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Noninvasive stimulation of the ventromedial prefrontal cortex modulates rationality of
human decision-making
Thomas Kroker, MSc1,2, Miroslaw Wyczesany, PhD3, Maimu Alissa Rehbein, PhD1,2, Kati
Roesmann, PhD1,2,4, Ida Wessing, PhD1,2,5 & Markus Junghöfer, PhD1,2
1 Institute for Biomagnetism and Biosignalanalysis, University of Muenster, Muenster, Germany
2 Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Muenster,
Muenster, Germany
3 Institute of Psychology, Jagiellonian University, Krakow, Poland
4 Institute for Clinical Psychology and Psychotherapy, University of Siegen, Siegen, Germany
5 Department of Child and Adolescent Psychiatry, University Hospital Muenster, Muenster,
Germany
Corresponding author:
Markus Junghöfer
Institute for Biomagnetism and Biosignalanalysis
University of Münster
Malmedyweg 15
48149 Münster, Germany
Email: markus.junghoefer@uni-muenster.de
Funding: Supported by the DFG (project JU 445/9-1) and the National Science Center (UMO-2018/31/G/HS6/02490)
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Summary
The framing-effect is a bias that affects decision-making depending on whether the
available options are presented with positive or negative connotations. Even when the outcome of
two choices is equivalent, people have a strong tendency to avoid the negatively framed option
because losses are perceived about twice as salient as gains of the same amount (i.e. loss-
aversion). The ventromedial prefrontal cortex (vmPFC) is crucial for rational decision-making,
and dysfunctions in this region have been linked to cognitive biases, impulsive behavior and
gambling addiction. Using a financial decision-making task in combination with
magnetoencephalographic neuroimaging, we show that excitatory compared to inhibitory non-
invasive transcranial direct current stimulation (tDCS) of the vmPFC reduces framing-effects
while improving the assessment of loss-probabilities, ultimately leading to increased overall
gains. Behavioral and neural data consistently suggest that this improvement in rational decision-
making is predominately a consequence of reduced loss-aversion. These findings recommend
further research towards clinical applications of vmPFC-tDCS in addictive disorders.
3 key words: tDCS, vmPFC, MEG
Introduction
We humans like to believe that our decision-making behavior follows rational
considerations. We think that as rational people we can conscientiously analyze the consequences
of available options by weighing their probabilities and comparing the results to finally choose
the optimal option that maximizes our gain and minimizes our loss. However, in pioneering
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studies leading to the development of the ‘prospect theory’, Kahneman and Tversky showed that
humans often tend to rely on heuristics and other cognitive shortcuts, instead of employing
cognitively demanding evaluations (Kahneman and Tversky, 1979; Tversky and Kahneman,
1992). Remarkable deviations from the behavior of an ideal rational agent (‘homo economicus’)
are direct consequences of a strong loss-aversion, as humans rate losses twice as salient as gains
of the same amount (Kahneman and Tversky, 1979; Tversky and Kahneman, 1992). This
cognitive bias of loss-aversion likely roots in an asymmetric evolutionary pressure on losses and
gains: “when survival is uncertain, marginal losses prove more critical for reproductive success
than marginal gains” (McDermott et al., 2008). Loss-aversion has been used to explain a wide
range of economic behaviors that compromise rationality such as the sunk-cost effect (Arkes and
Blumer, 1985; i.e. the belief that previous investments and even losses justify further
expenditures) or the status-quo bias (Kahneman, Knetsch and Thaler, 1991; i.e. changes from the
status-quo are expected as rather negative). A well-studied example to illustrate further far-
reaching consequences of loss-aversion is the so called framing-effect, where presenting either
positive or negative connotations of an option can bias decisions: For instance, when people are
asked to either opt for a treatment A which would save 400 of overall 600 infected patients or for
treatment B with which 200 of 600 patients would die, a vast majority would prefer treatment A,
which is positively framed, but still identical to B in terms of outcomes (Tversky and Kahneman,
1981).
The impact of the framing-effect and reactions to odds — and therefore the modulation of
rational decision-making by any kind of intervention — can nicely be investigated in gambling
studies that can consider gains and losses simultaneously. For example, if participants have to
decide between ‘lose 60ct of 100ct’ or ‘gamble for 100ct with 20% probability’ they would
typically avoid the negatively (‘lose’) framed option and gamble for the whole amount, despite a
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higher expected safe residual gain (40 cent) in contrast to the expected gain (20 cent) of the
gambling decision. Inversely, most participants would choose the positively framed ‘keep 40ct of
100ct’ instead of ‘gamble for a 100ct with 80% probability’, despite the expected gain of the
gambling option (80 cent) would be higher than the safe gain of 40 cents.
Studies investigating the underlying neural correlates of loss-aversion and the resulting
framing-effect in gambling studies consistently identified the ventromedial prefrontal cortex
(vmPFC) as a cardinal player. In fact, neural activity in the vmPFC decreased with increasing
potential losses and the strength of this association predicted individual loss-aversion (Tom et al.,
2007). Consistently, individuals with increasing susceptibility to the framing-effect showed
decreased activity in the vmPFC (Martino et al., 2006). The finding that patients with vmPFC
lesions feature an increased framing-effect relative to patients with other lesions and healthy
controls (Pujara et al., 2015) adds causality to this correlational evidence.
Another aspect of rational decision-making that is dependent on the vmPFC is the
inhibition of impulsive and short-sighted choices. Patients with vmPFC lesions for instance show
a distinct insensitivity to consequences of decision-making and are primarily guided by
immediate prospects (Kahneman and Tversky, 1983). These patients also show increased risk-
taking and bet more money in gambling studies than healthy controls (Studer et al., 2015). This
causal link between vmPFC dysfunctionality and irrational economic behavior is further
supported by the finding that the reduction of rational economic decision-making with aging
correlates with gray matter volume reduction in ventral PFC regions (Chung et al., 2017).
