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Creativity Research Journal
ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/hcrj20
The Receptive Brain: Up-Regulated Right Temporal
Alpha Oscillation Boosting Aha!
Amna Ghani, Caroline Di Bernardi Luft, Smadar Ovadio-Caro, Klaus-Robert
Müller & Joydeep Bhattacharya
To cite this article: Amna Ghani, Caroline Di Bernardi Luft, Smadar Ovadio-Caro, Klaus-
Robert Müller & Joydeep Bhattacharya (06 Dec 2023): The Receptive Brain: Up-Regulated
Right Temporal Alpha Oscillation Boosting Aha!, Creativity Research Journal, DOI:
10.1080/10400419.2023.2289757
To link to this article: https://doi.org/10.1080/10400419.2023.2289757
© 2023 The Author(s). Published with
license by Taylor & Francis Group, LLC.
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Published online: 06 Dec 2023.
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The Receptive Brain: Up-Regulated Right Temporal Alpha Oscillation
Boosting Aha!
Amna Ghani
a,b
, Caroline Di Bernardi Luft
c
, Smadar Ovadio-Caro
d
, Klaus-Robert Müller
e,f,g,h,i,j
,
and Joydeep Bhattacharya
k
a
Charite Universitatsmedizin Berlin, Berlin, Germany;
b
Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Germany;
c
Brunel
University, London;
d
University of Haifa;
e
Technische Universität Berlin, Berlin, Germany;
f
Berlin Institute for the Foundations of Learning and
Data – BIFOLD, Berlin, Germany;
g
Korea University, Seoul, South Korea;
h
Max Planck Institute for Informatics, Saarbrücken, Germany;
i
Google
DeepMind, Berlin, Germany;
j
Freie Universität Berlin, Berlin, Germany;
k
Goldsmiths University of London
ABSTRACT
Chance favors the prepared mind, said Louis Pasteur. Sometimes, significant breakthroughs occur
when we creatively integrate new information, leading to a creative insight or an Aha! moment,
while at other times when we fail to use a clue, we remain stuck in our habitual thinking patterns. In
this study, we hypothesized that the brain’s transient oscillatory states would characterize its
receptivity or preparedness for such insights. We conducted a real-time brain-state-dependent
cognitive stimulation experiment during insightful problem-solving. We showed that participants
were more successful in utilizing clues and experienced more Aha responses when these clues
were presented at the spontaneously up-regulated state of right temporal alpha oscillation, as
opposed to the down-regulated state. Furthermore, we observed an inverse correlation between
the coupling of alpha oscillation phase and gamma oscillation power and the frequency of insight.
These results shed light on the neural mechanism underpinning the brain’s receptivity to integrate
upcoming semantic information, emphasizing the pivotal role of dynamical brain oscillations in the
Aha! experience.
PLAIN LANGUAGE SUMMARY
In this study, we focused on finding the brain’s receptive state during insightful problem solving –
a state where new (semantic) information is successfully integrated to find creative solutions. We
predicted that the brain’s naturally fluctuating neuronal oscillations, specifically those occurring in
the right temporal region, might indicate this receptivity. We recruited healthy volunteers and
presented them with word association problems, and provided hints contingent on the brain’s
spontaneous up (or down) state of the right temporal alpha oscillation (8–12 Hz) on a trial-by-trial
basis. We found that participants solved more problems and reported more insights or Aha!
moments when hints were presented in the spontaneously up-regulated alpha states. In particular,
this effect was specific to alpha and not beta oscillations (16–22 Hz). We also revealed that a phase-
amplitude cross-frequency coupling between alpha phase and gamma (50–133 Hz) power was
negatively correlated with the frequency of Aha!. This study has established a clear association
between right temporal alpha oscillation and the brain’s receptivity and Aha! experience through
our innovative approach of real-time brain-state-dependent cognitive stimulation. Importantly, our
approach is noninvasive, free from adverse side effects, and does not rely on performance feed-
back, making it convenient, affordable, and readily applicable beyond the laboratory setting.
ARTICLE HISTORY
Received June 9, 2022
Introduction
Creative thinking is an essential skill that enables indi-
viduals to generate novel and useful ideas in various
contexts. To engage in creative thinking, it is crucial to
integrate new concepts with old ones (Turner &
Fauconnier, 1999). This may demand being open and
receptive to new information and combining it with
appropriate previous knowledge, leading to an Aha!
moment, a hallmark of creative cognition. Conversely,
sticking to habitual thinking patterns can lead to stagna-
tion, and the creative solution remains elusive. We
propose that this receptivity to new information during
creative problem-solving would be associated with the
spontaneous fluctuations of alpha oscillation, occurring
just before the new information becomes available.
