Can transcranial direct current stimulation (tDCS) of the cerebellum improve implicit social and cognitive sequence learning?

Abstract

Accumulating evidence shows that the posterior cerebellum is involved in mentalizing inferences of social events by detecting sequence information in these events, and building and updating internal models of these sequences. By applying anodal and sham cerebellar transcranial direct current stimulation (tDCS) on the posteromedial cerebellum of healthy participants, and using a serial reaction time (SRT) task paradigm, the current study examined the causal involvement of the cerebellum in implicitly learning sequences of social beliefs of others (Belief SRT) and non-social colored shapes (Cognitive SRT). Apart from the social or cognitive domain differences, both tasks were structurally identical. Results of anodal stimulation (i.e., 2 mA for 20 min) during the social Belief SRT task, did not show significant improvement in reaction times, however it did reveal generally faster responses for the Cognitive SRT task. This improved performance could also be observed after the cessation of stimulation after 30 min, and up to one week later. Our findings suggest a general positive effect of anodal cerebellar tDCS on implicit non-social Cognitive sequence learning, supporting a causal role of the cerebellum in this learning process. We speculate that the lack of tDCS modulation of the social Belief SRT task is due to the familiar and overlearned nature of attributing social beliefs, suggesting that easy and automatized tasks leave little room for improvement through tDCS.

Full text

Original article
Can transcranial direct current stimulation (tDCS) of the cerebellum improve
implicit social and cognitive sequence learning?
Qianying Ma
a,
*, Min Pu
a,
*, Meijia Li
a
, Naem Haihambo
a
, Kris Baetens
a
, Elien Heleven
a
,
Natacha Deroost
a
, Chris Baeken
a,b,c,d,e
, Frank Van Overwalle
a,
*
a
Department of Psychology, Center for Neuroscience, Vrije Universiteit Brussel, Pleinlaan 2, Brussels 1050, Belgium
b
Faculty of Medicine and Health Sciences, Department of Head and Skin, Ghent Experimental, Belgium
c
Psychiatry (GHEP) lab, Ghent University, Ghent, Belgium
d
Department of Psychiatry, University Hospital (UZBrussel), Brussels, Belgium
e
Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
ARTICLE INFO ABSTRACT
Accumulating evidence shows that the posterior cerebellum is involved in mentalizing inferences of social events
by detecting sequence information in these events, and building and updating internal models of these sequences.
By applying anodal and sham cerebellar transcranial direct current stimulation (tDCS) on the posteromedial cere-
bellum of healthy participants, and using a serial reaction time (SRT) task paradigm, the current study examined
the causal involvement of the cerebellum in implicitly learning sequences of social beliefs of others (Belief SRT)
and non-social colored shapes (Cognitive SRT). Apart from the social or cognitive domain differences, both tasks
were structurally identical. Results of anodal stimulation (i.e., 2 mA for 20 min) during the social Belief SRT task,
did not show significant improvement in reaction times, however it did reveal generally faster responses for the
Cognitive SRT task. This improved performance could also be observed after the cessation of stimulation after
30 min, and up to one week later. Our findings suggest a general positive effect of anodal cerebellar tDCS on
implicit non-social Cognitive sequence learning, supporting a causal role of the cerebellum in this learning pro-
cess. We speculate that the lack of tDCS modulation of the social Belief SRT task is due to the familiar and over-
learned nature of attributing social beliefs, suggesting that easy and automatized tasks leave little room for
improvement through tDCS.
Keywords:
Transcranial direct current stimulation (tDCS)
Brain stimulation
Cerebellum
Social sequencing
Cognitive sequencing
Serial reaction time task (SRT)
Introduction
The cerebellum has traditionally been considered as a uniquely
motor controller, helping people learn automatized motor skills by
building internal models of sequenced movements (Ito, 2008). However,
emerging studies have revealed that cerebellar areas also play a critical
role in mental, non-motor, processes (Ito, 2008;Van Overwalle et al.,
2014). Large scale meta-analyses of fMRI studies with healthy partici-
pants revealed robust cerebellar involvement in cognitive functions,
such as attention, working memory, executive control, and learning
sequences (Guell et al., 2018;Schmahmann et al., 2019;Van Overwalle
et al., 2014). More recently, neuroimaging studies have shown that the
cerebellum also contributes to the social domain, such as mentalizing,
which is the process of understanding others’metal states (Ito, 2008;
Metoki et al., 2021a;Premack &Woodruff, 1978;Van Overwalle et al.,
2014). Meta-analyses of fMRI studies also revealed robust cerebellar
activation in various mentalizing tasks, especially the posterior
cerebellar Crus I &II (Metoki et al., 2021b;Van Overwalle et al., 2015;
Van Overwalle et al., 2020). Further support for cerebellar involvement
in social processes comes from various clinical studies. For example,
patients with cerebellar damage display social deficits in inferring
other’s minds and emotions (Clausi et al., 2019;Van Overwalle et al.,
2019). Additionally, people with social deficits, such as people with
autism and schizophrenia, consistently showing distorted cerebellar vol-
umes, functioning, or connections (Clausi et al., 2021;Cui et al., 2022;
Siciliano &Clausi, 2020;Van Overwalle et al., 2021).
Cerebellum and sequencing learning
What is the specific function of the cerebellum in non-motor mental
processes? In general, the sequencing detection hypothesis assumes that
the evolutionary older role of the cerebellum in constructing internal
models of motor control, has evolved to include non-motor, purely men-
tal processes in the form of event sequences without the involvement of
*Corresponding authors.
E-mail addresses: qianying.ma@vub.ac.be (Q. Ma), min.pu@vub.be (M. Pu), Frank.VanOverwalle@vub.be (F. Van Overwalle).
This research was funded by SRP57 awarded by the Vrije Universiteit Brussel to Frank Van Overwalle, and a CSC fellowship awarded to Qianying Ma.
https://doi.org/10.1016/j.ijchp.2022.100355
Received 30 July 2022; Accepted 6 November 2022
1697-2600/© 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-
nc-nd/4.0/)
International Journal of Clinical and Health Psychology 23 (2022) 100355
Contents lists available at ScienceDirect
International Journal of Clinical and Health Psychology
journal homepage: www.elsevier.es/ijchp

Fig. 1. Schematic example showing the first 6 trials of the Standard sequence in the Belief SRT task [A] and the Cognitive SRT task [B]. On each trial, participants had
to report the number of flowers as seen by the protagonists (Papa Smurf or Smurfette in the Belief SRT task), or depending on the color variation of the shapes (square
or circle in the Cognitive SRT task). Without being informed, belief orientations/color variations followed a Standard sequence. In the Belief SRT task, when the protag-
onist was oriented to the screen and could see the flowers (true trial), the number of target flowers had to be reported from the current trial; when the protagonist was
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overt movements and somatosensory responses (Leggio &Molinari,
2015). For example, when listening to music, people can enjoy rhythms
or melodies, and anticipate what will likely happen in the upcoming
notes, even they do not play themselves and do not see or imagine some-
one else playing.