Since reduced vmPFC activity and vmPFC dysfunctions have been associated with
irrational decision-making based on loss-aversion, as indexed by an increased susceptibility to
framing-effects and poorer assessments of the odds, it appears tempting to assume that excitatory
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vmPFC stimulation (i.e. increasing vmPFC excitability) might have mitigating effects on these
biases and might improve rational decision-making. Because the vmPFC is an almost unique
structure in coding gains with activity increase and losses with activity decrease (Lindquist et al.,
2016; Tom et al., 2007), the vmPFC recommends itself as ideal target for valence specific
interventions via brain stimulation (i.e. modulation of valence biases such as loss-aversion). In
fact, a series of fMRI and MEG studies from our lab recently revealed that excitatory stimulation
of the vmPFC attenuated behavioral and neural negativity-biases to emotional scenes and
emotional facial expressions in healthy participants (Junghofer et al., 2017; Winker et al., 2020,
2019, 2018).
Here we combined the financial gambling paradigm developed by Kahneman and
Tversky (1983) - and adopted by De Martino and coworkers (2006) as well as Pujara and
colleagues (2015) - with the vmPFC stimulation and MEG neuroimaging approach as used in our
previous studies (Junghofer et al., 2017; Winker et al., 2020, 2019, 2018). Within this paradigm,
participants decided to either accept a safe amount of an initial stake or gamble for the whole
amount with variable risks to win or lose. The safe option was either framed in a positive or
negative way with identical net gains for both frames. The probability of taken risks thus
informed about the susceptibility to the framing-effect and the consideration of odds which here
were operationalized as indices of rational decision-making. Participants finally received
feedback about gains and losses. The participants’ evaluation of gains and losses informed about
the strength of the framing-effect in this feedback phase which was used as further index of
rational decision-making.
Directly preceding the financial gambling task, one half of the participants received
excitatory vmPFC stimulation at a first session and inhibitory vmPFC stimulation at a second
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session while the other half received the reversed order. We hypothesized that vmPFC-excitation
by transcranial Direct Current Stimulation (tDCS) would result in a decreased loss-aversion. In
contrast to inhibitory stimulation, this should be reflected in an attenuated framing-effect (i.e. a
decreased tendency to gamble in the ‘loss-frame’ and thus more rational decision-making) as well
as an improved consideration of odds and ultimately higher gains.
Investigations of event related potentials (ERPs) of gain and loss processing during
gambling tasks have consistently identified ERP components at mid-latency to late time intervals
(around 250ms after feedback) and at central to frontal-central scalp regions (Gehring and
Willoughby, 2002; Hajcak et al., 2006; Yeung et al., 2005). While these components typically
reflect responses to both positive and negative feedback, stronger reactions to losses as compared
to gains, most presumably reflecting loss-aversion, have been identified in various ERP studies
(Hajcak et al., 2006; Yeung et al., 2005). Thus, MEG correlates of reduced loss-aversion and
attenuated framing after excitatory stimulation were expected to occur at prefrontal regions and
mid-latency to late time intervals. Since previous research has shown that the vmPFC mediates
rationality by inhibiting maladaptive responses (i.e. irrational loss-aversion or choosing high-risk
options; Boes et al., 2009; Manuel, Murray and Piguet, 2019) we expected that the neural data
would reflect an enhanced ability of prefrontal brain regions to inhibit loss-aversion (in the
decision-phase and feedback-phase) and thus maladaptive choices (in the decision-phase)
following excitatory compared to inhibitory stimulation.
With the focus on effects of vmPFC stimulation on rational decision-making and
feedback-processing, behavioral and neural effects which were modulated by the stimulation are
reported in the following results section. For main effects and further interactions please consult
the supplementary material (SM).
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Results
Non-invasive vmPFC-tDCS modulates rational decision-making
We applied a within-subjects design so that each participant received excitatory and
inhibitory stimulation of the vmPFC on two different days with at least 48 hours in between
(Methods Fig. 1). The tDCS was optimized for targeting the vmPFC and minimizing the impact on
other brain regions (Methods Fig. 2). In both excitatory and inhibitory conditions participants were
stimulated with a current strength of 1.5mA over 10 minutes and were unable to differentiate
between stimulation conditions (see SM2.1). Immediately after stimulation, participants performed
the gambling task, where gambling behavior and event-related magnetic fields were measured. At
the beginning of each trial, participants received an initial amount (‘gambling stake’), which was
varied (25ct, 50ct, 75ct, 100ct) to enhance the credibility of the gambling paradigm (see Fig. SM2).
Participants were asked to either keep a smaller amount or to gamble for the full amount. The 'keep'
option was either framed as a gain (Fig. 1A; gain-frame: receive a smaller but safe amount) or
framed as a loss (Fig. 1A; loss-frame: subtraction of a smaller safe amount). Although the final
monetary amounts were equivalent in both frames, participants chose the ‘gamble’ option in the
loss-frame and accepted the safe smaller amount in the gain-frame more often, replicating the
framing-effect(Kahneman and Tversky, 1983) (Fig. 1B; z = 2.64 , p = 0.008). Next, we specifically
tested the hypothesis that noninvasive vmPFC stimulation modulates the framing-effect as index
of rational decision-making. Indeed, a logistic regression with the predictors of stimulation
(excitatory, inhibitory) and frame (gain-frame, loss-frame) revealed a significant interaction (z =
2.19, p = 0.029) modulating the decision (keep, gamble). Importantly and supporting our
hypothesis, excitatory vmPFC stimulation resulted in a significantly reduced framing-effect
compared to inhibitory stimulation (Fig. 1B; χ² = 14.74, p < 0.001). Furthermore, excitatory
stimulation significantly reduced the proportion of gambling choices in the loss-frame (χ² = 12.24,
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p < 0.001) but not in the gain-frame (χ² = 0.086, p = 0.769) supporting the hypothesis that reduced
loss-aversion after excitatory stimulation underlies the attenuated framing-effect.
