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Representational uncertainty in the brain during threat conditioning and the link with
psychopathic traits
Inti A. Brazil, PhD, Christoph D. Mathys, PhD, Arne Popma, MD, PhD, Sylco S.
Hoppenbrouwers, PhD., Moran D. Cohn, MD
PII: S2451-9022(17)30089-7
DOI: 10.1016/j.bpsc.2017.04.005
Reference: BPSC 152
To appear in: Biological Psychiatry: Cognitive Neuroscience and
Neuroimaging
Received Date: 23 February 2017
Revised Date: 12 April 2017
Accepted Date: 12 April 2017
Please cite this article as: Brazil I.A., Mathys C.D., Popma A., Hoppenbrouwers S.S. & Cohn M.D.,
Representational uncertainty in the brain during threat conditioning and the link with psychopathic
traits, Biological Psychiatry: Cognitive Neuroscience and Neuroimaging (2017), doi: 10.1016/
j.bpsc.2017.04.005.
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Representational uncertainty during threat conditioning and psychopathic traits
1
Representational uncertainty in the brain during threat conditioning and the link with
psychopathic traits
Inti A. Brazil, PhD
1,2,3,4
, Christoph D. Mathys, PhD
5,6,7
, Arne Popma, MD, PhD
8,9
, Sylco S.
Hoppenbrouwers, PhD.
10
, Moran D. Cohn, MD
8
1
Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the
Netherlands
2
Forensic Psychiatric Centre Pompestichting, Nijmegen, the Netherlands
3
Collaborative Antwerp Psychiatric Research Institute (CAPRI), University of Antwerp,
Antwerp, Belgium
4
Centre for Psychology, Behaviour, & Achievement, Faculty of Health and Life Sciences,
Coventry University, UK
5
Scuola Internazionale Superiore di Studi Avanzati (SISSA), Trieste, Italy
6
Max Planck UCL Centre for Computational Psychiatry and Ageing Research, London, United
Kingdom
7
Wellcome Trust Centre for Neuroimaging, Institute of Neurology, UCL, London, United
Kingdom
8
Department of Child and Adolescent Psychiatry, VU University Medical Center Amsterdam,
Amsterdam, the Netherlands
9
Institute for Criminal Law & Criminology, Leiden University, Leiden, the Netherlands
10
Department of Clinical Psychology, Erasmus University, Rotterdam, the Netherlands
Correspondence:
Inti A. Brazil, PhD
E-mail: I.Brazil@donders.ru.nl
Word count:
Abstract: 245, Main text: 4000, tables: 0, figures: 2, supplements: 1
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Representational uncertainty during threat conditioning and psychopathic traits
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Abstract
Background: Psychopathy has repeatedly been linked to disturbed associative learning from
aversive events (i.e., threat conditioning). Optimal threat conditioning requires the
generation of internal representations of stimulus-outcome contingencies and the rate with
which these may change. Because mental representations are imperfect, there will always be
uncertainty about the accuracy of representations in the brain (i.e., representational
uncertainty). However, it remains unclear i) to what extent threat conditioning is susceptible
to different types of uncertainty in representations about contingencies during the
acquisition phase and ii) how representational uncertainty relates to psychopathic features.
Methods: A computational model was applied to functional neuroimaging data to estimate
uncertainty in representations of contingencies (CoUn) and the rate of change of
contingencies (RUn), respectively, from brain activation during the acquisition phase of
threat conditioning in 132 adolescents at risk of developing antisocial personality profiles.
Next, the associations between these two types of representational uncertainty and
psychopathy-related dimensions were examined.
Results: The left and right amygdala activation were associated with CoUn, while the bilateral
insula and the right amygdala were associated with RUn. Different patterns of relationships
were found between psychopathic features and each type of uncertainty. Callous-
unemotional traits and impulsive-irresponsible traits uniquely predicted increased CoUn,
while only impulsive-irresponsible traits predicted increased RUn.
Conclusions: The findings suggest that i) the insula and amygdala differ in how these regions
are affected by different types of representational uncertainty during threat conditioning,
and ii) CoUn and RUn have different patterns of relationships with psychopathy-related
dimensions.