Extending this hypothesis to the social domain, researchers propose
that the cerebellum contributes to mentalizing by building internal mod-
els of repeated social sequences, and then automatizes and adjusts these
models according to the current reality during social interactions (Van
Overwalle et al., 2019;Van Overwalle et al., 2021). To illustrate, during
daily social interactions, in some cultures we gradually learn that people
we are close to, typically smile at us, stretch out their arms and hug us
when we greet them. These sequential social actions are registered as an
internal model for how close others greet us, and how we automatically
respond. For example, we may spontaneously open our arms to hug and
greet them back. If, however, they suddenly hesitate when we hug
them, we register that something is different. More importantly, most of
the time these behavioral sequences are implicit, automatic, and requir-
ing little conscious monitoring. We often learn and perform these tempo-
ral patterns of social behavior implicitly and adjust our behaviors to
novel social contexts implicitly.
To measure implicit learning, one well-known paradigm is the classic
serial reaction time (SRT) task (Nissen &Bullemer, 1987). In this para-
digm, participants rapidly respond to sequences of stimuli which are
surreptitiously repeated (e.g., spatial locations on a screen). Faster
responding to this hidden repetition and slower responding to any
disruptions indicates successful implicit learning.
To explore this sequence detection function in a social context, Ma
and colleagues designed a novel belief serial reaction time task (Belief
SRT task,Fig. 1A, Ma et al., 2021) which required participants to learn a
sequence that included information about others’beliefs. Participants
were asked to report what two protagonists believed (e.g., how many
flowers were offered to them). To resolve the task, they had to realize
that protagonists’beliefs might converge with or differ from reality,
resulting in true and false beliefs respectively. As shown in Fig. 1A,
when protagonists were oriented towards the flowers, they could see the
flowers and held a true belief about reality (e.g., Trial 1). Conversely, if
they were oriented away from the flowers, they were not aware of any
changes and therefore held a false belief (e.g., Trial 2). This is a conven-
tional set-up in mentalizing research (Saxe et al., 2006). Crucially, unbe-
knownst to participants, there was a standard sequence of these true-
false beliefs that was repeated over many trials (Fig. 1C). Although
learning the sequence was implicit, as participants did not know the
existence of the sequence beforehand and could not fully reproduce this
sequence after the task, they showed learning by responding faster to
repetitions of this hidden standard sequence during an intensive Train-
ing phase, and slower after any disruption in a Test phase (Ma et al.,
2021). While faster responses in the Training phase reflect a general
learning process, slower responses to disruptions of the specific sequence
in the Test phase demonstrate sequence-specific learning.
A non-social Cognitive SRT task involving executive control processes
was also created (Fig. 1B). Shapes replaced protagonists and colors
replaced true/false beliefs, but otherwise, the structure of the task
remained identical. Specifically, instead of mentalizing, colored shapes
in the Cognitive SRT task required participants to respond to the current
situations (= true belief) or inhibit their responses to the current situa-
tion and report the amount of flowers from previous trails (= false
belief). Nonetheless, implicit Cognitive SRT learning was also demon-
strated, in that participants showed general and specific learning. How-
ever, participants generally responded slower on the Cognitive than the
Belief SRT task (Ma et al., 2021).
One critical aspect of the Belief and Cognitive SRT tasks is that the
hidden standard sequence was unconfounded by participants’motor
responses. Responding was devoid of motor learning, because partic-
ipants’motor responses to indicate the number of flowers were ran-
domly determined. Consequently, the Belief and Cognitive SRT tasks
reflect pure perceptual learning (i.e., perceived sequences of beliefs
based on orientation in the Belief task, and colors in the Cognitive task),
without any aid from motor actions.
As noted earlier, previous behavioral research demonstrated implicit
learning of both Belief and Cognitive sequences (Ma et al., 2021). A fol-
low-up neuroimaging study using the same paradigm demonstrated that
the posterior cerebellar Crus I was preferentially involved in the Belief
SRT task, whereas more anterior cerebellar lobules VI/VIII were
involved in the Cognitive SRT task when participants began to learn the
hidden sequence (Ma et al., 2021). Also, when directly comparing the
Belief and Cognitive tasks, the posterior cerebellar Crus II was consis-
tently and more highly engaged in learning social sequences (Fig. 2,Ma
et al., 2021). Overall, this neuroimaging study showed cerebellar
involvements in Belief and Cognitive SRT tasks, but with differences in
the exact location of cerebellar activation.
Cerebellar transcranial direct current stimulation
Non-invasive transcranial direct-current stimulation (tDCS) has
gained considerable interest in recent years, as it can demonstrate a
causal effect of the targeted brain area and specific functions, and could
also be used as a potential clinical intervention to improve brain plastic-
ity and learning. tDCS uses direct electrical currents to stimulate specific
parts of the brain, by passing a low intensity current (e.g., 1 and 2 mA)
through two electrodes placed over the scalp above the target brain area
to stimulate it. Neural excitability is generally increased by anodal and
decreased by cathodal stimulation (Bikson et al., 2016;Paulus et al.,
2016). tDCS has been applied during the execution of tasks (concurrent
effect), or before the execution of a particular task (post-intervention
effect, van Dun et al., 2016). tDCS has been used with the classic SRT
task to examine the role of the cerebellum in motor sequence learning.
Studies found that, compared to sham, participants responded generally
faster when given anodal cerebellar tDCS during (Liebrand et al., 2020)
or after the stimulation (Ferrucci et al., 2013). More importantly, several
cerebellar tDCS studies provide initial evidence that stimulating the cer-
ebellum modulates performance of various affective and social tasks.