Alpha (8–12 Hz) oscillation represents a prominent
feature of spontaneous brain activity and is often
CONTACT Joydeep Bhattacharya [email protected] Department of Psychology, Goldsmiths University of London, London, UK
Supplemental data for this article can be accessed online at https://doi.org/10.1080/10400419.2023.2289757
CREATIVITY RESEARCH JOURNAL
https://doi.org/10.1080/10400419.2023.2289757
© 2023 The Author(s). Published with license by Taylor & Francis Group, LLC.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use,
distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted
Manuscript in a repository by the author(s) or with their consent.
considered as an effective indicator of cortical excit-
ability (Klimesch, Sauseng, & Hanslmayr, 2007). Alpha
oscillation has been extensively studied in sensory
attentional processing (Peylo, Hilla, & Sauseng,
2021). While the precise neurophysiological mechan-
isms governing alpha oscillation’s role in attentional
processing remain a subject of debate (Schneider,
Herbst, Klatt, Wöstmann, & Keitel, 2022), it is widely
recognized that alpha oscillation within the visual
cortex (i.e., within the task-relevant brain regions)
represents a transient modulation of local cortical
excitability; this modulation, in turn, influences the
processing of upcoming visual stimuli. A growing
body of research has delved into the pivotal, and
potentially causal, role of alpha oscillation in the pres-
timulus period in shaping poststimulus responses. For
example, studies have shown that prestimulus alpha
activity over visual areas can predict perceptual task
performance (Michail, Toran Jenner, & Keil, 2021),
impact the perception of phosphenes (Romei et al.,
2008), influence perceptual dominance in multisen-
sory illusion (Yun et al., 2020), and play a role in
temporal binding across sensory modalities (Buergers
& Noppeney, 2022).
Alpha oscillation has also been robustly linked to
creativity. For example, alpha oscillation is consistently
observed in several cortical regions during divergent
thinking tasks (see, for a review, Fink & Benedek, 2014).
Further, alpha oscillation is associated with creative
insights, characterized by the sudden emergence of
a solution into conscious awareness without any fore-
warning, often described subjectively as the “Aha!”
moment. In a seminal study exploring insight (Jung-
Beeman et al., 2004), participants solved remote associate
tests (RAT), where they were asked to find a solution
word that could make three compound words or familiar
phrases with three cue words (e.g., walker/main/sweeper;
solution: street). Solutions were obtained either by
insight, where they appeared suddenly, or by analysis,
involving a conscious, deliberate and incremental
approach. The authors reported heightened alpha power
specifically over the right posterior parietal region around
1 s before the moment of insight in insight trials com-
pared to the analysis trials. In a follow-up study by the
same group of authors (Kounios et al., 2006), they showed
that alpha power during the 2 s prestimulus period pre-
ceding the RAT trials was higher for insight trials than for
analysis trials; this effect was observed over a broad range
of brain regions, including right temporal, right inferior
frontal, mid frontal cortex and left temporal areas. The
results suggest heightened preparatory processing in the
semantic network, influenced by the top-down control of
the cognitive control network (Kounios et al., 2006).
The first preliminary evidence suggesting alpha oscil-
lation as a marker of the brain’s receptivity was provided
by our previous study (Sandkühler, Bhattacharya, &
Zak, 2008). In this study, we recorded the EEG of
participants while they solved RAT trials. When
a RAT problem could not be solved within 45 s, we
presented a hint or a clue (e.g., s _ _ _ _ t) with hints
revealing the solution word partially but always includ-
ing the first letter. We found higher alpha power in the
right temporal region from −0.2 to 0.3 s after the onset
of hint presentation for trials that resulted in a correct
solution, compared to trials that led to a timeout (when
no solution was found within the allotted time of 7
s after the hint). To solve RAT problems, individuals
need to suppress the most obvious associations of at
least one of the three given words and instead find
a fourth word associated with all three words. This
alpha-band activity before the hint might reflect the
inhibition of ongoing, habitual semantic processing,
allowing a competitive but weaker, unconscious seman-
tic processing to integrate with the hint. This integration
eventually leads to the production of the solution or
target word, reaching the level of conscious awareness
(Bowden & Jung-Beeman, 2003a; Bowers, Regehr,
Balthazard, & Parker, 1990; Sandkühler, Bhattacharya,
& Zak, 2008).
While this early finding of right temporal alpha oscil-
lation in the brain’s receptivity was promising, the evi-
dence remains purely correlational. To establish any
form of causality, it is essential to regulate alpha oscilla-
tion in a controlled manner and then subsequently
investigate its impact on creative insights. The two
most widely used techniques for modulating brain oscil-
lations to demonstrate causal links between specific
brain oscillation and cognitive processes are transcranial
alternating current stimulation (tACS) and neurofeed-
back (Herrmann, Strüber, Helfrich, & Engel, 2016). The
first technique, tACS, involves applying current at spe-
cific frequencies to boost neural oscillation at the same
frequency (Wischnewski, Alekseichuk, & Opitz, 2023).