Keywords: threat conditioning; fear conditioning; uncertainty; representations; psychopathy;
amygdala; insula; computational modelling.
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Introduction
In a dynamic world, success relies heavily on our ability to adapt our behaviour to avoid
aversive outcomes. Threats have a large impact on the modulation of our behaviour (1). One
developmental condition that has repeatedly been linked to diminished learning from
aversive experiences is psychopathy (2, 3), which encompasses callous-unemotional traits
(e.g., blunted affect, lack of empathy or remorse), a grandiose-manipulative interpersonal
style (e.g., dishonesty, superficial charm, lying, manipulation of others) and impulsive-
irresponsible behavioural tendencies (e.g., thrill-seeking, lack of impulse control) (4).
Adolescents with high levels of psychopathic traits are at increased risk for engaging in
antisocial behaviour (5) and may be more difficult to treat because of their more severe
antisocial behaviour and diminished treatment responsivity (6). The maladaptive behaviour
seen in psychopathy is thought to be strongly influenced by disturbed learning from aversive
events, such as threats (i.e., threat conditioning)(7–9), which is reflected in abnormal
physiological and brain responses in both psychopathic adults (7–9) and youths with severe
antisocial personality profiles (10, 11).
Threat conditioning is multifaceted and learning relies on interacting cognitive
computations, similar to other forms of associative learning (12, 13). Learning which stimuli
are threatening requires accurate representations of threat contingencies. In order to
maintain accurate representations of contingencies we need to update the representations
continually after each observation, also taking into account that the learned contingencies
may change. However, our observations are imperfect (14). Therefore, there will always be
some uncertainty regarding the accuracy of the cognitive representations (i.e.,
‘representational uncertainty’)
1
that are generated based on these imperfect observations
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(15). Learning about contingencies involves generating representations based on our
estimates of the likelihood that contingencies will change and of the rate at which these
changes occur (13). As these representations are imperfect, there will be uncertainty about
the accuracy of the representation of contingency changes (i.e., contingency uncertainty;
CoUn) and that of the rate at which changes occur (i.e., change rate uncertainty; RUn (16)).
Empirical evidence indicates that these two types of representational uncertainties are
hierarchically related, as the rate at which changes in contingencies are perceived to occur
will influence our belief about the overall likelihood that the contingencies will change (13,
15). Furthermore, a recent study has shown that beliefs about each type of uncertainty play
key roles in modulating physiological responses to threats (17). Importantly, effective
learning requires cognitive uncertainty to be minimized (18). Therefore, it is likely that
uncertainty in the representation of contingencies may also play a role in understanding the
impairments in threat conditioning seen in psychopathy.
Threat conditioning in psychopathy has often been investigated in case-control
studies in which groups of individuals scoring high on psychopathic features are compared to
a low scoring group (e.g., (7, 11)). In one of the few threat conditioning studies employing a
dimensional approach to psychopathy, Cohn et al. (19) reported a positive relationship
between BOLD activation in the amygdala and insula (during acquisition learning) and
impulsive-irresponsible traits in at-risk adolescents, but a negative correlation between
callous-unemotional traits and BOLD activation in these regions. Other studies using
physiological measures have found a similar negative relationship between threat
conditioning and fearless dominance (a construct that overlaps with callous-unemotional
traits) but no correlation with impulsive-antisociality in undergraduates (20), and reduced
threat conditioning in adult psychopathic individuals scoring high on interpersonal-affective
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deficits (21). Taken together, the evidence points towards decreased threat conditioning in
individuals high on callous-unemotional traits, while the findings are mixed for the impulsive-
antisocial features. However, these prior studies have approached threat conditioning as a
unitary form of learning, without taking the multifaceted nature of associative learning into
account. As a consequence, how psychopathy may be related to any of the various
interacting cognitive computations that subserve acquisition learning during threat
conditioning has been overlooked. Systematically studying the integrity of these
computations is essential for pinpointing the deficiencies in the threat conditioning
mechanism in psychopathy.