Ferrucci et al. (2012) found that after 20 min of anodal stimulation, par-
ticipants recognized negative emotions faster than before stimulation.
oriented away from the screen and could not see the flowers (false trial), the number of target flowers had to be reported from the previous true trial from the same pro-
tagonist. Similarly, in the Cognitive SRT task, a blue square or a green circle indicated that the number of flowers had to be taken from the current trial (= true trial),
while an orange square or black circle (= false trial) indicated that the number of flowers had to be taken from the previous true trial from the same shape. The number
of flowers was random (1 or 2), making the response unpredictable, and sequence learning dissociated from motor responses. Each trial was self-paced, with all stimuli
remaining on the screen for 3000 ms until a response was given, followed by a response-stimulus interval of 400 ms before the next trial started. [Bottom Inset] The
inset shows an enlargement of the target stimulus, consisting of a pair of one or two flowers surrounded by clovers (as a distraction) of approximately the same shape
and color. [Trial 1 - 2] To illustrate the instructions for the Belief SRT task, in Trial 1, there is one flower that Papa Smurf can see because he is oriented toward the
screen, meaning that the correct response is 1. In Trial 2, there are two flowers. Because Papa Smurf is oriented away from the screen, he cannot see the number of flow-
ers on this trial, hence he still holds the belief to have received one flower which he last saw on the previous (1st) trial. The correct response is thus again 1. In the Cog-
nitive SRT task, Trial 1, there is one flower. Because the color of the square is blue, the correct response is the observed number of flowers, or 1. In Trial 1, the square is
orange, so participants must report the number of flowers from the blue square from the previous (1st) trial. The correct response is thus, again, 1. [C]. The Standard
sequence in the Belief SRT task. M = male (Papa Smurf), Fe = female (Smurfette), T = true, Fa = false. In the Cognitive SRT task, the sequence is identical, and stim-
uli were replaced by shapes and colors, as depicted in [A-B] (i.e., Male True = Blue Square; Male False = Orange Square, Female True = Green Circle, Female
False = Black Circle).
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Importantly, a recent study found that concurrent anodal cerebellar
tDCS improved participants’performance in correctly predicting social
behaviors (e.g., grasping a glass for drinking versus offering to another
person) under high uncertainty situations, whereas it did not improve
performance in predicting non-social moving shapes (e.g., square versus
rectangle; Oldrati et al., 2021). Given these promising results, in the cur-
rent study, we explored the effect of anodal cerebellar tDCS on implicit
social and cognitive sequence learning.
Recall that our previous neuroimaging study showed that the Belief
SRT task activated the posterior cerebellum whereas the Cognitive SRT
task activated the anterior cerebellum, and that most activations were
located in both cerebellar hemispheres (Fig. 2,Ma et al., 2021). There-
fore, we targeted the posteromedial cerebellum as this anatomical posi-
tion is effective in modulating task performance in social and cognitive
domains (Ferrucci et al., 2013,2012). We also examined tDCS effects
after different time intervals. Specifically, participants engaged in the
Belief or Cognitive SRT tasks concurrently with stimulation, 30 min after
stimulation and then again 1 week after the stimulation.
Overall, we hypothesized that the cerebellum is causally involved in
sequence learning of social mentalizing and cognitive control. This
should be revealed by replicating general learning and sequence-specific
learning as in previous implicit Belief and Cognitive SRT research (Ma et
al., 2021;Ma et al., 2021), and more importantly, by demonstrating
that participants respond faster when they are given anodal cerebellar
tDCS, as found in previous anodal cerebellar tDCS studies (Ferrucci et
al., 2013,2012;Liebrand et al., 2020;Oldrati et al., 2021). We also
hypothesized that the closer proximity of tDCS to the posterior cerebel-
lum would affect plasticity in this area more compared to the anterior
cerebellum, and hence will favor Belief compared to Cognitive SRT
learning. Moreover, we also speculated that improvements in sequence
learning could be maintained over a longer period (e.g., week) after
stimulation, similar to previous tDCS studies (Ferrucci et al., 2013;
Firouzi et al., 2021;Savic &Meier, 2016).
Method
Participants
A total of 138 healthy, Dutch-speaking participants were recruited.
Participants were randomly assigned to receive either anodal or sham
stimulation with the Belief or Cognitive SRT tasks to avoid any carry-
over effects of sequence learning (Geiger et al., 2018) or ineffective
blinding of brain stimulation (Jongkees et al., 2019). After the experi-
ment, the data from thirty-two participants were excluded. Fifteen par-
ticipants dropped out before the long-term post-intervention sessions.
Thirteen participants had very high error rates (i.e., errors above the 3rd
quartile plus 1.5 times the interquartile range, i.e., 1st vs. 3rd quartile
difference). Four participants showed high awareness of sequence
knowledge in the post-hoc sequence recollection test (i.e., correct recol-
lection was above the half-length of the sequence; among them, three
participants received the anodal Belief SRT task, and one participant
received the sham Belief SRT task).
Hence, valid data were acquired from the remaining 106 participants
(25 males, age 18-35 years, mean age 20.3±3.3). A statistical prior
power analyses using G*Power3 (Faul et al., 2007) revealed that 92 par-
ticipants are required in order to obtain a power of 90%,α= 0.5 with a
rather small size effect (f = 0.15). For the remaining participants, 25
participants received the anodal Belief SRT task (7 males, mean age
20.3±3), 27 participants received the sham Belief SRT task (7 males,
mean age 20.7±4.2), 28 participants received the anodal Cognitive SRT
task (6 males, mean age 19.8±2.3), and 26 participants received the
sham Cognitive SRT task (5 males, mean age 20.5±3.5). All participants
gave written informed consent with the approval of the UZ Brussel
Ethics Committee.
Stimuli material
The stimulus materials are identical to those used in the previous
behavioral and fMRI study on implicit sequence learning (Ma et al.,
2021;Ma et al., 2021a).
Implicit belief SRT task
In the implicit Belief SRT task (Fig. 1A), the target was 1 or 2 flowers,
appearing in one of four horizontal locations, marked by four little
smurfs (who offered the flowers), on the top of the screen. The target
flower(s) was presented along with clovers as distractors, which occu-
pied other remaining locations, resulting in two plants on each location.
The two protagonists, Papa Smurf and Smurfette, were each shown indi-
vidually at the bottom of the screen with their faces orientated either
toward or away from the screen.
Participants were instructed: “One of the four little smurfs will give
the flowers while Papa Smurf or Smurfette is watching (facing the
screen) or not watching (facing you). Papa Smurf and Smurfette count
the flowers they receive. Throughout the task, you have to track how
many flowers (1 or 2) Papa Smurf or Smurfette thinks he or she will get.
If they are turned with their back to the 4 smurfs, you have to indicate
how many flowers they (remember that they) received last time.”
Implicit cognitive SRT task
The following changes were made in the non-social implicit Cogni-
tive SRT task (Fig. 1B). The target flower(s) was marked by four side-
walk boards instead of little smurfs. The orientations of Papa Smurf
(true and false) and Smurfette (true and false) were replaced by squares
(blue and orange) and circles (green and black) respectively. Thus, the
four distinct pictures used in the implicit Belief SRT task were replaced
by four distinct pictures of colored shapes in the implicit Cognitive SRT
task.