To uncover underlying neural correlates of the framing-effect and its interaction with
stimulation, we computed a 2x2x2 repeated-measures ANOVA on source-localized MEG data (the
dependent variable; see SM1.4) with the factors stimulation (excitatory, inhibitory), frame (gain-
frame, loss-frame) and decision (keep, gamble). The interaction of stimulation by frame was
significant at the junction of right anterior temporal and right orbitofrontal regions in a mid-latency
time interval between 220 and 270ms (p-cluster = 0.023; Fig. 1C). Post-hoc t-tests of this
interaction revealed less activation in the gain-frame after excitatory compared to inhibitory
stimulation (t = 1.77, p = 0.044), while activation in the loss-frame showed the opposite trend-
significant effect (t = -1.65, p = 0.055). The relatively greater neural response to the ‘loss-frame’
after excitatory tDCS suggests an improved inhibition of loss-aversion, most probably leading to a
reduced salience of losses and eventually resulting in an attenuated framing-effect as seen in the
behavioral stimulation effects. The location of the cluster nicely converges with fMRI findings of
Martino and coworkers (2006), who showed that, in addition to vmPFC areas, enhanced activity at
right orbitofrontal cortex regions (OFC) was also associated with more rational decision-making
as reflected by reduced framing-effects.
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Figure 1. A. Course of a single trial in the gambling task adapted from Martino and colleagues (2006). Each trial began
with a fixation cross presented for 500ms, followed by the presentation of the ‘game stake’ of 25, 50, 75 or 100 cents.
The subsequent ‘choice stimulus’ reminded participants of the initial amount (center), the chance to win or the risk of
losing when choosing the ‘gamble’ option (20%, 40%, 60%, 80%, based on the relative sizes of the blue and yellow
circles), and the frame when choosing the safe ‘keep’ option (green and red outer ring for gain and loss frames,
respectively). After choosing the ‘keep’ or ‘gamble’ option, feedback on the win or loss was given via green (win) and
red (loss) circles, with the amount depicted in the center. Stimuli were placed centrally to minimize eye movements
and related MEG artifacts. MEG correlates of neural activity evoked by the choice and the feedback stimuli were
analyzed.
B. Proportion of ‘gamble’ choices for gain- and loss-framed trials in percentage. An ideal rational agent would have
chosen the gain-frame option and the loss-frame option in equal frequency, as both resulted in identical wins or losses.
However, replicating a strong deviation from rationality, participants chose the risky ‘gamble’ option in the loss-framed
condition much more often. Importantly, this framing-effect (i.e., a preference for gambling in the loss frame compared
to in the gain frame) was stronger after inhibitory than excitatory stimulation of the vmPFC. Convergent with the idea
that vmPFC excitation modulates loss-aversion, the effect of exciting the vmPFC was highly significant in the loss
frame but not in the gain frame.
C. Significant spatio-temporal cluster in right anterior temporal/orbitofrontal areas featuring an interaction effect of
stimulation by frame. The relatively greater neural activation in response to the loss frame after excitatory tDCS
suggests that vmPFC excitation results in more elaborate inhibition of the loss-frame processing than in vmPFC
inhibition, such that excitation leads to a reduced saliency of the loss condition and, eventually, a reduced framing-
effect, as seen in B. The location of this cluster agrees well with results of the Martino et al. study (2006), in which
enhanced activity in the vmPFC as well as in right OFC regions was associated with more rational decision-making.
Topographies of effects observed in L2-MNE were projected on standard 3D brain models for visualization. Boxplots
indicate means (black dot), medians (grey line) and lower and upper quartiles. Asterisks indicate significance levels:
+ < 0.1, * < 0.05, ** < 0.01, *** < 0.001.
Next, we aimed to investigate whether the more rational decision-making following
excitatory versus inhibitory vmPFC stimulation as shown above in the ‘keep’ option generalized
to decision-making depending on the odds (i.e. when choosing the ‘gamble’ option). Here, the risk-
to-lose or the chance-to-win respectively was varied in steps of 20%, 40%, 60% and 80%, indicated
by the size relation of the inner blue and yellow circles in both frames (Fig. 2A). Of course,
participants increasingly avoided gambling with increasing risk-to-lose (z = -9.60, p < 0.001). But
importantly, the effect of risk-to-lose on gambling behavior was significantly modulated by
stimulation (risk-to-lose by stimulation: z = 6.60, p < 0.001; Fig. 2B). Post-hoc tests revealed more
risky-choices in the two low-risk conditions (20%: χ² = 35.81, p < 0.001 and 40%: χ² = 4.81, p =
0.028) but fewer risky-choices in the two high-risk conditions (60%: χ² = 37.32, p < 0.001 and
80%: χ² = 40.97, p < 0.001) after excitatory compared to inhibitory stimulation. A representation
of the averaged winnings achieved in the game across all choices (‘keep’ or ‘gamble’; Fig. 2C)
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elucidates the consequences of the participant’s behavior. A constant choice of the ‘keep’ option
would have resulted in averaged winnings of 25 cents across all risk conditions (Fig. 2C dotted
line). A constant choice of the ‘gamble’ option would have resulted in averaged winnings of 50
cents at 20% risk, 37.5 cents at 40% risk, 25 cents at 60% risk and 12.5 cents at 80% risk (Fig. 2C
dashed lines). Thus, participants behaved quite rational as they almost reached the maximal win
for all risk conditions. However and importantly, a t-test across all trials (t(11519) = 2.23; p =
0.026) revealed that after excitatory vs. inhibitory stimulation averaged wins were significantly
higher. Post-hoc t-tests showed significantly higher winnings in the lowest 20% (t = 1.74, p =
0.041) and predominately in the highest 80% (t = 2.95, p = 0.002) risk conditions after excitatory
compared to inhibitory stimulation. Averaged winnings did not differ in the 40% condition (t =
0.62, p = 0.267) and could not differ in the 60% risk-to-lose conditions because both ‘keep’ and
‘gamble’ choices resulted in an identical amount of 25ct.