In this study, we examined if the threat conditioning impairments seen during the
acquisition phase in relation to psychopathy may be partly attributed to increased
uncertainty in representations of contingencies and their rate of change. Importantly, as the
uncertainty computations are latent, it is impossible to directly quantify them without
employing computational modelling approaches. A computational model was applied to the
large fMRI dataset collected by Cohn et al. (19) to quantify uncertainty in the representations
of contingency change and the rate of change in target brain areas during threat
conditioning. The right and left amygdala and insula, respectively, were chosen as regions of
interest (ROIs) because these areas: i) consistently show activation during threat conditioning
across studies (22), ii) show relatively large responses to uncertainty about threat
contingencies (23, 24), iii) are linked to learning impairments in psychopathy (25), and iv) we
aimed to maintain comparability with the very few previous studies on psychopathy
dimensions and threat conditioning. Importantly, as two types of representational
uncertainties were quantified, it was unlikely that all ROIs showed an equal amount of CoUn
and Run, respectively. To take account of this, we subsequently used Bayesian structural
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equation modelling (BSEM) to examine which of the ROIs was more affected by each type of
representational uncertainty, and determined how psychopathy-related personality
dimensions uniquely predicted CoUn and RU.
Extant findings suggest that threat conditioning is reduced in individuals scoring high
on callous-unemotional features, and we hypothesized that this learning impairment should
be linked to increased representational uncertainty in these individuals. This is based on the
premise that disturbed learning should be related to a failure in reducing uncertainty in the
information processed in the brain [cf. 25]. But, given that we sought to obtain a higher level
of precision by parsing representational uncertainty into CoUn and RUn for the first time, it is
difficult to predict which type of uncertainty is related to the impairments seen in those with
elevated levels of callous-unemotional traits. The findings for the impulsive-irresponsible
dimension are mixed, but the hyper-conditioning found by Cohn et al. (19) suggest that
elevated impulsive-irresponsible traits should be linked to reduced representational
uncertainty during threat conditioning.
Methods and Materials
Participants and assessment
Participants were recruited from a Dutch cohort
of 364 adolescents who were first arrested
by the Dutch police before the age of 12, all of whom had participated in three previous
waves of a longitudinal study (26). The mean age at study entrance was 10.9 (SD=1.4) years.
All participants were assessed using the Dutch Youth Psychopathic traits Inventory (YPI)(27),
a valid and reliable 50-item self-report instrument developed to assess psychopathic traits in
juvenile community samples (28). In the current study, internal consistency of the total score
and its constituting dimensions were good to excellent: Cronbach’s alpha was .93 for the
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total score and .86, .89 and .85 for callous-unemotional, grandiose-manipulative and
impulsive-irresponsible scales, respectively.
For the current study, data was re-analysed from a subsample characterized by a wide
range of externalizing risk (N=150), with an even distribution of participants with low-risk,
medium-risk, and high-risk of antisocial behaviour. Eighteen participants were excluded from
analyses due to invalid MRI data (n=9); invalid task performance (n=6); drug use prior to
scanning (n=2); and missing questionnaire data (n=1). Analyses were performed on the
remaining 132 participants (mean age=17.7, SD=1.6; see Supplement)
Procedure
This study was approved by the Institutional Review Board of the VU University Medical
Center Amsterdam (VUmc). All participants and their parents/custodians (if the participant’s
age was below 18) signed for informed consent. Participants underwent a neuroimaging
protocol in a Philips 3T Intera MRI scanner at the VUmc. All participants were instructed to
refrain from using alcohol, cannabis or psychostimulant medication for at least 24 hours
before the MRI-scan.