Participants were instructed: “At the bottom of the screen is a square
or circle. Throughout the task, you have to follow how many flowers (1
or 2) there are at the blue square or the green circle. If the square is
Fig. 2. Sagittal views of cerebellar activations when participants learned the standard sequences. A prior neuroimaging study (Ma et al., 2021) showed that the Belief
SRT task recruited more posterior cerebellar parts whereas the Cognitive SRT task recruited more anterior cerebellar parts. Moreover, higher posterior cerebellar Crus
II activation was shown in the Belief than the Cognitive SRT task.
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orange, you must report the previous number of flowers from the blue
square. If the circle is black, you have to report the previous number of
flowers from the green circle.
In both Belief and Cognitive SRT tasks, each trial started after a
response-stimulus interval of 400 ms. Responses were self-paced, and all
stimuli remained on screen for 3000 ms until a response was given.
When participants made a wrong response or when they did not respond
within 3000 ms, the word “Error”appeared (“Fout”in Dutch) for
750 ms on the screen. After each block, participants received feedback
about their mean reaction time and error rate, and they were encour-
aged to make less than 5%errors. Participants got a break of 15 s after
every two blocks. Meanwhile, the number of flowers was randomly
determined at every trial, leading to random motor responses, and a dis-
sociation between the sequence of belief orientations/color variations
and motor responses, while the sequence embedded in the two tasks and
the instructions were structurally the same (Fig. 1C).
Cerebellar transcranial direct-current stimulation
This brain stimulation protocol was replicated from a previous cere-
bellar tDCS study (Ferrucci et al., 2013), except for the use of the tDCS
equipment. A Soterix Medical 1 ×1 tDCS Low-Intensity Stimulator was
used to deliver cerebellar tDCS via a pair of rectangular saline-soaked
synthetic sponge (5 ×7 cm) with round rubber (50 mm diameter) elec-
trodes inside the sponge: the active electrode was positioned horizon-
tally on the median line 2 cm below the cerebellar inion and the
reference electrode was positioned vertically over the right upper arm
(Ferrucci et al., 2013) . The stimulating current was an anodal direct cur-
rent at 2 mA intensity and was delivered for 20 min over the posterome-
dial cerebellum. As noted earlier, we stimulated the posteromedial
cerebellum as in Ferrucci et al. (2013) to target the bilateral cerebellum,
where we found activation in our previous fMRI study (Ma et al., 2021,
Fig. 2).
For the anodal stimulation, the current intensity was slowly ramped
up from 0 to 2 mA in 30 s, then this current intensity was maintained for
20 min, and then ramped down from 2 mA to 0 in 30 s. For the sham
stimulation, the current intensity was slowly ramped up from 0 to 2 mA
and then slowly ramped down to 0 mA in one minute. After 20 min, this
slow ramping-up and ramping-down of the current intensity was applied
again.
We used a self-report questionnaire to assess side effects due to tDCS
intervention (Brunoni et al., 2011), which included 10 most common
complaints (headache, neck pain, scalp pain, tingling, itching, burning
sensation, skin redness, sleepiness, trouble concentration, acute mood
change). Responses on each item require participants to indicate “Do
you experience any of the following symptoms or side-effects?”on a 4-
point scale (1 = absent, 2 = mild, 3 = moderate, 4 = severe).
Experiment procedure
The participants were blind to the type of tDCS delivered (sham ver-
sus anodal), however, blinding the experimental researcher was impossi-
ble with the current device. So, we used a single blind design. Note that
since the reference electrode was placed over participants’right upper
arm, they were required to complete the task using their non-dominant
left-hand.
Concurrent session
In the concurrent session, participants first completed a number of
practice trials to familiarize themselves with the task. This consisted of
two blocks with 24 trials each and these practice data were excluded
from further analysis. Next, they completed a number of baseline trials.
This also consisted of two blocks with 24 trials each. These trials were
recorded to measure participants’baseline performance before the main
task. The sequence of the trials in these practice and baseline blocks was
different than in the main experiment.
After the practice and baseline trials, participants started the Belief
or Cognitive SRT task (Fig. 3B), which consisted of 11 blocks with 48 tri-
als each. The full version was divided into a Training and a Test phase.
In the initial Training phase, the Standard sequence was repeated
throughout 5 blocks (Standard blocks 15). This Standard sequence con-
sisted of 16-trials of varying Protagonists (smurfs / shapes) and Orienta-
tions (beliefs / colors; Fig. 1C) and was repeated three times per block.
In the Test phase, Blocks 6, 8, 9, and 11 were identical to the Standard
blocks in the Training phase. In Blocks 7 and 10, the Standard blocks
were inconspicuously replaced by Random blocks. There were two types
of Random blocks. In a Total Random block, Protagonist (smurfs /
shapes) and Orientation (beliefs / colors) were totally randomized with
the limitation of at most 2 subsequent trials of the same Orientation,
consistent with the Standard blocks. In a Random Orientation block,
only Orientation (beliefs / colors) was changed into a different pseudo-
random sequence while Protagonists (smurfs / shapes) remained identi-
cal as in the Standard blocks (See Supplementary Table S1). The order
Fig. 3. [A] Schematic representation of the experimental design. [B] The SRT task with block numbers 1-11. S = Standard block (with standard sequence).
R = Random block. Note that every block has a length of 48 trials. One random sequence was created for Protagonist and Orientation together (i.e., Total Random),
and one pseudo-random sequence was created to test the learning of Orientation (i.e., Random Orientation). Participants would perform one Total Random and one
Random Orientation block in the Test phases, and the order of two Random blocks was counterbalanced among participants.
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of the Total and Orientation Random blocks in Blocks 7 and 10 was
counterbalanced between participants.
While performing the SRT task, participants received either anodal
or sham stimulation for 20 min, regardless of the duration participants
spent on completing the SRT task (Fig. 3A). In the current study, partici-
pants received stimulation after completing the task for an average of
4.27 min.
Short-term post-intervention
After tDCS, participants took a break for 30 min (Fig. 3A). During
this break, participants were asked to finish a questionnaire that
assessed potential adverse effects associated with brain stimulation
(Brunoni et al., 2011). After completing the questionnaire, participants
were allowed to do anything they wanted as long as they would rest and
not engage in anything cognitively or socially taxing. They could stay in
the lab or have a short walk outside. Coming back from the 30 min
break, participants completed a short version of the Belief/Cognitive
SRT task which only consisted of Test phase, identical to the one in the
full version (Fig. 3B).
Long-term post-intervention
The long-term post-intervention took place one week later (Fig. 3A).