Regarding creative cognition, boosting alpha power by
10 Hz tACS, but not by 40 Hz, in the frontal region has
been shown to improve divergent thinking task perfor-
mance (Lustenberger, Boyle, Foulser, Mellin, &
Fröhlich, 2015). More relevant to our current study,
we found earlier that under 10 Hz tACS to the right
temporal region, participants solved more RAT pro-
blems with words that shared misleading associations
(Luft, Zioga, Thompson, Banissy, & Bhattacharya,
2018), corroborating the critical role of right temporal
alpha activity in suppressing obvious but misleading
associations. Notably, another brain stimulation study
2A. GHANI ET AL.
has established a causal link between the right temporal
brain region and insight problem-solving (Salvi,
Beeman, Bikson, McKinley, & Grafman, 2020); how-
ever, because the stimulation method used was tDCS
in which a direct current with specific polarity was
applied to a target brain region, no specific inferences
about the involved oscillations could be made out of this
study. The second technique, neurofeedback, measures
brain activity in real-time and provides participants
with feedback to help them self-regulate specific brain
activity (Sitaram et al., 2017). Feedback can be overt,
where participants are explicitly aware of the nature of
the feedback, or covert, where targeted brain activity is
reinforced implicitly (Ramot & Martin, 2022). Past
research has demonstrated the usefulness of neurofeed-
back in boosting creativity (see for a review, Gruzelier,
2014). However, the causal links between specific brain
activity patterns and constituent cognitive processes
during creative problem-solving remain elusive. OIt is
noteworthy that the effects of both tACS and neurofeed-
back are longer lasting, ranging from minutes to hours,
limiting their value in investigating the brain’s receptiv-
ity during creative problem-solving.
An appropriate method in this context is brain-state-
dependent cognitive stimulation (Jensen et al., 2011),
which allows for the manipulation of cognitive proces-
sing, such as the brain’s receptivity, by considering real-
time brain activity, specifically the right temporal alpha
oscillation. This manipulation could be achieved by
adjusting the stimuli presented to the participant based
on the real-time evaluation of their brain activity
(Hartmann, Schulz, & Weisz, 2011). The efficacy of
this method has been demonstrated in research studies
using single-neuron recording (Cerf et al., 2010) and
EEG (Vigué-Guix, Morís Fernández, Torralba Cuello,
Ruzzoli, & Soto-Faraco, 2022). In our current study, we
monitored the ongoing right temporal alpha oscillation
in real-time while participants were solving RAT pro-
blems. If a participant was unable to solve a problem
within an allotted time, we presented a hint; the timing
of the hint was contingent on the state, up or down, of
the right temporal alpha oscillation. Our primary
hypothesis was that hints followed by an elevated right
temporal alpha state would result in more correct
responses and frequent insights, implying the impor-
tance of right temporal alpha oscillation in the brain’s
receptivity. More particularly, we sought to examine
whether hints provided contingent on an increase in
the right temporal alpha power would improve partici-
pants’ overall accuracy and lead to more frequent
insights compared to hints provided contingent on
a decrease in the right temporal alpha power.
Although our unique real-time experimental design
was exclusively centered around monitoring alpha oscil-
lation in real-time, other brain oscillations are also
involved in creative problem-solving (Jung-Beeman
et al., 2004; Oh, Chesebrough, Erickson, Zhang, &
Kounios, 2020; Sandkühler, Bhattacharya, & Zak, 2008;
Sheth, Sandkühler, & Bhattacharya, 2009). Gamma oscil-
lation (30–50 Hz) is particularly relevant for creative
insights due to its involvement in multiple cognitive
processes, including selective attention (Fries, Reynolds,
Rorie, & Desimone, 2001), retrieval (Sederberg et al.,
2007), semantic integration (Jung-Beeman et al., 2004),
and conscious awareness (Summerfield, Jack, & Burgess,
2002) – all of which are essential for insights or Aha!
moments (Stevens & Zabelina, 2019). A previous study
showed that applying 40 Hz tACS over the right temporal
region resulted in a substantial (20%) increase in insights
during the solving of RAT problems (Santarnecchi et al.,
2019); this finding suggests a causal role of gamma oscil-
lation in the right temporal brain region in facilitating
creative insight. Although alpha and gamma activity have
typically been studied independently in the context of
creative cognition, some key findings have underscored
the coupling between slow (theta and alpha) and fast
(gamma) oscillations as a characteristic of enhanced com-
munication between neuronal assemblies during cogni-
tive processing (Canolty & Knight, 2010; Canolty et al.,
2006; Esghaei, Treue, & Vidyasagar, 2022). In particular,
gamma power coupled with the alpha phase acts as a filter
for incoming information (Bonnefond, Jensen, & Tort,
2015) so that gamma oscillation retains the information
while alpha oscillation protects that information from
distractors (Park et al., 2016; Roux & Uhlhaas, 2014).