Threat conditioning task
A classical differential delay threat conditioning task was employed (7). Pictures of two
neutral male faces served as conditioned stimuli (CS), one of which (chosen at random during
each experiment) was consistently paired with an aversive electric unconditioned stimulus
(US) at the end of a 10s viewing period (CS+; 100% reinforcement) during the acquisition
period, while the other picture (CS-) was never followed by a US. The acquisition period,
which consisted of 8 trials of each CS, was preceded by a habituation phase, in which CSs
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were presented 4 times each for 3.5s without a US and was followed by an extinction phase
in which CSs were presented 4 times each for 7s and were not followed by a US either.
fMRI protocol
T1-weighted anatomical scans (180 slices, 1mm
3
voxels, FOV 256x256mm, TR 9.0ms, TE
3.5ms), were acquired using an 8-channel SENSE head-coil. Furthermore, 400 T2*weighted
axial echo-planar images (EPI) were acquired during threat-conditioning (38 slices, 3mm
thickness, 2.29x2.29 in-plane resolution, FOV 220x220mm, TR 2300ms, TE 30ms).
Statistical analyses
Functional MRI data were processed using SPM8, including realignment, unwarping, slice-
time correction, normalization to MNI space based on the segmented anatomical scan, and
8mm FWHM smoothing. First level models included separate regressors for CS+/- and CS-
acquisition, US and rating blocks. During acquisition, the first five seconds of each trial were
modeled separately from the remainder of the trial (5s) to account for fast within-trial
habituation of threat neurocircuitry, focusing analyses on the first epoch only during
acquisition (19). Realignment parameters were also included in first-level models to account
for movement effects. Next, average neural response estimates for each ROI were extracted
using the MarsBaR toolbox for SPM (29). Following previous work (23, 30), we focused
analyses on the amygdala and insula. Amygdala and insula were anatomically defined using
the Automated Anatomic Labeling atlas (31).
Computational modeling
In order to model how activity in the ROIs responded to CS in the experiment, the activation
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trajectory of the principal eigenvariate was extracted for each ROI. This trajectory was then
analyzed using the HGF (15). The HGF is a generic hierarchical Bayesian time series model,
which explains a time series as a sequence of noisy observations of a hidden state. Crucially,
the HGF also models the dynamics of change (i.e., volatility) of the hidden state explicitly and
gives one-step update equations for the evolving estimate of both the hidden state and its
volatility. This evolving estimate is a probability distribution referred to by us as a belief. In
the HGF, beliefs follow a normal distribution and are fully specified by their mean and
variance. The mean of the belief represents the most probable value of the hidden state and
the variance represents the uncertainty.
More formally, the HGF consists of a hierarchy of Gaussian random walks where the
variance of each walk is a function of the value at the next higher level. Since the variance of
its walk determines a quantity’s likelihood of changing, each value but the one at the base of
the hierarchy represents the volatility of the next lower level. In the present study, we used a
two-level HGF where the first level represents the BOLD activation in a given brain region and
the second level is the volatility of that activation (Supplement, Fig.S1). We applied the HGF
to the measured BOLD activation (represented as ݑ in the HGF model graph of supplemental
Fig.S1) to infer the latent true activation at the time of measurement and its volatility along
with the posterior uncertainty about these quantities. This resulted in two estimated belief
trajectories for each subject and ROI: that of the BOLD activation (ݔ
ଵ
in the HGF hierarchy, see
Fig.S1) and that of its volatility (ݔ
ଶ
in the HGF hierarchy, Fig.S1). Each of the two belief
trajectories consisted of a mean trajectory (ߤ
ଵ
regarding ݔ
ଵ
and ߤ
ଶ
regarding ݔ
ଶ
) and a
variance (i.e., uncertainty) trajectory (ߪ
ଵ
and ߪ
ଶ
). Crucially, this means that every observed
update implies a certain level of uncertainty, and by observing the evolution of the BOLD
signal in the ROIs during threat conditioning, we can infer the evolution of the uncertainty
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the brain has about the quantity encoded by the activity in each ROI. Estimation was
performed using the HGF Toolbox v3.0 (https://www.tnu.ethz.ch/en/software/tapas.html)
and fits could be obtained for all subjects and ROIs. All HGF modeling was performed before
the BSEM described below and all of the hyper-parameters of the HGF analysis (number of
levels, noise level, regularizing priors) were chosen only in terms of this analysis.