In this session, participants first completed two practice blocks to help
them to re-familiarize with the task. Then, the same, full version of the
Belief/Cognitive SRT task was performed again without the application
of tDCS (Fig. 3B). After the task, an online post questionnaire was com-
pleted to test whether participants became aware of the sequence. Par-
ticipants were first asked “whether you notice something particular or
not”. Next, they were asked to reproduce, as accurately as possible, the
"order in which Papa Smurf and Smurfette appeared" and whether or
not they "could or could not see who gave the flowers”in the Belief SRT
task; and the “order in which squares and circles appeared”for each
combination of shape and color in the Cognitive SRT task. Sequence
retrieval accuracy was scored by taking the maximum length of a correct
sequence, starting at every possible point in the sequence.
Statistical analysis for sequence learning and stimulation effects
Participants’number of errors and mean reaction times (RTs) per
block were analyzed. For the RTs analysis, responses during and imme-
diately after an error were excluded. In addition, each participant was
given a sequence awareness score ranging from zero to sixteen, based on
their longest correct sequence recollected in the post-check of the final,
long-term post-intervention session. This recollection score was used as
a covariate in the following mixed ANOVA analyses on learning effects
to minimize the effect of any potential sequence knowledge.
First, a general learning effect during the Training phase was tested by
a mixed ANOVA with Standard Blocks (Block 1 to 5) and Sessions (Con-
current and 1-week post) as within-participants factors, and Task (Belief
versus Cognitive) and Stimulation (Anodal versus Sham) as between-
participants factor. This will not be applicable to the short-term post-
intervention as it only included the Test phases.
Second, sequence-specific learning effects were tested by a mixed
ANOVA with Block Type (Random versus Standard Blocks [the average
of Standard Blocks adjacent to each Random block) and Session (Con-
current, 30-mins post and 1-week post) as within-participants factors,
and Task (Belief and Cognitive) and Stimulation (Anodal versus Sham)
as between-participants factors. Given that we have two types of
Random blocks (i.e., Total Random and Random Orientation block; i.e.,
Block 7 or 10) and different Standard Blocks adjacent to them (i.e.,
Blocks 68or911 respectively), these two types were tested sepa-
rately.
Significant main and interaction effects were further tested by a post
hoc Bonferroni test. The significance level was set to 0.05, and two-tailed
tests were applied. When the sphericity assumption was violated for
ANOVA, the Greenhouse-Geisser correction is reported.
A Bayes factor analysis was also used to evaluate the effects found in
the experiment (JASP0.16.0.0). Here we relied on Bayes factors (BF
10
)
for interpreting our main results. BF
10
indicates the strength of the evi-
dence in favor of alternative hypothesis (H
1
) over the null hypothesis
(H
0
). For example, if BF
10
= x, this means that the data are x times more
likely under the alternative hypothesis (e.g., there is a learning effects)
than under the null hypothesis (e.g., there is no learning effect). Results
with 1 <BF
10
<3 indicate anecdotal evidence to support the alternative
hypothesis, BF
10
>3 indicate substantial evidence to support the alter-
native hypothesis, and BF
10
>10 indicate strong evidence to support the
alternative hypothesis (van Doorn et al., 2021).
Results
Manipulation check: limit awareness of sequence and stimulation
Awareness of the (repetition of the) Standard sequence was assessed
at the end of the long-term post-intervention. Participants were first
asked to answer a sequence awareness question (“whether you notice
something particular or not”), and then had to reproduce the whole
sequence for the Protagonists (Smurfs / Shapes) and their Orientation
(Beliefs / Colors).
Most participants (57%) reported no sequence awareness, and the
number of these participants was comparable across Task and Stimula-
tion by chi-square tests (all ps >0.3). To exhaustively test awareness,
each participant was given a sequence awareness score ranging from
zero to sixteen, based on their longest correct sequence recollection. The
mean recollection score was 2.87 out of 16, indicating a limited
sequence awareness in the current study. A two-way ANOVA showed
that the recollection score did not differ between Task (Belief versus
Cognitive) and Stimulation (Anodal versus Sham, all ps >0.1, M ±SD:
Anodal Belief: 3.24 ±1.3; Sham Belief: 2.74 ±1.6; Anodal Cognitive:
2.89 ±1.5; Sham Cognitive: 2.62 ±1.7 see details in Supplementary
Table S2). To minimize the effect of any potential sequence knowledge,
this recollection score was used as a covariate in the mixed ANOVA anal-
yses on learning performance (see below), and results showed this
covariate was insignificant in these analyses.
Potential side effects due to tDCS were assessed through a self-report
questionnaire (Brunoni et al., 2011). Consistent with previous studies,
none of the participants reported major side effects and the most com-
mon complaints were itching and skin redness under the electrodes
(Brunoni et al., 2011;Jongkees et al., 2019). A two-way ANOVA showed
that there were no differences between Task and Stimulation (ps >0.1),
except that participants reported more itching and skin redness under
anodal stimulation than sham (see details in Supplementary Table S3).
Because almost all participants reported that this was the first time they
participated in a tDCS study (only four participants had tDCS experience
before), and because we used a between-participant design, it is unlikely
that participants were aware of the distinct Stimulation types only based
on these subjective experiences of the tDCS side effects. Therefore, the
experienced side effects most likely did not modulate or confound the
learning effects reported below.
Baseline measurement showed no task differences before stimulation
A 2 Stimulation (Anodal versus Sham) ×2 Tasks (Belief versus Cog-
nitive) ANOVA was conducted to examine baseline performance before
the main SRT task at the concurrent session (2 baseline blocks). Results
revealed no significant difference in RTs or errors at baseline (all ps >
0.2, see details in Supplementary Table S4).
Anodal stimulation accelerated RT performance on the cognitive but not the
Belief SRT tasks
To examine the influence of anodal tDCS, mixed ANOVAs were
applied on RTs and errors. Here, we first report the results for RTs. As
6
Q. Ma et al. International Journal of Clinical and Health Psychology 23 (2022) 100355

noted earlier, these analyses were conducted with awareness (i.e., recol-
lection score) as a covariate, but the results showed no substantial effect
of this covariate and were, therefore, omitted (but are accessible, see
Data Availability).
Response times and general learning
A mixed ANOVA was used with five Standard Blocks in the Training
phase (Blocks 15) and Session (Concurrent and 1-week post) as within-
participants factors and Task (Belief versus Cognitive) and Stimulation
(Anodal versus Sham) as between-participants factors. As shown in
Fig. 4A, participants responded faster across Standard Blocks during
Training, revealed by a significant main effect of Blocks 1-5 (F
(3.57,
360.85)
= 10.67, p<0.001, η
2
= 0.10, BF
10
= 80577). Compared to the
concurrent session, participants also responded faster in the 1-week post
intervention, shown by a significant main effect of Sessions (F
(1,
101)
= 122.16, p<0.001, η
2
= 0.55, BF
10
= 2.16E+242, MD
1-week post-
Concurrent
= -270 ms).