On the neural level, we performed a 2x4 repeated-measures ANOVA with the factors
stimulation (excitatory, inhibitory) and risk-to-lose (20%, 40%, 60%, 80%) on source-localized
MEG data (dependent variable). This analysis revealed an interaction of stimulation by risk-to-lose
between 310 and 460ms (Fig. 2D) at left anterior temporal and left orbitofrontal regions (p-cluster
= 0.025) and thus later but laterally symmetric to the above reported interaction of stimulation by
frame (Fig. 1C). Post-hoc tests of neural activity within this cluster revealed that this interaction
was driven by the lowest and highest risk conditions (20%: t = -1.56, p = 0.065; 80%: t = 1.85, p =
0.038), while the medium risks, did not show significant effects of stimulation (40%: t = -0.82, p
= 0.209; 60%: t = 0.23, p = 0.409). Convergent with the behavioral effects, this neural pattern
suggests that excitatory compared to inhibitory stimulation facilitates rational decision-making
towards maximized winnings.
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Taken together, the behavioral data consistently indicates a modulation of decision-making
towards increased rationality after excitatory compared to inhibitory vmPFC-stimulation. The
reduced framing-effect and greater ability to estimate risks is mirrored by the neural data providing
relatively enhanced inhibition of loss-aversion and high-risk options after excitatory versus
inhibitory stimulation.
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Figure 2. A. The relative risk-to-lose percentages or chance-to-win percentages, respectively, when choosing the
‘gamble’ option was based on the relative sizes of the blue and yellow inner circles of the choice stimulus.
B. Proportion of ‘gamble’ choices in percentage depending on the respective risk-to-lose. After excitatory stimulation,
participants gambled more often at the lower risk-to-lose conditions (20% and 40%) but avoided gambling more often
at higher risk-to-lose conditions (60% and 80%).
C. Mean expected values of actual choices in cents depending on the respective risk-to-lose conditions. The mean
expected outcome for the ‘keep’ option averaged across all initial amounts was 25 cents (dotted orange line in C). The
mean expected outcome of the ‘gamble’ option across all initial amounts was 50 cents at 20% risk, 37.5 cents at 40%
risk, 25 cents at 60% risk and 12.5 cents at 80% risk (dashed blue lines in C). Concluding, to maximize the expected
value (i.e., their overall winnings) participants should have always chosen the ‘gamble’ option in the 20% and 40%
risk conditions and should have always chosen the ‘keep’ option in the 80% risk condition (in the 60% risk condition,
the ‘keep’ and ‘gamble’ options led to identical averaged wins). Without stimulation, participants performed already
quite rationally, as they almost reached the maximal wins for each risk-to-lose condition. However, in the low-risk
20% condition and the high-risk 80% condition, participants reached significantly higher winnings after excitatory
compared to inhibitory stimulation. As the ‘keep’ and ‘gamble’ choices both resulted in 25 cents for the 60% condition,
the respective expected values were always identical and are shown for clarity reasons only.
D. Significant spatio-temporal cluster at left prefrontal and anterior temporal areas featuring an interaction effect of
stimulation by risk-to-lose condition. Integration of these neural responses with the behavioral results (Fig. 2B&C)
suggests that excitatory (versus inhibitory) stimulation gave participants a greater ability to inhibit inadequate risky
behavior in the high-risk 80% condition, while it reduced the inhibition of risky behavior (i.e., risky behavior was
facilitated) in the low-risk 20% and semi-low-risk 40% conditions. Thus, convergent with the behavioral effects, this
neural pattern suggests that excitatory compared to inhibitory stimulation facilitates rational decision-making toward
the expected value (Fig. 2C).
Topographies of effects observed in L2-MNE were projected on standard 3D brain models for visualization. Boxplots
indicate means (black dots), medians (grey lines) and lower and upper quartiles. Asterisks indicate significance levels:
+ < 0.1, * < 0.05, ** < 0.01, *** < 0.001.
Non-invasive vmPFC-tDCS modulates loss-aversion in feedback-processing
After the participant's choice, the feedback on win or loss was indicated by green and red
circles with the amounts of the wins or losses in the middle, respectively (Fig. 3A). Finally,
participants rated their subjective hedonic valence and emotional arousal in response to each
outcome. Having established that neurostimulation significantly modulated the rationality of
decision-making, here we aimed to determine the effects of tDCS on rational feedback-processing
in particular on its modulation of loss-aversion. We addressed this question by computing a mixed
effects linear regression with the predictors stimulation (excitatory, inhibitory), outcome (gain,
loss) and decision (keep, gamble). A main effect of outcome (t = -25.08, p < 0.001; Fig. 3A)
reflected the trivia that gains were rated more positive than losses. Importantly, feedback
evaluations were overall (across keep and gamble decisions; Fig. 3A&B) rated more positive after
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excitatory than inhibitory stimulation (t = 2.99, p = 0.003). A main effect of decision (t = -5.16, p
< 0.001; keep > gamble) was mainly driven by a less negative evaluation of losses in the ‘keep’
condition after excitatory stimulation which will be further discussed below. While stimulation did
not affect the factors decision and outcome alone (stimulation by decision: t = -0.17, p = 0.867;
stimulation by outcome: t = 1.65, p = 0.245), the three-way interaction was significant (stimulation
by decision by outcome: t = 5.33, p < 0.001). Post-hoc repeated-measures ANOVAs that were
conducted separately for the 'keep' and 'gamble' conditions revealed a significant interaction of
stimulation by outcome in the ‘keep’ condition (F(1, 35) = 11.48, p = 0.001; Fig. 3A), while the
respective interaction was insignificant in the 'gamble' condition (F(1, 35) = 1.79, p = 0.190; Fig.