Bayesian structural equation modeling
To assess which brain areas were substantially affected by representational uncertainty
during threat conditioning in a data-driven fashion we employed BSEM to determine i) which
ROIs were involved in coding CoUn and RUn and ii) which psychopathy-related dimensions
predicted each type of representational uncertainty. First, mean uncertainty estimates for
learning were created by averaging and subtracting the uncertainty trajectories of CS- trials
from that of the CS+ trials in each ROI, yielding 4 average scores representing the ‘additional
uncertainty’ found in the CS+ relative to the CS- condition for CoUn and Run, respectively. In
particular, the CoUn score was the mean of the uncertainty (i.e., variance ߪ
ଵ
) at the bottom
level of the HGF hierarchy during the CS+ condition minus the mean of the same uncertainty
ߪ
ଵ
during the CS- condition. Correspondingly, the RUn score was the mean of the uncertainty
(i.e., variance ߪ
ଶ
) at the second level of the HGF hierarchy during the CS+ condition minus
the mean of the same uncertainty ߪ
ଶ
during the CS- condition. Note that this subtraction
method is the common approach to obtaining BOLD responses reflecting conditioning, but
that we only used the uncertainty estimate derived from the BOLD signal instead of the raw
BOLD signal. Next, a BSEM was built in which the 4 average CoUn estimates were loaded on a
latent factor, which was regressed on the scores of the callous-unemotional, grandiose-
manipulative and impulsive-irresponsible subscales of the YPI. The same procedure was
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followed for the estimates of RUn.
The analyses were conducted in Mplus v7.4 (32) using a Bayesian estimator (PX1)
with five Markov chain Monte Carlo (MCMC) chains and 100.000 iterations. The first half of
the iterations was discarded (i.e., burn-in trials) and model fit was determined using different
indexes for Bayesian testing: i) a Chi-Square test for Posterior Predictive Checking and ii) the
Posterior Predictive P-value (PPP-value). Convergence of the MCMC chains was established
with Gelman-Rubin’s potential scale reduction (PSR) factor (33). In general, a good fit is
indicated by a 95% credibility interval (CI) for the Chi-square posterior predictive check that
includes the value 0, the PPP-value should approach the value 0.50 and convergence is
achieved when the PSR is below 1.05 (32). Significance of the regression weights was
determined based on the 95% CIs of the Bayesian posterior distributions and variables with
95% CIs not containing the value 0 were considered as significant.
Results
After creating the mean difference scores between the CS+ and CS- conditions for CoUn and
Run, respectively, the mean difference scores were used as outcome variables in the BSEMs.
The first BSEM included each subject’s mean CoUn estimates in the right and left amygdala
and insula, respectively, loaded on a latent factor that was in turn regressed on the YPI
subscales. This model had very good fit (95% CI -19.22−25.35, PPP=0.38), and indicated that
the left and right amygdala loaded significantly on the latent factor capturing CoUn and that
callous-unemotional (β=.45, 95% CI 0.21−0.72) and impulsive-irresponsible scales were
significant positive predictors (β=.27, 95% CI 0.05−0.51) of the latent factor (Figure 1). The
grandiose-manipulative scale was not a significant predictor (β=.10, 95% CI -0.15−0.35). This
procedure was repeated for change rate uncertainty in the 4 ROIs, loaded on a latent factor
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measuring RUn that was regressed on the 3 YPI scales. This model also had a very good fit
(95% CI -27.52−17.17, PPP=0.67). The results showed that the left and right insula and the
right amygdala loaded significantly on the latent factor and that the impulsive-irresponsible
scale predicted the latent variable for RUn (β=.29, 95% CI 0.07−0.49) (Figure 2). The callous-
unemotional (β=-.02, 95% CI -0.25−0.21) and grandiose-manipulative (β= -.12, 95% CI -
0.35−0.12) scales were not significant predictors.
Discussion
This study is the first to provide direct quantifications of different types of representational
uncertainty in the brain during threat conditioning in a large sample of adolescents at risk of
developing persistent antisocial behavior. The findings show that a significant level of CoUn
can be found in the left and right amygdala during threat conditioning, while the right
amygdala, left and right insula are more responsive to RUn. The mapping of these brain
responses onto different dimensions of psychopathy indicated that both callous-unemotional
and impulsive-irresponsible traits were uniquely related to CoUn, while only impulsive-
irresponsible features were positively linked to RUn (Figures 1-2).