There was a main effect of Task (F
(1, 101)
= 5.08, p= 0.03,
η
2
= 0.05, BF
10
=1, MD
Belief-Cognitive
= -52 ms), of Stimulation (F
(1,
101)
= 6.60, p= 0.01, η
2
= 0.06, BF
10
=3,MD
Anodal-Sham
= -59 ms),
and an interaction between Tasks and Stimulation (F
(1, 101)
= 5.09,
p= 0.03, η
2
= 0.03, BF
10
= 3). Post-hoc comparisons with Bonferroni
correction showed faster responses in the Anodal than Sham condition
of the Cognitive SRT task (MD
Anodal-Sham
= -111 ms, t= -3.45,
p= 0.005), but not in the Belief SRT tasks (MD
Anodal-Sham
= -8 ms).
There was no interaction between Stimulation and Blocks (1-5),
indicating that anodal stimulation did not increase further the amount
of general learning. There were no other significant effects.
Response times and sequence-specific learning
Two mixed ANOVAs with Block Type (Total Random/Random Ori-
entation versus the average of adjacent Standard Blocks) and Session
(Concurrent, 30-mins post and 1-week post) as within-participants fac-
tors and Task (Belief and Cognitive) and Stimulation (Anodal versus
Sham) as between-participants factors was applied on RTs (Fig. 4A).
The analysis on Total Random Blocks showed a main effect of Block
Type which revealed sequence-specific learning by slower responses in
the Total Random versus Standard blocks (F
(1, 101)
= 22.47, p<0.001,
η
2
= 0.18; BF
10
= 8.25E+09), and a main effect of Session (F
(1.74,
176.07)
= 36.76, p<0.001, η
2
= 0.27; BF
10
= 3.414E+66). Post-hoc
Bonferroni tests showed faster RTs across sessions (MD
30-mins post-Concur-
rent
= -96 ms, t= -11.48, p<0.001; MD
1-week post-Concurrent
= -145 ms,
t= -17.38, p<0.001; MD
1-week post-30-mins post
= -49 ms, t= -5.92, p<
0.001). Participants responded significantly faster under Anodal than
Sham Stimulations (F
(1, 101)
= 6.63, p= 0.01, η
2
= 0.06, BF
10
=3)
and their response times trended towards being slightly faster in the
Belief than the Cognitive Tasks (F
(1, 101)
= 2.8, p= 0.10, η
2
= 0.03,
BF
10
= 0.5). There was an interaction between Task and Stimulation
(F
(1, 101)
= 4.06, p= 0.05, η
2
= 0.04, BF
10
= 2), and post-hoc compar-
isons showed that the effect of stimulation on RT was significant for the
Cognitive SRT task (MD
Anodal-Sham
= -82 ms, t= -3.29, p= 0.008) but
not for the Belief SRT task (MD
Anodal-Sham
= -10 ms).
Fig. 4. Mean RTs [A] and errors [B] after anodal and sham stimulation for the Belief and Cognitive SRT tasks. “*”indicated significant RT differences in general learning
and sequence-specificlearning; “+”indicated significant RT differences between anodal and sham stimulation (uncorrected for multiple comparisons). S = Standard
Block, TR = Total Random block, RO = Random Orientation block. Error bars = Standard errors of the mean. Random blocks of the same type and their adjacent
sequence blocks were collapsed in the analysis.
7
Q. Ma et al. International Journal of Clinical and Health Psychology 23 (2022) 100355

The analysis on Random Orientation Blocks revealed similar results.
There was a main effect of Block Type which revealed sequence-specific
learning by slower responses in the Random Orientation versus Standard
blocks (F
(1, 101)
= 13.90, p<0.001, η
2
= 0.12; BF
10
= 151516), and a
main effect of Session (F
(1.85, 186.61)
= 19.48, p<0.001, η
2
= 0.16;
BF
10
= 1.15E+78). Post-hoc Bonferroni tests showed faster RTs across
sessions (MD
30-mins post-Concurrent
= -113 ms, t= -13.21, p<0.001; MD
1-week post-Concurrent
= -163 ms, t= -18.96, p<0.001; MD
1-week post-30-
mins post
= -49 ms, t= -5.76, p<0.001). Participants responded signifi-
cantly faster under Anodal than Sham Stimulations (F
(1, 101)
= 7.15,
p= 0.009, η
2
= 0.07, BF
10
= 3) and their response times were faster in
the Belief than the Cognitive Tasks (F
(1, 101)
= 4.5, p= 0.04,
η
2
= 0.04, BF
10
= 1).There was an interaction between Task and Stimu-
lation (F
(1, 101)
= 7.28, p= 0.008, η
2
= 0.07, BF
10
= 4) and post-hoc
comparisons showed that a stimulation effect on RT for the Cognitive
SRT task (MD
Anodal-Sham
= -97 ms, t= -3.84, p= 0.001) but not for the
Belief SRT task (MD
Anodal-Sham
= -0.1 ms).
Crucially, there were no interactions between Stimulation and Block
Type for the two types of Random blocks, indicating the anodal stimula-
tion did not improve the amount of sequence-specificeffect in Total Ran-
dom or Random Orientation blocks.
The results of the mixed ANOVAs demonstrate that participants
responded faster under anodal compared to sham stimulation during the
Cognitive SRT task. To further test the effect of anodal stimulation, inde-
pendent t-tests were applied for each block. The results showed that RT
improvements during the Cognitive SRT task appeared for most of the
standard blocks in the concurrent stimulation session, and for all types of
blocks in the 30-mins post and 1-week post sessions (Fig. 4A). Looking at
theCognitiveSRTtaskmoreclosely,wealsocomputedadifferencescore
betweenStandardBlocks1and5intheTrainingphasestoreflect the
amount of general learning, and a difference score between Random and
adjacent Standard Blocks to reflect the amount of sequence-specificlearn-
ing. Larger RT difference scores reflectstrongerlearningeffectsduringthe
Cognitive SRT task. As shown in Fig. 5, although these effects were non-
significant, anodal stimulation seems to show stronger sequence learning.
Number of Errors
We also ran the same sets of mixed ANOVAs for the number of errors.
The results showed that participants made more errors in the Cognitive
than the Belief SRT tasks during the Training phase in the concurrent
session (MD
Cognitive-Belief
= 0.6, Fig. 4B). Also, they made more errors in
Random Orientation blocks compared to adjacent Standard blocks (MD
Random Orientation-Standard
= 0.9, Fig. 4B). No other learning effects were
found. As these results did not appreciably change the interpretation of
the learning and stimulation effects based on RTs discussed above, they
are reported in the Supplementary Materials.