This alpha-gamma phase-amplitude coupling provides
a mechanism for organizing and controlling the flow of
information (Jensen, Gips, Bergmann, & Bonnefond,
2014). Therefore, we had a secondary hypothesis that
posited that the nature of alpha-gamma phase-
amplitude coupling at the hint presentation could be
a determining feature of solutions reported as Aha!
effect – insight responses. More specifically, we aimed
to observe whether alpha – coupled gamma power sup-
pression would be related to the frequency of insights.
Materials and methods
Participants
Two independent groups of healthy human adults par-
ticipated in two separate conditions, alpha and beta (as
control). Each condition had two separate sessions – up
and down – held a week apart. Each participant
CREATIVITY RESEARCH JOURNAL 3
attended two separate sessions on two separate days
with an intersession interval of about seven days. The
sessions were named alpha-up and alpha-down for the
alpha condition (beta-up and beta-down for the beta
condition). There were two sets of 100 remote associa-
tion problem sets (RAT-A and RAT-B). These two
problem sets and two sessions (up or down) were coun-
terbalanced across participants. The alpha condition
had seventeen participants, and the beta condition had
nineteen participants (10 females, 24.11 ± 2.73 years).
All participants were healthy human adults and right-
handed university students, and they gave informed
consent before participating in the experiments. The
study protocol was approved by the Local Ethics
Committee.
Task and procedure
In each session, participants were tasked with solving 100
compound versions of the remote associate test,
RAT (Bowden & Jung-Beeman, 2003b; Sandkühler,
Bhattacharya, & Zak, 2008). In each RAT trial, three cue
words (e.g., river, note, account) were presented on
a computer screen; the task was to find a solution word
that would make three compound words with the pre-
sented cue words (e.g., the solution word is “bank” in this
case: riverbank, banknote, bankaccount). As mentioned
earlier, previous research suggests that a RAT problem can
be solved via insight (i.e., the solution appearing suddenly
in awareness without any prior conscious forewarning) or
analysis (i.e., the solution appearing gradually after work-
ing out in a deliberate, conscious manner) (Jung-Beeman
et al., 2004; Rothmaler, Nigbur, & Ivanova, 2017).
Furthermore, an extensive body of research demonstrates
the suitability of RAT problems for studying the neural
markers of insight in neuroimaging studies (for reviews,
see Bowden, Jung-Beeman, Fleck, & Kounios, 2005;
Kounios & Beeman, 2009).
In this study, on each trial (as shown in Figure 1),
participants were initially given 20 s to solve a RAT
problem. They were asked to press a button as soon as
they found the solution word without engaging in
detailed mental checks. Subsequently, participants ver-
balized the solution and reported whether they obtained
it with insight or non-insight, as explained to them
beforehand, after previous research (Jung-Beeman
et al., 2004). Afterward, they proceeded to the next
trial. If a solution was not found within the initial 20
s period, we provided a hint showing the number of
letters in the solution word but revealing only the first
letter (e.g., “b _ _ _”). However, the timing of hint
presentation depended on the fluctuations (either up
or down) of ongoing alpha oscillation over the right
temporal brain regions. For the alpha condition, we
computed the average alpha power (8–12 Hz) across
the three right temporal electrodes (FT8, T8, and TP8)
in real-time, with power computed with a 1 s window
and a 50% overlap on each trial. We obtained the mean
Figure 1. Brain-state-dependent cognitive stimulation paradigm. participants solved RAT problems. Three cue words were
presented on each trial, and participants had to find a fourth word, which makes a compound word with each of these three words.
We calculated alpha power over the right temporal brain region (FT8, T8, TP8) in real-time during the problem presentation to
estimate a trial-specific threshold. If a solution was not found within the first 20 s, a hint (revealing the first letter of the solution) was
provided at a time when the transient right temporal alpha power was higher (shown in red for alpha up) or lower (blue for alpha-
down) than the trial-specific threshold.
4A. GHANI ET AL.
(μ) and standard deviation (σ) of the right temporal
alpha power for the first 20 s of the problem presenta-
tion. For the alpha-up condition, we set a trial-specific
threshold (T) as follows: T = μ + 1.5σ; a hint was pre-
sented in the alpha-up condition if the transient right
temporal power on a given trial surpassed this trial-
specific threshold. The alpha-down was the opposite:
the trial-specific threshold was T = μ − 1.5σ, and a hint
was presented if the transient alpha power dropped
below this threshold of that specific trial. Following
the hint presentation, the participants were given
a further 15 s to solve the problem. Like earlier, they
verbalized the solution and reported whether it was
obtained via insight or analysis. The trial was terminated
if no solution was found within 15 s following the hint
presentation.
The experimental task, including instructions and
stimuli, was presented on a PC using the MATLAB
Toolbox Cogent 2000 (http://www.vislab.ucl.ac.uk/
cogent_2000.php). Real-time processing of the EEG sig-
nals was performed using ActiView®, the acquisition
software of the BiosSemi ActiveTwo EEG system.