Our results in an at-risk population are in line with those obtained in healthy samples
indicating that the amygdala (23, 34) and the insula (24) are involved in processing
uncertainty related to threat contingencies. Our results significantly advance this knowledge
by specifying how activation in the insula and amygdala is related to uncertainty about the
accuracy of different aspects of contingency representation. The amygdala seems to be
particularly sensitive to CoUn during threat conditioning, but also encodes a relatively high
amount of RUn together with the insula. Given the hierarchical relationship between RUn
and CoUn in our computational framework (13, 15), these results converge with the
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suggestion that the insula passes information concerning aversive stimuli to the amygdala
during threat conditioning (35). One tentative interpretation of the general pattern of results
is that the interaction between the insula and the amygdala during threat conditioning might
reflect a circuit in which the insula is primarily responsive to RUn and the information is then
relayed to the right amygdala, which would then function as a point of entry, to inform
computations taking place in the right and left amygdala that are related to the estimation of
the likelihood that contingences changes occur. This proposal would also be consistent with
the general notion that the insula relays somatosensory information to other areas to initiate
adaptive responses (36).
Regarding the link with psychopathy, callous-unemotional and impulsive-irresponsible
traits predicted increased CoUn in the amygdala. Because associative learning relies on
successful reduction of uncertainty (15), heightened levels of uncertainty in the cognitive
computations that are engaged should ultimately lead to reduced learning (18). In
agreement with this prediction, Cohn et al. (19) reported a negative relationship between
BOLD activation and callous-unemotional traits during threat conditioning in the dataset
used in the present study, pointing towards mechanistic disturbances in threat conditioning
(20, 21). Our findings further specify one aspect of the mechanism that seems impaired in
individuals with high levels of callous-unemotional traits, who seem to form more uncertain
(i.e., less accurate) representations of contingency changes in the amygdala, while
representations of change rate in the insula are relatively accurate.
Cohn et al. [18] also found a positive unique correlation between impulsive-
irresponsible traits and BOLD signal during threat conditioning. This positive relationship,
indicating enhanced learning from threats in those with increased levels of impulsive-
irresponsible traits, was expected to be related to reduced representational uncertainty (i.e.,
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more accurate representations) in the present study. Instead, we found that impulsive-
irresponsible traits were positively linked to increased RUn and CoUn in contingency
representations. Given these results, it seems plausible that there is a broader deficiency in
forming representations concerning change in individuals scoring high on impulsive-
irresponsible traits during threat conditioning, while the deficiency is limited to CoUn in
those scoring high on callous-unemotional traits. However, representational uncertainty does
not seem to be sufficient for explaining the enhanced threat conditioning seen with elevated
impulsive-irresponsible traits (19). Therefore, it should be considered whether an additional
mechanism could be interacting with the threat conditioning processes in this sample of at-
risk adolescents. One possibility is that the insula and amygdala are hyper-sensitive to
aversive information in impulsive-irresponsible individuals. This interpretation builds on the
previous finding that higher perceived uncertainty sensitizes the insula and amygdala to
aversive information (23, 24, 34), presumably leading to exaggerated aversive responding in
these regions. Thus, more representational uncertainty during aversive learning may be
interacting with a bias toward exaggerated affective responding in the amygdala in
individuals with high levels of irresponsible-impulsive features, while this bias may not be
present in individuals with elevated callous-unemotional traits. One tentative prediction that
follows is that impulsive-irresponsible individuals should also show exaggerated responses
during extinction learning, as the hyper-sensitization of the amygdala after threat
conditioning combined with excessive representational uncertainty should interfere with the
unlearning of contingencies during extinction. Obviously, this proposal is made with care as
we did not quantify amygdala bias in the present study and also because threat extinction
has yet to be studied using a dimensional approach to psychopathy instead of group
comparisons [11,37].