Summary
Overall, as we hypothesized, we found general learning and sequence-
specificlearning effects for both Belief and Cognitive SRT tasks across
sessions. Contrary to our hypothesis, the anodal stimulation accelerated
participants’responses only in the Cognitive SRT task, and this improve-
ment was significant during the concurrent and two post-intervention
sessions. However, the anodal stimulation did not significantly improve
the amount of sequence learning. There was no effect of anodal stimula-
tion on the Belief SRT task.
Discussion
The current study investigated the causal impact of the cerebellum
on implicit belief and cognitive sequence learning. To investigate this,
we stimulated the posteromedial cerebellum using anodal tDCS to see
its influence on performance on a social Belief and a non-social Cogni-
tive SRT task. Recall that by stimulating the posteromedial cerebellum,
we expected to obtain tDCS effects on both Belief and Cognitive SRT
learning. We also hypothesized to find somewhat stronger effects on
Belief SRT learning because stimulation is closer to the posterior cerebel-
lum involved in social mentalizing as opposed to the anterior cerebellum
involved in cognitive sequence learning. Our results only partly confirm
our hypothesis. We found that anodal cerebellar stimulation accelerates
response time in the non-social Cognitive SRT task, although accelera-
tion was general and not specific for the standard sequence. Importantly,
we found that this RT acceleration was still present 30 min and one week
after stimulation. However, contrary to our hypothesis, anodal tDCS did
not accelerate responding in the social Belief SRT task. Overall, our
results suggest that anodal tDCS on the posteromedial cerebellum has a
positive effect on general performance in the Cognitive SRT task, but
not in the social Belief SRT task.
Anodal cerebellar tDCS does not accelerate performance in the Belief SRT
task
The current results demonstrated general learning and sequence-spe-
cific learning of the standard sequences as reflected in faster RTs to the
standard sequence and slower RTs to random sequences, which repli-
cates results of previous Belief and Cognitive SRT studies (Ma et al.,
2021). However, the lack of improvement after anodal stimulation on
RTs in the Belief SRT task, while the RTs improved on the Cognitive SRT
task, is unexpected.
One potential factor for this differential tDCS effect on type of task is
familiarity and effort expenditure. When a task is familiar and does not
require much mental effort, it might profit less from any attempts for fur-
ther improvement, such as non-invasive neurostimulation (Bongaerts et
al., 2020;Pope &Miall, 2012). To illustrate, Pope &Miall (2012) found
that a complex cognitive task showed improved performance after tDCS,
while an simple version of the same task did not. Similarly, a recent
study showed that anodal cerebellar tDCS improved accuracy to recog-
nize a sequence of three letters, but did not for a much simpler sequence
of one letter (Maldonado &Bernard, 2021). Likewise, Ferrucci et al.
(2012) found that participants recognized negative emotions faster after
receiving anodal cerebellar tDCS compared to before stimulation
Fig. 5. Cognitive SRT task: Mean RTs differences between Standard Block 1 to 5
denoted as S(B1-B5), mean RT differences between Total Random and its adjacent
Standard Blocks (TR), and mean RT differences between Random Orientation and
its adjacent Standard Blocks (RO). Statistical analyses showed that the improve-
ment given anodal stimulation at the concurrent and 30-mins and 1-week post-
intervention sessions was larger than the sham but nonsignificant. Error
bars = Standard errors of the mean.
8
Q. Ma et al. International Journal of Clinical and Health Psychology 23 (2022) 100355

(baseline), but showed no improvement in recognizing positive and neu-
tral emotions. In their study, participants took longer to identify nega-
tive emotions at baseline, which may reflect a general difficulty in
discriminating negative emotions from the start, which left enough
room for further improvement as a result of stimulation.
How might familiarity and mental effort have impacted on our
results? Evidently, our Belief SRT task involves a very familiar process of
belief understanding, whereas the Cognitive SRT tasks rests on artificial
and unfamiliar rules. This has been attested by previous experiments
(Ma et al., 2021), which showed generally faster responses on Belief
than Cognitive SRT tasks. Likewise, in the current study, there was no
RT difference between Belief and Cognitive SRT tasks during the base-
line, but participants adjusted quicker to the Belief SRT task. This sug-
gests that healthy participants are quite familiar with social schemata
and have extensive real-life practice with mentalizing (in the Beliefs
SRT task), whereas thinking of colorful objects (in the Cognitive SRT
task) is purely artificial and peculiar and rarely practiced outside the lab-
oratory (Callejas et al., 2011;Cohen &German, 2010;Ma et al., 2021).
Familiarity suggests that a process could be automatized, so that more
attention can be be allocated and maintained to the repeated sequence
during the whole SRT experiment. This gives less room for further RT
improvement on the Belief than the Cogntive SRT task.
An alternative, but related explanation is that perhaps some neuro-
logical effects were produced by tDCS stimulation, but were not trans-
formed into observable behavioral effects. This may be especially the
case when the task is familiar and easy. To illustrate, in a concurrent
tDCS and fMRI study, D’Mello et al. (2017) found that anodal tDCS suc-
cessfully increased activation in the right posterior cerebellar Crus I &II
when reading sentences, although no behavioral effects were observed
(i.e., faster responses in sentence completion).
Together, our and previous findings suggest that cerebellar stimula-
tion may not facilitate familiar processes such as inferring other’s minds
under very structured lab conditions. However, under more taxing
everyday social circumstances or for clinical populations that struggle
with social mentalizing, stimulating the cerebellum may provide further
benefits. (Van Overwalle et al., 2021). Recent clinical studies have dem-
onstrated that patients with autism or with cerebellar damage show
social deficits in understanding others’minds or emotions (Clausi et al.,
2021;Van Overwalle et al., 2019), and are therefore prime candidates
for such an approach.
Anodal cerebellar tDCS improves general performance on the cognitive SRT
task
As hypothesized, participants generally responded faster during
anodal cerebellar tDCS in the Cognitive SRT task, and even after stimula-
tion up to one week later. Fig. 4A indeed shows that RT performance on
the Cognitive task was closer to performance on the Belief SRT task with
anodal stimulation, which supports our reasoning that improvement
was more likely to be observed in the unfamiliar Cognitive task.
There are two potential explanations for the prolonged stimulation
effect. It may indicate that concurrent tDCS (i.e., during learning) can
improve general performance, and this improvement could still be pres-
ent and observed one week later. Alternatively, it may indicate that cere-
bellar excitability may have lasted one week after stimulation, and that
this extended excitability caused improved performance one week later.