3B) indicating that the three-way interaction was mainly driven by the 'keep' condition. To further
elucidate the influence of outcome in the three-way interaction and the effect of stimulation on
rational decision-making, we calculated the difference of gain-ratings minus loss-ratings after
‘keep’ and ‘gamble’ decisions and calculated t-tests comparing excitatory and inhibitory
stimulation. With respect to modulations of the framing-effect, the difference of gain-ratings minus
loss-ratings in the ‘keep’ condition (Fig. 3A) was of major interest since both options held the same
monetary value while monetary outcomes were very different in the ‘gamble’ condition (Fig. 3B).
As predicted, the ‘framing-difference’ was smaller following excitatory compared to inhibitory
tDCS (t(35) = -3.14, p = 0.003), reflecting less loss-averse feedback-processing, while the
corresponding t-test in the ‘gamble’ condition was insignificant (t(35) = 1.64, p = 0.111).
To investigate the neural responses to the feedback stimuli, we performed a 2x2x2 ANOVA
employing the factors stimulation (excitatory, inhibitory), decision (keep, gamble) and outcome
(gain, loss). In a repeated-measures ANOVA, this did not reveal a significant main effect of
stimulation nor significant interactions with the factor stimulation. This was most likely due to the
extremely strong main effect of outcome (loss >> gain) - reflecting the strong loss-aversion since
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losses are usually rated twice as salient as gains - that explained a large part of variance (see
SM2.4.3). Nevertheless, we tested our specific hypothesis regarding the modulation of framing-
effects via tDCS by subtracting gains from losses in the ‘keep’ option (i.e. the relevant comparison
to evaluate the effect of stimulation on framing) and used the resulting difference for a t-test
(excitatory versus. inhibitory). This revealed a significant cluster (p-cluster = 0.045) in the
dorsomedial prefrontal cortex in a late time interval between 470 and 510ms (Fig. 3C). Post-hoc
tests of neural activity within this cluster revealed greater activations in response to losses after
excitatory compared to inhibitory stimulation (t = 2.79, p = 0.010) while stimulation did not
modulate gain trials (t = -0.79, p = 0.437). The increased responses to losses in the ‘keep’ condition
following excitatory stimulation suggest facilitated inhibition of loss-aversion leading to less
negative evaluations of losses (Fig. 3A) and eventually more rational evaluation of feedback
stimuli.
In summary, convergent to effects in the decision-making phase, the analysis of feedback-
processing also revealed that excitatory compared to inhibitory stimulation reduced behavioral
loss-aversion and enhanced inhibition of loss-aversion in neural measures.
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Figure 3. A. Rated hedonic valence (pleasantness) on a SAM scale - 1 (most negative) to 9 (most positive) - in the
‘keep’ condition with equal monetary outcomes.
B. Rated hedonic valence in the ‘gamble’ condition with unequal monetary outcomes.
Excitatory stimulation led to an overall (across keep and gamble option) more positive feedback evaluation and resulted
in a relatively reduced framing-effect (the difference between gain and loss ratings in the ‘keep’ option, A). Thus,
excitatory stimulation led to an attenuated loss-aversion-bias and to more rational (i.e., less loss averse) feedback-
processing.
C. Significant spatio-temporal cluster in the dorsomedial prefrontal cortex featuring a significant effect of stimulation
revealed by a t-test employing the difference of gain minus loss in the relevant framing ‘keep’ option. The greater
activation in response to ‘keep’ losses following excitatory compared to inhibitory stimulation suggests that excitation
stimulation helped inhibit negative feedback, leading to a reduced negative evaluation of losses in the keep option (A)
and eventually to a more rational (i.e., less loss averse) evaluation of feedback stimuli.
Topographies of effects observed in L2-MNE were projected on standard 3D brain models for visualization. Boxplots
indicate means (black dots), medians (grey lines) and lower and upper quartiles. Asterisks indicate significance levels:
+ < 0.1, * < 0.05, ** < 0.01, *** < 0.001.
Discussion
We investigated the contribution of the vmPFC on rational decision-making and feedback-
processing by increasing or reducing its excitability via non-invasive tDCS. We found evidence
for a causal role of vmPFC-activity in rational decision-making on both behavioral and neural
levels. Excitatory stimulation induced a more adaptive decision-making, which was reflected by
reduced loss-aversion that resulted in less susceptibility to the framing-effect, a greater likelihood
to choose the option providing the higher expected value and a decreased tendency to risk larger
amounts (SM2.3.2). The neural data supported this pattern, suggesting an increased ability of
bilateral ventral prefrontal cortex regions to inhibit maladaptive choices (reduction of loss-aversion
and less frequent choice of options with a high risk-to-lose) and thus enable more adaptive and less
impulsive gambling behavior after excitatory stimulation. The same applied to the feedback-
processing, as reduced loss-aversion with less susceptibility to the framing-effect was present after
excitatory stimulation in both, behavioral and neural data. Furthermore, excitatory stimulation
resulted in an overall reduction of the loss-aversion-bias for feedback-processing. Overall, our
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results show that non-invasive brain stimulation of the vmPFC can influence the rationality of
human decision-making and feedback-processing as seen on both behavioral and neural levels.
Remarkably, we could show that vmPFC-inhibition led to increased susceptibility to the
framing-effect. As seminal studies have previously shown (Kahneman and Tversky, 1983, 1979),
participants exhibited more risk-taking behavior in the ‘loss-framed’ option, compared to the ‘gain-
framed’ option. This bias was further increased by inhibitory compared to excitatory stimulation,
as participants featured even more risk-taking behavior in the ‘loss-framed’ option after vmPFC-
deactivation (Fig. 1B). The importance of vmPFC-activation to integrate frames into decisions has
been found before (Deppe et al., 2005) and is present in our neural data as well (Fig. 1C). We found
greater activity in response to the ‘loss-frame’ following excitatory stimulation in the right OFC
replicating a correlation of right OFC-activity and rational decision-making in the fMRI study from
Martino and coworkers (2006). Greater OFC-activation to the ‘loss-frame’ after excitatory
stimulation suggests an improved inhibition of a potentially irrational loss-aversion. In contrast,
inhibitory stimulation could even strengthen this non-adaptive tendency (i.e. intensifying loss-
aversion) and promote a more impulsive decision for the ‘gamble’ option, although the expected
value may be higher in the ‘loss-framed’ option. Accordingly, a study employing delayed reward
found that excitatory tDCS of the vmPFC improved the ability to wait for rewards and reduce
impulsivity accordingly (Manuel et al., 2019).