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Interestingly, our findings also suggest that representational uncertainty could play an
important role in explaining other learning impairments found in antisocial populations, such
as disturbed passive avoidance (38, 39) and reversal learning (40, 41). During reversal
learning, for example, we learn that a stimulus previously associated with reward now may
lead to negative outcomes, which requires us adapt our beliefs and behaviour to avoid
negative consequences. Importantly, the change in stimulus-outcome associations introduces
representational uncertainty about the contingencies and their rate of change, which affects
how well and how fast we learn the new contingencies. From this perspective, the reversal
learning impairment found in children (41) and adults (3, 40, 42) with psychopathic
tendencies could reflect sub-optimal management of representational uncertainty, similar to
what we found in the present study. In line with this prediction, Budhani and Blair (41) found
that the reversal learning impairment in boys with psychopathic tendencies got worse as the
saliency of the contingency changes decreased. Such a reduction of saliency increases
ambiguity and uncertainty about the contingencies, so their results suggest that increased
CoUn may play a significant role in the reversal impairment. Future studies should try to
confirm this expectation and determine the impact of representational uncertainty on
reversal learning.
One critique to the current study could be that our task did not include a
manipulation of uncertainty, as the aversive stimulus was always associated with the same
neutral face during learning, which would make it clear when to expect the aversive stimulus.
This argument stems from the issue that quantifying the impact of uncertainty on threat
conditioning is not possible using traditional analytical approaches, thus requiring the
manipulation of uncertainty through the experimental task in blocked designs. However, the
computational model used in the present study overcomes this limitation, as it directly
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quantifies the level of uncertainty about contingencies on a trial-by-trial basis. Therefore, the
impact of uncertainty on the cognitive operations involved in learning can be measured
without introducing drastic changes in contingencies through task design. Another potential
issue is the possibility that the increased RUn in the insula found in those with elevated
impulsive-irresponsible tendencies could be driven by a more general impairment in the
integration of multiple sources of information used to generate contingency representations
during threat conditioning (43, 44). The insula can be seen as a processing hub implicated in
various cognitive operations that include representations of pain, contextual appraisal and
general uncertainty (36). Thus, it is possible that increased RUn is a consequence of
inaccurate lower level representations in the insula, such as those pertaining to the pain
stimulus. Such an account would also be in line with studies on reinforcement-based
decision-making in adolescents with conduct problems, who show impairments in generating
accurate representations of expected value in the insula (39, 45). Together, these caveats and
novel hypotheses highlight the need for further studies that focus on the interaction
between personality dimensions, representational uncertainty, and their neural correlates
during threat conditioning and other forms of associative learning.
In conclusion, the present study is the first to directly quantify different kinds of
representational uncertainty during threat conditioning in an at-risk sample of adolescents.
The results highlight the importance of examining how uncertainty in cognitive
representations may be key to understanding some of the maladaptive characteristics often
linked to psychopathy. A more precise understanding of the various interacting cognitive
computations involved in maladaptive learning may lead to better, (neuro)biology-oriented
diagnostics and the development of targeted treatment approaches in various conditions
showing disturbed associative learning (46, 47).
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Acknowledgements
MDC, AP and IAB were supported by a Mosaic (017.007.022), Brain and Cognition
(056.23.010) and VENI Grant (451.15.014), respectively, from Netherlands Organization for
Scientific Research. The authors thank Professor Essi Viding for her valuable input.
Financial Disclosures
All authors report no biomedical financial interests or potential conflicts of interest.
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Footnote
1
Note that the operationalization of uncertainty used in this study differs from other
definitions of the term. Neuroimaging studies have primarily focused on how uncertainty
due to unsure outcomes is processed in the brain (e.g., (48, 49)), while we examined the
uncertainty about the accuracy of cognitive representations of change. In other words, our
definition of uncertainty in this study refers to the inaccuracy in representations that are
formed, which is driven by imperfect observations in addition to uncertain outcomes.
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Figures
Figure 1. Structural Equation model depicting the relationships between the YPI scales
measuring callous-unemotional (YPI-CU), grandiose-manipulative (YPI-GM), impulsive-
irresponsible (YPI-II) traits and a latent factor representing the amount of perceptual
uncertainty concerning contingencies (CoUn), as estimated from the BOLD signal trajectories.