To test these potential explanations, future studies can ask participants
to perform new sequencing tasks after the intervention, and any
improved performance of the novel tasks may support the idea of
extended excitability. Nevertheless, the current results suggest that cere-
bellar tDCS benefits could extend over a longer period (at least up to one
week), which is critical for potential clinical applications. This informa-
tion is also useful for determining a sufficient wash-out period for future
neuro-stimulation studies.
Previous work showed that participants responded faster on motor
sequence learning tasks when they received anodal cerebellar tDCS
(Jongkees et al., 2019;Liebrand et al., 2020;Shimizu et al., 2017). The
current Cognitive SRT task extended this RT benefit to perceptual learn-
ing which was unconfound by motor responses (i.e., the sequence of
shapes’colors was not related to the answers on the number of the flow-
ers). This is consistent with previous studies demonstrating that percep-
tual sequence knowledge could be obtained without help from
sequential motor responses (Coomans et al., 2012;Deroost &Coomans,
2018;Deroost et al., 2009;Firouzi et al., 2021). More importantly, our
result supports the idea that the cerebellum is causally involved in non-
motor cognitive processes, and can be modulated by brain stimulation.
Limitation and theoretical implications
Based on the current findings, there are several limitations and theo-
retical implications which are important for future research. When we
delivered anodal tDCS on healthy participants, we found RT improve-
ment on the Cognitive, but not on the Belief SRT task. As noted earlier,
one potential factor influencing tDCS effects is the type of task, because
it is less likely that participants benefit from stimulation when a task
itself is already automatized and does not require much mental effort
(Bongaerts et al., 2020;Pope &Miall, 2012). However, the current study
used a between-participant design, which makes it difficult to directly
compare how individual levels of effort may have impacted performance
on the Belief and Cognitive tasks. For future neurostimulation studies,
researchers should consider a within-participant design, which controls
for individual differences. Also, as noted before, the current study sug-
gests that brain stimulation may not lead to detectable changes in RTs or
accuracy due to daily exposure, experience and automatization of social
mentalizing (Oldrati et al., 2021;Pope &Miall, 2012). Therefore,
researchers should be cautious about drawing conclusions that brain
stimulation does not modulate performance, especially in studies using
healthy participants.
The current study used protocols focusing on the posteromedial cere-
bellum (Ferrucci et al., 2013,2012). It is possible that the current tDCS
protocol stimulated an extensive part of the bilateral cerebellum from
posterior to anterior parts. Previous studies used varying protocols (e.g.,
varying in intensity, montage sizes and locations) on different social
tasks and found contradictory effects (Bongaerts et al., 2022;D’Mello et
al., 2017;Ferrucci et al., 2012;Oldrati et al., 2021). Meta-analytic stud-
ies are needed to find a limited set of effective cerebellar tDCS protocols
for a diversity of social tasks that are meaningful for theoretical insight
on social cognition or practical use in everyday life. One potential ave-
nue for future research is more focal stimulation by using high definition
tDCS with multichannel optimized montages to selectively stimulate
only the posterior cerebellum.
The current study showed that anodal tDCS facilitated response
times. These findings are congruent with the known role of the cerebel-
lum in learning and automatizing skills by building internal models of
sequential information (Leggio &Molinari, 2015;Van Overwalle et al.,
2019). However, the cerebellum does not work alone to compute this
information. One dynamic causal modellng study showed that the poste-
rior cerebellum is connected with key cerebral mentalizing areas, such
as the bilateral temporoparietal junction via closed loops, during implic-
itly belief sequence learning (Ma et al., 2022). This suggests that the cer-
ebellum receives social input signals from the cerebral cortex and
returns feedback signals to the same brain areas. Consequently, anodal
cerebellar tDCS may not only modulate the cerebellum, but also broader
brain networks that the cerebellum interconnects to, resulting in stron-
ger output signals being sent to the cerebral cortex (Pope &Miall, 2012;
Rice, et al., 2021). By combining tDCS and fMRI or event-related poten-
tial (ERP)techniques, future studies could further investigate potential
changes in neural signals within the cerebellum, corresponding cerebral
mentalizing regions, as well as cerebello-cerebral connectivity.
During implicit sequence learning, it is difficult to find a single point
to indicate participants’shift from being unaware to being aware of the
sequence (Ferrucci et al., 2013). As awareness of sequence knowledge is
9
Q. Ma et al. International Journal of Clinical and Health Psychology 23 (2022) 100355

a continuum, we controlled its influence by using the length of sequence
recollection as a covariate in the ANOVAs. Interestingly, in a recent
study, healthy participants who were informed of the existence of a stan-
dard sequence, showed better performance in recalling the sequence
after anodal cerebellar tDCS than sham (Liebrand et al., 2020). This is
also the case in the current study. Even when participants did not dem-
onstrate changes in belief sequence learning during and after receiving
anodal stimulation, they did slightly better (at least numerically) in the
post-check when they were asked to recall the standard belief sequence.
One potential explanation is that participants who received anodal stim-
ulation can recall the sequence better, because anodal stimulation may
have freed up more resources to pay attention to, store and retrieve this
knowledge more solidly. As this better performance on recall was not
statistically significant in the current study, these results should be inter-
preted with caution. To further address this, future neurostimulation
studies could use explicit instructions that inform participants about the
social sequence, to investigate whether tDCS stimulation under these cir-
cumstances can improve any learning performance, including mean
length of the correctly recalled SRT sequence.
Conclusion
The current study showed that anodal tDCS on the cerebellum
improved general performance on Cognitive sequence learning of
healthy young adults, while Belief sequence learning which was struc-
turally identical, remained unaffected. This supports the idea that the
cerebellum is involved in learning sequences, although in a different
manner than we had hypothesized. One potential explanation for the
lack of improvement on our social Belief SRT task is that social mentaliz-
ing is a very familiar and automatized process for healthy participants,
so that more attention and mental effort is allocated to the novel
sequence during the task, and hence leaves little room for further
improvement. Future studies are needed to find a set of suitable proto-
cols for targeting the posterior cerebellum involved in social processes,
including the use of clinical populations with limited social capacities,
which may potentially further increase implicit social sequence perfor-
mance. Studies should also investigate whether or how cerebellar stimu-
lation modulates corresponding cerebral areas or cerebello-cerebral
connectivity.
Data availability
All requested (pseudonymized or anonymous) data are available
upon request, excluding data that allow identifying individual partici-
pants. Belief and Cognitive SRT tasks are also available upon request.
Results of all analyses, including auxiliary analyses not reported in detail
in this article, can be accessed via the Open Science Framework: https://
osf.io/mn2yq/?view_only=8b05cfdc6d6a4730a02a47b74754d5a2.
Supplementary materials
Supplementary material associated with this article can be found, in
the online version, at doi:10.1016/j.ijchp.2022.100355.
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