ActiView, developed within the LabVIEW® program-
ming environment, was modified to enable real-time
processing of the EEG signals. This modified ActiView
program communicated with the MATLAB-based sti-
mulus-presentation program through a parallel port
(DB-25). At the start of a RAT trial, a trigger was sent
from the MATLAB Cogent software to the ActiView
program to initiate real-time signal processing and cal-
culate the trial-specific threshold. When the ActiView
program detected an increase or decrease in alpha
power over three right temporal electrodes, exceeding
or falling below the trial-specific threshold for the alpha
up or down condition, it sent a trigger back to the
MATLAB program. This trigger prompted the presen-
tation of a hint on the screen. Participants responded by
pressing a button, and all their responses were recorded
through the MATLAB Cogent program.
The entire protocol remained similar for the beta-up
and beta-down conditions, with the exception that we
monitored the right temporal beta power (16–24 Hz) in
real-time. Hints were presented if the transient beta
power exceeded or fell below the trial-specific threshold,
which was calculated within the beta frequency band, as
described above.
EEG recordings
We recorded EEG signals with sixty-four Ag-AgCl elec-
trodes placed according to the extended 10–20 electrode
placement system and amplified by a BioSemi
ActiveTwo® amplifier. Vertical and horizontal electro-
occulograms were recorded by placing electrodes above
and below the left eye and at the outer canthus of each
eye, respectively. The sampling frequency was 512 Hz.
We used the MATLAB Toolbox EEGLAB (Delorme &
Makeig, 2004) and custom MATLAB scripts for EEG
preprocessing. EEG data were high-pass filtered at 1 Hz
and algebraically re-referenced to the average of the two
earlobes. The line-noise interference at 50 Hz was
removed by the EEGLAB CleanLine function. Artifact
rejection was done in a semiautomatic fashion. First, we
removed sections with large artifacts by visual inspec-
tion and replaced bad channels by spline interpolation.
Second, we applied independent component analysis
(ICA) to correct eye-blink-related artifacts. Finally, the
ICA-cleaned sections were visually inspected to remove
any remaining large artifacts with amplitudes over
±120 µV.
Behavioral analysis
We calculated the accuracy for each participant as the
number of correct solutions obtained in each session
(up, down) for each condition (alpha, beta). Further, we
categorized the accuracy based on whether solutions
were obtained with or without a hint. Further, we cal-
culated the percentage of correct insights with hints out
of the total problems solved with a hint. The data were
normalized using the square-root transformation and
analyzed using mixed ANOVA; normality was validated
by the Shapiro-Wilk normality test. Finally, we used
Wilcoxon and paired-sample tests to compare the
reported insight obtained with hints in alpha-up and
alpha-down sessions. The statistical analysis was per-
formed in R and SPSS software packages.
EEG analysis
We obtained EEG sources using the Brainstorm toolbox
(Tadel, Baillet, Mosher, Pantazis, & Leahy, 2011) for the
epoch spanning 4 s before and 3 s after the hint pre-
sentation (−4 s to + 3 s). First, we calculated the stan-
dard head model using the openMEG toolbox, and then,
we obtained cortical sources using sLORETA without
selecting any noise covariance option. Once the sources
were obtained, we extracted the scout time series for
these sources using the Desikan-Killiany atlas (Desikan
et al., 2006), which comprises 68 regions of the brain.
Our primary focus was the right superior temporal
gyrus. However, we also explored the inferior frontal
gyrus (IFG) due to its known involvement in semantic
and cognitive control (Becker, Sommer, & Kühn, 2020;
Salvi, Beeman, Bikson, McKinley, & Grafman, 2020;
CREATIVITY RESEARCH JOURNAL 5
Stramaccia, Penolazzi, Altoè, & Galfano, 2017). Previous
research has suggested that a balance between the right
and left IFG is crucial for achieving better performance
in creative tasks (Mayseless & Shamay-Tsoory, 2015);
nonetheless, for this study, we limited our investigation
to the right hemispheric region.
In this study, our primary hypothesis revolved around
the right temporal alpha oscillation. Our secondary
hypothesis was related to alpha-gamma phase-
amplitude coupling, which was studied using cross-
frequency coupling (CFC) maps (Canolty & Knight,
2010). The analysis focused on a specific time window,
covering 4 s before and 3 s after the presentation of a hint.
The CFC maps were obtained using the Brainstorm tool-
box for 68 brain regions. CFC maps are essentially time-
frequency decompositions that evaluate the relationship
between low-frequency and high-frequency oscillations.
These CFCs were quantified using phase-amplitude cou-
pling (PAC) or nesting, which means that the amplitude
of high-frequency oscillation is modulated by the phase
of low-frequency oscillation. We specifically focused on
the upper alpha oscillation at 12 Hz alpha as the average
gamma power (60–100 Hz) was largest at the 12 Hz alpha
phase (Figure S1 in the Supplementary Materials).
Results
Table 1 shows the average percentages of problems
solved with and without a hint for alpha-up, alpha-
down, beta-up and beta-down conditions, individually.