Second, vmPFC- excitation decreased risk-taking behavior, when the risk of losing was
high and increased risk-taking, when the chance to win was high, overall enhancing the expected
value of decisions and the overall wins compared to inhibitory stimulation (Fig. 2B&C). This is
consistent with previous findings showing that patients with vmPFC-lesions are less able to assess
the probability of gains/losses and consequently win less money than healthy controls (Studer et
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al., 2015). The spatiotemporal MEG cluster of stimulation by risk-to-lose overlapping the vmPFC
(Fig. 2D) may illuminate the underlying neural mechanism: after inhibitory stimulation an
increasing risk-to-lose came along with decreasing prefrontal activity, suggesting that vmPFC-
inhibition reduced the ability to suppress maladaptive choices. By contrast, excitatory tDCS
facilitated this capability in participants as indicated by increasing prefrontal activity with
increasing risk to lose, in turn leading to more adaptive decision-making. This result can be
underlined with findings of an fMRI study, where greater activity in medial prefrontal areas
occurred when risk anticipation was necessary (Fukui et al., 2005). Concluding, our results
combined with previous findings strongly suggest that ventro-medial prefrontal activity is
responsible for risk assessment and that this process can be modulated by non-invasive tDCS.
Another important finding is the increased tendency to gamble with increasing ‘game
stakes’ after inhibitory stimulation, while risk-taking behavior did not change after vmPFC-
excitation (see SM2.3.2). This is consistent with the finding that vmPFC-lesioned patients
typically bet more money than healthy participants (Studer et al., 2015). Additionally,
pathological gamblers show vmPFC-hypoactivation and its strength was correlated with
gambling severity (Reuter et al., 2005). Combined with the finding that excitatory stimulation
induced less risk-taking behavior overall and specifically in high-risk situations, these findings
suggest that excitatory vmPFC-tDCS might be a promising add-on treatment option for patients
suffering from gambling addiction. It should be noted, that the reported behavioral and neural
effects occurred after only one rather short and mild excitatory/inhibitory tDCS (far below the
thresholds considered to be safe; Sparing and Mottaghy, 2008). Thus, follow-up clinical tests on
add-on therapy effects have the option to use more sessions, longer durations and greater
intensities of excitatory stimulation.
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Now considering feedback-processing we can further elaborate the idea that the vmPFC
modulates loss-aversion and, thus, is important for more rational (i.e. less biased) feedback-
processing and can be modulated through tDCS. We identified that the significant interaction of
stimulation by decision by outcome (Fig. 3A and 3B) was driven by a greater framing-effect after
inhibitory stimulation. Thus, as in decision-making, more rational feedback-processing (i.e.
decreased susceptibility to framing) occurred after excitatory compared to inhibitory stimulation.
This can also be explained by a model that sees the vmPFC as a region tracking the (financial)
value of a decision providing the basis for learning from reward and punishment. This function is
implied by lesion (Liu et al., 2011) and functional imaging (Pujara et al., 2015; Tom et al., 2007)
studies, which suggest that the vmPFC is also responsible for evaluating decisions and guiding
future behavior by these experiences. The relevance of the vmPFC for learning and prediction is
underlined by a study on cocaine addicts (Parvaz et al., 2015), in which the so called feedback-
negativity, an ERP-component that is predominately modulated by vmPFC-activity (Carlson et
al., 2011), was examined. Cocaine addicts exhibited an impaired feedback-processing in response
to losses (Parvaz et al., 2015), indicating their inability to learn from losses possibly related to
vmPFC-hypoactivation eventually resulting in compromised predictions.
More positive feedback ratings overall following excitatory vmPFC-tDCS also dovetail
with our previous findings of a relative reduced negativity-bias of emotional face and emotional
scene processing after excitatory compared to inhibitory stimulation (Junghofer et al., 2017;
Winker et al., 2020, 2019, 2018). Indeed, losses in the ‘keep’ option in particular were rated as less
negative after excitatory compared to inhibitory stimulation, which fits with the hypothesis that
vmPFC-excitation reduces loss-aversion. Convergent, losses in the ‘keep’ option also evoked
stronger neural responses after excitatory compared to inhibitory stimulation (Fig. 3C). Like the
strengthened neural responses to loss-frames in right anterior temporal/orbitofrontal areas (Fig. 1C)
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after excitatory stimulation - which was interpreted as improved inhibition of a loss-aversion-bias
- the neural responses to ‘keep’ losses in dorsomedial prefrontal areas also suggest an enhanced
inhibition of loss processing eventually leading to a less negative evaluation of ‘keep’ loss
outcomes as seen in figure 3A. In fact, dorsolateral and dorsomedial prefrontal areas are typically
active during implicit (Roesmann et al., 2020) and explicit (Ochsner and Gross, 2005) suppression
of negative stimulus processing (e.g. the cognitive control of emotion).
As expected, spatiotemporal neural clusters involving stimulation and rational choice and
feedback effects occurred in mid-latency or late time intervals (earliest effect starts at 220ms). This
is probably due to the nature of rational processing, as a higher-order cognitive process, that is
connected downstream to sensory stimulus processing and emotionally driven decisions. ERP
results confirm this pattern, where correlates of rational decision-making are typically not found
earlier than the P300 (Wichary et al., 2017). In comparison, clusters that are more related to
impulsive decision-making have their onset already in rather early time ranges (Fig. SM3, SM4,
SM5A).
