flexibly switching attention between and within tasks to maximize performance). Recent (including unpublished) research relevant to theoretical predictions from attentional control theory is discussed. In addition, future directions for theory and research in the area of anxiety and performance are presented.