Participants in the alpha-up and alpha-down conditions
solved an average of 20.8% and 16.9% of the problems
without a hint, respectively. For the beta-up and beta-
down conditions, the values for the same are 20.9% and
20.8%, respectively. With hints, the solution rates for
alpha-up and alpha-down were 37.0% and 34.5%,
respectively; for the beta-up and beta-down conditions,
the values for the same are 39.6% and 40.3%.
We analyzed these solution rates without a hint by a 2
(frequency: alpha, beta) x 2 (session: up, down) mixed
ANOVA. There was no main effect of session (F(1,32 =
3.38, p=.08, partial-η
2
=.09) or frequency (F(1,32)=.62,
p=.44, partial-η
2
=.02); the interaction between fre-
quency and session was also not significant (F(1,32) =
2.50, p=.12, partial-η
2
=.07).
A similar analysis for overall reported insights
revealed a main effect of frequency, F(1,32) = 8.99,
p = .005, partial-η
2
= 0.22 and session, F(1,32) = 4.34,
p = .045, η
2
= 0.12 and a significant interaction between
frequency and session, F(1,32) = 4.87, p = .035, η
2
=
0.013. Planned contrasts revealed Figure 2(A) that the
alpha-up session (M = 27.11) elicited significantly
higher (p = .017) reported insights than the alpha-
down session (M = 20.8); no such difference (p=.091)
was observed between the beta-up (M = 35.94) and beta-
down (M = 35.53) sessions. Another 2 × 2 mixed
ANOVA for overall correct solutions revealed
a marginal main effect of frequency, F(1, 32) = 4.03, p
= .053, η
2
= 0.11, and a significant interaction between
frequency and session, F(1,32) = 5.38, p = .027, η
2
= 0.14.
Planned contrasts revealed that the alpha-up session (M
= 47.64) elicited significantly more (p = .033) correct
solutions than alpha-down session (M = 42.64); no
such difference (p = .43) was found between the beta-
up (M = 50.29) and beta-down (M = 51.53) sessions.
Further, a Wilcoxon signed-rank test indicated that
hints presented on alpha-up state led to more correct
insights than those on alpha-down (V = 124, p = .011;
Figure 2(B). Further descriptions and analysis of various
behavioral measures (e.g., average time for a hint to
appear, average solution time, average solution rate for
non-insights with or without hints) are included
(Figures S2-S8) in the Supplementary Materials.
Next, we investigated cross-frequency coupling by
CFC maps averaged across participants. These maps
were used to evaluate the coupling between alpha (low-
frequency) and gamma (high-frequency) oscillations
around the hint presentation, analyzed separately for
alpha-up and alpha-down sessions Figure 3(A). We
observed a moderate negative correlation between the
frequency of insights and the alpha-coupled gamma
power in three brain regions (depicted in Figure 3(B),
which are notably associated with semantic processing
(right superior temporal: r(17)=-0.51, p = .038; pars
opercularis: r(17)=-0.52, p = .03; pars triangularis: r
(17)=-0.55, p = .022). Participants reporting more
insights exhibited more gamma suppressions by the
phase of 12 Hz alpha oscillation around the hint pre-
sentation. Importantly, these correlations were
observed exclusively for the alpha-up session and
were not evident for the alpha-down session
Figure 3(B).
We also demonstrated the effectiveness of our brain-
state-dependent paradigm by visualizing time-
frequency representations of problems solved with
Table 1. Averages of percent of problems solved without a hint,
with a hint across alpha-up, alpha-down, beta-up, and beta-
down.
Solved
without hint
Solved
with hint
Average percent SD Average percent SD
Alpha-up 20.8 9.53 37.0 10.3
Alpha-down 16.9 6.34 34.5 5.39
Beta-up 20.9 7.10 39.6 9.66
Beta-down 20.6 7.66 40.3 7.37
6A. GHANI ET AL.
hints. During the alpha-up session, we observed, as
expected according to our experimental manipulation,
a substantial increase in the alpha activity approximately
1 s before the hint onset (Supplementary Materials:
Figure S9A); its scalp map showed increased alpha
power concentrated around the right temporal regions
(Figure S9B). Further, CFC maps provided additional
insights into the power differences between the alpha-
up and alpha-down sessions (Figure S9C).
Discussion
Efficiently integrating complex semantic information
necessitates the seamless merging of external informa-
tion with preexisting knowledge. In this study, using
a novel brain-state-dependent cognitive stimulation
paradigm during insight problem solving, we showed
that hints were effectively utilized to obtain solutions
with creative insights when hints were presented during
an up-regulated alpha state as opposed to a down-
regulated one. This outcome supports our primary
hypothesis and is consistent with our previous study
which has indicated the predictive nature of right tem-
poral alpha oscillation in successfully leveraging hints
(Sandkühler, Bhattacharya, & Zak, 2008). Further, it
aligns with the findings of another of our prior studies
that boosting alpha oscillation by 10-Hz tACS resulted
in improved access to remote associations (Luft, Zioga,
Thompson, Banissy, & Bhattacharya, 2018).