Despite these new insights in vmPFC-functioning and non-invasive brain stimulation in
decision-making and feedback-processing, there are limitations to consider. First, to guarantee
successful blinding of participants to the stimulation conditions and to reduce inter individual
variance we opted here for a within-subjects-design and first sacrificed the comparison with a sham
condition (i.e. participants easily detect the difference between active and sham tDCS while both
active conditions are typically undistinguishable; see SM2.1). While all conclusions regarding
causal functionality and modulating capability of the vmPFC remain unaffected, it remains to be
resolved whether inhibitory stimulation might solely evoke temporary vmPFC dysfunctions in
healthy controls as reported in patients and/or if excitatory stimulation might improve rational
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decision-making even in healthy participants. To investigate this question, follow-up studies should
use between-designs comparing effects of excitatory versus sham vmPFC-tDCS in healthy
participants. Such designs could also be used in clinical settings to test a complementary treatment
option for gambling (and other behavioral) addictions. In fact, first indications for positive effects
of mPFC stimulation have been demonstrated for obsessive-compulsive disorders (Adams et al.,
2021), which is likewise associated with impaired impulse-control. Furthermore, the neural data of
the feedback phase contained limited informative value, due to the extremely strong differential
effects of the outcomes (loss >> gain, driven by loss-aversion), which explained most variance and
potentially masked relevant interaction effects. Nevertheless, we could at least partly circumvent
this problem by calculating a t-test, that was justified by our hypotheses and by the behavioral
effects.
Conclusion
Our results not only support the claim that the vmPFC plays a causal role for rational
decision-making and feedback-processing, but they also indicate that rationality can be modulated
by its non-invasive stimulation. Improved decision-making after excitatory vmPFC-tDCS as
reflected in maximized wins and decreased susceptibility to the framing-effect is a compelling
illustration of this influence. The same is true for the feedback phase, where less loss-biased
stimulus processing might guide more adaptive behavior in the future. These higher-order cognitive
processes were reflected in prefrontal clusters of neural activity at mid-latency to late time
intervals. They putatively indicate that loss-aversion can be attenuated leading to more rational
decision-making. The finding that vmPFC-excitation compared to inhibition can induce more
rational decision-making and feedback-processing raises the hope for clinical applications of
vmPFC-tDCS in addiction disorders such as pathological gambling.
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Methods
Participants
We included 37 (17 women) right-handed volunteers, aged 19 to 29 years (M = 23.42, SD
= 2.70) meeting the inclusion criteria (see SM1.1). Participants were pseudo-randomly assigned to
experimental groups that were matched regarding demographic and psychometric features. The
study was ethically approved by the ethics committee of the medical school at the University of
Münster.
Participants were told a cover story to ensure authentic gambling behavior. It was stated
that they could win an amount between 0 and 36€ in addition to the fixed allowance of 30€.
Following the study, participants were elucidated regarding the cover story and everybody received
the full amount of 66€.
Specifications on the Gambling Task
Each trial began with a fixation cross after which the initial amount of money was
presented, for which the participants could successively gamble with a specified risk (game stake
of 25ct, 50ct, 75ct or 100ct). Second, the ‘choice stimulus’ appeared on which basis the participants
had to decide whether they would choose a safe (‘keep’) or a risky (‘gamble’) option. The framing-
effect is relevant in the ‘keep’ option: If the game stake was for instance 50ct (Fig 1A), the green
gain-frame informed about a safe win of 20ct (i.e. equivalent to a safe loss of 30ct) while the red
loss-frame predicted a safe loss of 30ct (i.e. equivalent to a safe win of 20ct). According to this
scheme the gain-frames informed about safe wins of 10ct, 30ct, or 40ct and the loss-frames of safe
losses of 15ct, 45ct, or 60ct, if the initial amounts were 25ct, 75ct or 100ct, respectively.
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Experimental procedure
In a within-subjects-design each participant received excitatory/anodal and
inhibitory/cathodal stimulation over the course of two sessions with a minimum-interval of 48
hours between the sessions (Methods Fig. 1). The allocation of stimulation order (excitatory or
inhibitory stimulation first) was randomized across participants. In the beginning of the first
session, participants gave written informed consent, filled questionnaires, comprising the Beck
Depression Inventory (Beck et al., 1996), the Reward Responsiveness scale (Van den Berg et al.,
2010), the Intolerance of Uncertainty scale (Gerlach et al., 2008), and the Social Desirability Scale
(Crowne and Marlowe, 1960). After tDC stimulation, participants executed the gambling task in
the MEG, where event-related fields (ERFs) in response to the choice- and feedback stimuli were
measured. At the end of each session, participants rated the feedback regarding subjective hedonic
valence and emotional arousal on a self-assessment manikin (SAM) rating scale (Bradley and Lang,
1994), rated their state mood on the Positive and Negative Affect Schedule (PANAS; Watson,
Clark & Tellegen, 1988) and rated their perceived stimulation pleasantness and stimulation
intensity on an in-house questionnaire. In the second session, the same procedure was used with
the opposite stimulation polarity. Finally, subjects were elucidated about the cover story. The
duration of both sessions combined was approximately 200 minutes.
Methods Figure 1. Overview over the experimental procedure. Abbreviations: BDI-II: Beck Depression Inventory-II.