Only the left amygdala (L-Amy) and the right amygdala (R-Amy) loaded significantly on this
latent factor. To increase readability, non-significant loadings are not depicted. Solid arrows
represent significant unique correlations, dashed arrows represent non-significant effects,
and dotted arrows represent factor loadings.
Figure 2. Structural Equation model depicting the relationships between the YPI scales
measuring callous-unemotional (YPI-CU), grandiose-manipulative (YPI-GM), impulsive-
irresponsible (YPI-II) traits and a latent factor representing perceptual uncertainty concerning
change rate of contingencies (RUn), as estimated from the BOLD signal trajectories. The left
insula (L-Ins), right insula (R-Ins) and right amygdala (R-Amy) loaded significantly on this
latent factor. To increase readability, non-significant loadings are not depicted. Solid arrows
represent significant unique correlations, dashed arrows represent non-significant effects,
and dotted arrows represent factor loadings.
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Brazil et al. Supplement
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Representational Uncertainty in the Brain During Threat Conditioning
and the Link With Psychopathic Traits
Supplemental Information
Additional information about subjects (1)
The 132 subjects (86% male) consisted of individuals from 3 groups, with an even distribution of
subjects with low-risk, medium-risk, and high-risk of antisocial behavior. These subsamples
were selected in order to increase the likelihood that the scanning sample would reflect the full
spectrum of behavioral functioning. There was a i) low-risk group without a diagnosis of
Disruptive Behavior Disorder (DBD), Oppositional Defiant Disorder or Conduct Disorder in the
three previous waves according to the DISC-IV and below median scores on aggression (RPQ)
and callous-unemotional traits (YPI) in the 3 previous waves (n=34 out of the original sub-
sample of 110); ii) a medium-risk group with above-median scores on aggression and callous-
unemotional traits in the previous waves but no previous diagnosis of DBD (n=52 out of 174);
and, iii) a high-risk group with a previous DBD diagnosis (n=46 out of 80).
Computational model
The nature of each trajectory was determined by five estimated parameters (see Table S1
below). Those were the initial variances of the two trajectories, their volatilities, and the initial
value of the activation trajectory. Since the measurement scale of volatility was arbitrary in our
case, the initial value of the volatility trajectory could be set to 1 without loss of generality (for
details see Appendix F in the publication by (2)).
Estimation of the trajectories took place by finding the maximum-a-posteriori (MAP)
value of the HGF parameters that govern them using the Broyden-Fletcher-Goldfarb-Shanno
(BFGS) algorithm. MAP estimation entails maximizing the log-likelihood under regularization by
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appropriate priors (see Table S1). Parameters were estimated in logarithmic space if they had a
lower bound such that Gaussian priors could be chosen in all cases.
Table S1. Initial values of the estimated parameters.
Parameter Estimation Space Mean Variance
Initial value of BOLD
activation trajectory Native 0 log(3)=1.1
Initial variance of BOLD
activation trajectory Logarithmic 1 1
Initial variance of phasic
volatility trajectory Logarithmic 0.1 1
Tonic volatility of BOLD
activation Native -1 1
Meta-volatility Native -4 16
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Figure S1. Graphical depiction of the HGF model used. 𝑘: observation index. 𝑢: observed
BOLD signal. 𝑥1: true hidden neuronal activation level. 𝑥2: volatility of activation level. 𝜔1: tonic
volatility of 𝑥1. 𝜔2: tonic volatility of 𝑥2. Observation noise: 𝛼=log 2. Hexagons represent
quantities that depend on their own previous state, diamonds represent quantities that do not
depend on their own previous state, and circles represent constants.
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Supplemental References
1. Cohn MD, et al. (2013) Fear conditioning, persistence of disruptive behavior and
psychopathic traits: an fMRI study. Transl Psychiatry 3:e319.
2. Mathys CD, et al. (2014) Uncertainty in perception and the Hierarchical Gaussian Filter.
Frontiers in Human Neuroscience 8:825.