Our real-time-based paradigm demonstrates that the
brain’s receptivity can be characterized by controlling
the timing, intensity, and precision of ongoing alpha
oscillation, which acts as a mechanism of pulsed-
inhibition (Klimesch, 2012). Through such pulsed inhi-
bition, we postulate that alpha oscillation suppresses
gamma band activity and deactivates the neuronal
population responsible for decoding the most obvious
but incorrect associations. Further, the alpha phase
might be linked to stimuli-bound features or associa-
tions (Brickwedde, Krüger, & Dinse, 2019), and by sup-
pressing gamma, alpha oscillation can regulate the flow
of information, either enhancing or degrading task per-
formance (Bonnefond, Jensen, & Tort, 2015). Thus, our
secondary hypothesis was also supported, suggesting
that the observed alpha-gamma phase-amplitude cou-
pling when hints were presented could be related to the
brain’s receptivity. This interaction between slower
alpha and faster gamma oscillations may reflect coding
principles underlying the brain’s ability to retrieve
remote ideas and integrate new information (Varga &
Manns, 2021).
A recent study hypothesizes that insights occur dur-
ing cognitive navigation and involve rapid changes in
cellular plasticity (Aru, Drüke, Pikamäe, & Larkum,
2023). Insight is thought to happen when a new idea
or stimulus is introduced to the brain, activating specific
hippocampal neurons and creating connections
between previously unrelated concepts. This process
results in the formation of a new concept field, and
Figure 2. Higher reported insights and correct responses in the alpha-up session. (A) numbers of reported insights (i.e., solutions
associated with an Aha! experience) and correct solutions were significantly higher in alpha-up than in alpha-down session; no
significant differences were found in control sessions (beta-up vs. beta-down). (B) correct insights were more frequent during alpha-
up hints than alpha-down. (C) insights and correct responses with hints were positively correlated with insights and correct responses
without hints.
CREATIVITY RESEARCH JOURNAL 7
such insight depends on encountering a new stimulus.
We suggest that hints, as presented in our study, might
trigger such opportunistic integration, leading to the
emergence of Aha! moments (Moss, Kotovsky, &
Cagan, 2011), supporting the prepared mind account
of insight (Seifert, Meyer, Davidson, Patalano, & Yaniv,
1995).
Two surprising findings emerged from our study: (i)
the association between alpha-gamma coupling and
insight frequency at the participant level and (ii) the
session-wide effect of increased insight during the alpha-
up session. In our earlier study (Luft, Zioga, Thompson,
Banissy, & Bhattacharya, 2018), we did not observe
enhanced insight experiences when we boosted temporal
alpha by 10-Hz tACS. However, in the current study, we
observed that presenting occasional new information
during a spontaneously up-regulated alpha state led to
an increase in insights. As mentioned earlier, the alpha
phase might encode the gamma activity, acting as a gate
to control the flow of information from the neuronal
population carrying misleading associations. This gating
mechanism facilitates the retrieval of distant, remote
associations, resulting in a sudden burst of conscious
awareness – an insightful experience or an Aha! moment.
The session-wide effect of boosting this subjective experi-
ence implies a degree of neuroplasticity and cumulative
effects. However, it is crucial to note that this accumula-
tion was observed exclusively in the alpha-up session. We
could speculate that participants were involved in impli-
cit self-regulation, whose goal was solving a problem
(Muñoz-Moldes & Cleeremans, 2020). This active self-
regulation strategy appeared to influence the timing of
problem presentation, irrespective of hints. We observed
an upward trend in right temporal alpha power during
the alpha-up session compared to the alpha-down ses-
sion at the time of problem presentation (Figure S10 in
Supplementary Materials). This suggests that participants
might have implicitly learned to up-regulate their right
Figure 3. Alpha-gamma phase-amplitude coupling during brain-state-dependent cognitive stimulation. (A) Cross-frequency
coupling maps around hint (−4 s to + 3 s after hint presentation) at the right superior temporal gyrus. The time-frequency maps were
plotted for low nesting frequency (overlaid black) as alpha at 12 hz while the higher frequency was selected as broadband gamma 37–
133 hz showing alpha phase-coupled gamma power. We observed different coupling strengths between the alpha phase and the
gamma power for alpha-up and alpha-down sessions. (B) the scatter plots between the reported insights and the average gamma
(60–100 hz) power (12 hz alpha phase-coupled) at three selected brain regions (right superior temporal, pars opercularis, and pars
triangularis) separately for alpha-up and alpha-down sessions. Significant negative correlations (r ~0.5, p < .05, see text for details)
were observed only during the alpha-up session.
8A. GHANI ET AL.
temporal alpha oscillation. Interestingly, we did not
observe a similar boost in right temporal alpha power
at the problem presentation during the beta sessions
(Figure S11), nor did we detect any enhancement of
beta power at the problem presentation in the beta ses-
sions (Figure S12). Therefore, this implicit boosting of
brain oscillation was specific to the alpha-up session.