RR: Scale for Measuring Reward Responsiveness. UI-18: Intolerance of Uncertainty scale. SDS-CM: Social
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Desirability Scale by Crowne and Marlowe. SAM-Rating: Subjective Ratings of Hedonic Valence and Emotional
Arousal. PANAS: Positive and Negative Affect Schedule. For results of the questionnaires see SM1.1.
tDCS Transcranial direct current stimulation (tDCS) is a widespread and effective method to
modulate brain activity from outside the skull. An anodal or excitatory stimulation depolarizes
the membrane potential of neurons, what increases their excitability depending on the strength of
the applied electric field. In contrast, cathodal or inhibitory stimulation hyperpolarizes the neuron
membrane, reducing the likelihood of action potentials (Sparing and Mottaghy, 2008). An
important advantage of tDCS is the low rate of side effects (e.g. headache, nausea and insomnia)
and, with particular relevance for ventral prefrontal target regions, the absence of unwanted co-
stimulation of facial and ocular muscles and nerves. Changes of cortical excitability can last up to
one hour after a single stimulation (Poreisz et al., 2007). We implemented the tDCS-montage as
used in our previous studies to non-invasively stimulate the vmPFC (Junghofer et al., 2017;
Roesmann et al., 2021; Winker et al., 2020, 2019, 2018). The active electrode was placed on the
forehead (3 x 3 cm) and the extracephalic reference under the chin (5 x 5 cm). Electrodes were
plugged into sponges that were soaked with a sodium-chloride solution to ensure electric
conductivity. For excitatory/anodal or inhibitory/cathodal stimulation, the forehead electrode was
used as anode or as cathode respectively. This electrode-montage results in maximal stimulation
of the vmPFC and minimal stimulation of adjacent brain regions, as revealed by finite-element
based forward modelling of tDCS currents (Wagner et al., 2014). Using a DC Stimulator Plus
(NeuroConn GmbH), we applied a maximum current of 1.5 mA for 10 minutes with both
stimulation polarities.
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Methods Figure 2. An iterative gain function algorithm aiming at maximal vmPFC stimulation revealed an electrode
positioning with a small mid-frontal electrode and an expanded extracephalic chin reference. This array allowed a
quasi-reference-free stimulation, providing clear differentiation of excitatory and inhibitory effects. Participants were
stimulated at two different days for 10 min with 1.5 mA in an either excitatory (anodal forehead electrode) or inhibitory
(cathodal forehead electrode) fashion. While the current strength is identical for anodal and cathodal stimulations, the
direction of effects, as indicated by cones in the magnification, is reversed. A modeled 1.5mA stimulation resulted in
a maximum current density in the vmPFC regions of approximately 0.09 mA/cm2 (red colors). Actually, all sponges
had the same color to prevent any inferring of the participants based on sponge color. This figure was published first
in Junghoefer et al. Cerebral Cortex (2017).
Recording and preprocessing of MEG
Event-related fields were measured using a 275 whole-head sensor system (CTF Systems,
first-order axial gradiometers) with a sampling rate of 600 Hz across a frequency range from 0 to
150Hz (anti-aliasing hardware filtering). The continuous data were down-sampled to 300Hz and
filtered with a 0.1 high-pass-filter and 48 Hz low-pass-filter. We extracted epochs from 200ms
before and 600ms after stimulus onset and employed the interval of -150ms to 0ms for baseline
adjustment. To identify and reject artifacts the method suggested by Junghöfer and colleagues was
used (Junghöfer et al., 2000). With this method, individual and global artifacts were discovered. In
case noisy channels were identified, their signal was estimated by spherical-spline-interpolation
based on the weighted signal of all remaining sensors. A minimum threshold of 0.01 for the
estimated Goodness of Interpolation was applied and trials exceeding this value were rejected. If
more than 30% of the trials were discarded in any session, the respective participant was excluded
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from further analysis (three participants). Trials within each experimental condition were averaged
for each participant and session and the underlying neural sources of the measured ERFs were
estimated by applying L2-Minimum-Norm-Estimates (L2-MNE; Hämäläinen & Ilmoniemi, 1994).
The preprocessing and analysis of the MEG data was performed with the MATLAB (2019b)-based
EMEGS software (version 3.1; Peyk, De Cesarei & Junghöfer, 2011). For details see SM1.2.
Data-Analysis
We used mixed effects models for the behavioral data because of their robustness in
repeated-measures compared to conventional models (Baayen et al., 2008). Detected effects were
then resolved with traditional post-hoc tests.
Aiming to test which factors influence rational decision-making (choice to ‘keep’ or
‘gamble’) we calculated a mixed effects logistic regression with the predictors stimulation
(excitatory, inhibitory), risk-to-lose (20%, 40%, 60%, 80%) and frame (gain-frame, loss-frame).
Since we were mainly interested in the interaction effects of stimulation by frame and stimulation
by risk-to-lose we modeled random effects for these interactions. The overall model was significant
(χ²(16) = 13297.00, p < 0.001) and within this model all main effects got significant: stimulation
(z = -9.69, p < 0.001), risk-to-lose (z = -9.60, p < 0.001) and frame (z = 2.64 , p = 0.008). The
interaction of stimulation by frame was insignificant (z = 0.39, p = 0.695), but since we had specific
hypothesis regarding this effect, we calculated a separate logistic regression with only these
predictors. As expected, the interaction of stimulation by frame was significant in this model (z =
2.19, p = 0.029). However, the interaction of stimulation by risk-to-lose got highly significant in
the overall model (z = 6.60, p < 0.001).
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The neural analyses in the decision-making phase (stimulation by frame by decision and
stimulation by risk-to-lose) were performed separately from each other to ensure a sufficient
number of trials per condition for a reasonable signal-to-noise-ratio in the source estimation. To
correct for multiple comparisons we applied a non-parametric approach proposed by Maris and
Oostenveld (Maris and Oostenveld, 2007). For details see SM1.4.For the analysis of the perceived
hedonic valence (SAM-rating; Bradley & Lang, 1994), 9-point Likert scale) of the feedback, we
computed a mixed effects linear regression with the predictors stimulation (excitatory, inhibitory),
outcome (gain, loss) and decision (keep, gamble). Since we were mainly interested in the
interaction effects of stimulation by decision, we modeled random effects for this interaction.
The overall model was significant (χ²(9) = 526.44, p < 0.001) and revealed significant main
effects of stimulation (t = -2.99, p = 0.003), outcome (t = -25.08, p < 0.001) and decision (t = -5.16,
p < 0.001). While the two-way-interactions of stimulation by decision (t = -0.17, p = 0.867) and
stimulation by outcome (t = 1.65, p = 0.245) were insignificant, the three-way-interaction got
highly significant (outcome by decision by stimulation: t = 5.33, p < 0.001).
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