Of note, our experimental approach of brain-state-
dependent cognitive stimulation overlaps with a covert
form of neurofeedback (Ramot & Martin, 2022), where
training occurs implicitly. In both approaches, partici-
pants, much like in our study, do not receive explicit feed-
back about their brain activity, resulting in implicit
learning without conscious awareness of the association
between reward and brain activations. Interestingly,
experiencing insight is intrinsically rewarding and is asso-
ciated with activations of the orbitofrontal cortex (Oh,
Chesebrough, Erickson, Zhang, & Kounios, 2020) and
the midbrain dopaminergic network (Tik et al., 2018).
These rewarding feelings associated with achieving solu-
tions with insight may encourage participants to develop
more effective implicit strategies for spontaneous self-
regulation of the right temporal alpha oscillation (Oh,
Chesebrough, Erickson, Zhang, & Kounios, 2020; Ramot,
Grossman, Friedman, & Malach, 2016).
Our study has several limitations. First, while our
dataset comprised approximately 70 separate EEG ses-
sions, each consisting of 100 trials, which is adequate
for testing our primary hypotheses, the number of
participants ( = 17) in each condition was relatively
on the lower side. Second, we focused our investigation
solely on the right temporal brain region. While this
choice was entirely based on our previous research
(Sandkühler, Bhattacharya, & Zak, 2008) and the pro-
minent role of the right temporal region in semantic
processing and integration (Binder, Desai, Graves, &
Conant, 2009; Lambon Ralph, Cipolotti, Manes, &
Patterson, 2010; Tranel, Damasio, & Damasio, 1997),
we could not rule out the potential contribution of
other brain areas to the brain’s receptivity, as studied
here. For instance, it would be useful to explore the role
of left temporal regions because alpha oscillation over
this brain region in the resting state (Erickson et al.,
2018) or the prestimulus state (Kounios et al., 2006) is
associated with insight. Our current study could not
include additional brain regions due to practical lim-
itations. Introducing more brain regions and other
types of oscillations related to insight would have
overly complicated the experiment. Future research
could investigate this issue further, focusing on other
brain regions, including the left temporal and left
fronto-polar cortex (Salvi, Beeman, Bikson,
McKinley, & Grafman, 2020). Third, we conducted
our cross-frequency coupling analysis at the partici-
pant rather than the trial level. We acknowledge that
evaluating a trial-by-trial cross-frequency coupling
approach would have been more informative, espe-
cially in the context of dynamically presenting hints.
Future research could explore trial-by-trial cross-
frequency coupling to study the dynamics of achieving
insights. Surprisingly, only a few studies have investi-
gated functional connectivity analysis within
a frequency band for insights (Razumnikova, 2007),
and virtually nothing is known about insight-related
cross-frequency coupling. Therefore, our present find-
ings represent an initial step toward revealing the
interdependence between large-scale brain oscillations
during insight problem solving. Fourth, our real-time
monitoring focused exclusively on right temporal
alpha oscillation; however, through offline analysis,
we showed that the alpha phase was coupled with
gamma oscillation. Therefore, in future studies using
a similar brain-state-dependent stimulation approach,
it may be interesting to present hints contingent on the
nature of alpha-gamma coupling. Finally, while it is
not necessarily a limitation per se, we would like to
comment on the potentially causal role of right tem-
poral alpha oscillation in the brain’s receptivity.
Establishing causality is a fundamental question in
most scientific domains, including cognitive neu-
roscience (Marinescu, Lawlor, & Kording, 2018).
Inferring causality from noninvasive brain stimulation
(Bergmann & Hartwigsen, 2021) and neurofeedback
(Bergmann & Hartwigsen, 2021) is not straightfor-
ward. We propose that the right temporal brain oscil-
lation may not be the sole cause of the brain’s
receptivity, but rather, our findings suggest that it
could be one component of a causal network contri-
buting to the brain’s receptivity.
In conclusion, our results establish a clear association
between specific neuronal oscillatory patterns and the
brain’s receptivity during insightful problem solving.
Further, we demonstrate a nonobtrusive way of boost-
ing creative insight.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Author Contributions
J.B. conceived the research idea; A.G. and C.D.B.L collected
the data; A.G. analyzed the data; C.D.B.L, S.O-C., K-L. M
provided data analysis expertise; J.B. and A.G. wrote the
paper; J.B. received the research funding and provided overall
research supervision
CREATIVITY RESEARCH JOURNAL 9
Funding
The research was partially supported by the CREAM project
funded by the European Commission Grant 612022. This
publication reflects the views only of the authors, and the
European Commission cannot be held responsible for any
use which may be made of the information contained therein.
ORCID
Klaus-Robert Müller http://orcid.org/0000-0002-3861-
7685
Joydeep Bhattacharya http://orcid.org/0000-0003-3443-
9049
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