
==== Front
Transl Psychiatry
Transl Psychiatry
Translational Psychiatry
2158-3188
Nature Publishing Group UK London

39266521
3042
10.1038/s41398-024-03042-3
Article
Lower confidence and increased error sensitivity in OCD patients while learning under volatility
Hoven Monja 1
Mulder Tosca 1
http://orcid.org/0000-0002-3191-3844
Denys Damiaan 1
van Holst Ruth J. 12
http://orcid.org/0000-0002-9395-9426
Luigjes Judy j.luigjes@amsterdamumc.nl

1
1 grid.12380.38 0000 0004 1754 9227 Amsterdam UMC location University of Amsterdam, Department of Psychiatry, Amsterdam, The Netherlands
2 https://ror.org/04dkp9463 grid.7177.6 0000 0000 8499 2262 Centre for Urban Mental Health, University of Amsterdam, Amsterdam, The Netherlands
12 9 2024
12 9 2024
2024
14 3706 10 2023
16 7 2024
26 7 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
A decoupling between confidence and action could relate to compulsive behaviour as seen in obsessive-compulsive disorder (OCD). The link between confidence and action in OCD has been investigated in clinical case-control studies and in the general population with discrepant findings. The generalizability of findings from highly-compulsive general population samples to clinical OCD samples has been questioned. Here, we investigate action-confidence coupling for 38 OCD patients compared to 37 healthy controls (HC), using a predictive inference task. We compared those results to a comparison between matched high and low compulsive individuals from the general population. Action-updating, confidence and their coupling were compared between the groups. Moreover, computational modeling was performed to compare groups on error sensitivity and environmental parameters. OCD patients showed lower confidence and higher learning rates in reaction to (small) prediction errors than HC, signaling hyperactive error signaling and lower confidence estimation. No evidence was found for differences in action-confidence coupling between groups. In contrast high the compulsive group showed higher confidence and stronger decoupling than the low compulsive group, both of which were related to symptoms. The underlying mechanisms of obsessive-compulsive behaviour might differ between clinical and highly-compulsive general population samples, resulting in different (meta)cognitive profiles.

Subject terms

Human behaviour
Learning and memory
Psychiatric disorders
Diseases
https://doi.org/10.13039/501100001826 ZonMw (Netherlands Organisation for Health Research and Development) 916-18-119 Luigjes Judy NWO ZonMw Veni Grant 916-18-119issue-copyright-statement© Springer Nature Limited 2024
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pmcIntroduction

Throughout daily life we perform actions based on our beliefs and our sense of confidence in those beliefs. For example, if I am confident that I have brought my passport to the airport, I am less likely to engage in actions to double-check it. Following the Bayesian framework of belief updating [1–3], in addition to new information, confidence in prior beliefs also plays a role in determining the extent to which a belief is updated. The more confident one is in their belief, the less impactful new information is, leading to minimal updates of their existing beliefs. When confidence is low, however, it can motivate the gathering of additional evidence to update those beliefs [4–6]. In this way, the confidence with which a belief is held shapes our future behavior, particularly in volatile and uncertain environments.

This process of utilizing new information and confidence to guide decision-making can go awry in various psychiatric disorders, including obsessive-compulsive disorder (OCD). OCD is a psychiatric disorder that is typically characterized by intrusive obsessions and compulsions [7]. Moreover, OCD has been associated with abnormalities in confidence (i.e., the subjective feeling of being correct in a choice, belief or decision), with most studies showing lower confidence in patients compared with healthy controls [8]. A tendency toward lower confidence estimation could lead to doubts and uncertainty that drive compulsive behavior in OCD.

However, a study that investigated the interaction between confidence and behavior in OCD patients during a volatile learning process [9] did not find differentiating confidence patterns in OCD patients. Their results showed a decreased correlation between confidence and behavior in OCD patients, which correlated with their symptoms. This led to an alternative hypothesis that compulsive behavior may be fueled by this decoupling between behavior and confidence, even though confidence judgement remains intact. Based on this finding and in line with the view that OCD behavior is a disruption of goal-directed action [10–12], it was suggested that compulsions relate to inaccurate use of confidence judgements to inform behavior [9]. This was tested with a task, wherein participants had to predict, based on previous trials, where a particle would land by placing a bucket to catch it (i.e., predictive inference). The landing location of the particle was closely distributed around a central point which would change unexpectedly. After their prediction, the participants assessed their confidence in successfully catching the particle on a scale of 1–100 (see methods for more detailed description).

The study found that the decoupling between behavior (i.e. bucket movement or “action”) and confidence was primarily driven by the tendency of OCD patients to move their bucket in response to small prediction errors (i.e., distance between the predicted and actual landing location). These adjustments to small errors are excessive, since they do not warrant any adjustment to catch the particle, indicating higher error sensitivity in OCD patients. Together these results resemble the clinical presentation of OCD where patients often continue performing actions that they know are disproportionate, and which are by definition ego-dystonic (i.e., not consistent with the persons’ beliefs) [13]. Using the same paradigm, Marzuki and colleagues [14] also found excessive action for small prediction errors in adolescent OCD patients, but did not observe decoupling between action and confidence.

In addition to clinical samples, OCD is often studied using analogue samples from the general population to assess relationships between (meta)cognitive phenomena and obsessive-compulsive (OC) symptoms [15]. Seow & Gillan [16] conducted a study with the same predictive inference task as Vaghi et al. [9] using a large general population sample, and found that OC symptom severity was positively related to decoupling between action and confidence. However, unlike the results of the study by Vaghi et al. [9], here the authors reported that subjects with high OC symptom severity (i.e., high compulsive individuals) showed increased confidence rather than action. The decoupling between action and confidence was not specific to OC symptoms, however, and was also found for other psychiatric symptoms (e.g., depression, anxiety, psychosis). When using a transdiagnostic approach to consider co-morbid symptoms across psychiatric disorders, it was discovered that a symptom dimension of compulsivity specifically contributed to the action-confidence decoupling through inflated confidence. A recent replication study, however, failed to replicate the associations between OC symptoms and both confidence or the coupling between action and confidence [17].

In general population studies it is often assumed that the (meta)cognitive abilities of highly compulsive individuals resemble those of patients with OCD, albeit to a lesser degree. However, in a recent study we challenged this assumption and showed distinct metacognitive patterns in highly compulsive individuals and patients with OCD (with similar OC symptom severity) such as underconfidence in OCD patients versus overconfidence in high compulsive individuals, suggesting that these groups are inherently different [18]. This may explain increased confidence in high compulsive individuals in the translational study [16] and the contrasting finding from Vaghi et al. [9] that the deviation in confidence and not action were driving the decoupling between the two.

The primary aim of this study is to address the discrepancies between previous studies by investigating action, confidence, and their coupling in a group of OCD patients who were not taking medication, and did not have any co-morbid diagnoses. We compared this group to healthy controls using the same predictive inference paradigm as previous studies. Moreover, we compared our subjects’ behavior to a reduced Bayesian model used in previous studies, allowing us to compare how patients and healthy controls respond differently to various sources of environmental information (i.e. recent outcomes, surprising outcomes, uncertainty, and feedback) in updating their actions and confidence. Second, to better understand how OCD deviations in action and confidence relate to findings in general population studies with high compulsive individuals, we will conduct a group comparison from the discussed translational study [16] where we match a high and a low compulsive group to our two groups based on demographics and OCD symptoms. This allows us to explore the specificity and generalizability of our results to a translational approach. We found evidence for lower confidence and higher error sensitivity in OCD patients, but no group differences in coupling between action and confidence compared to healthy controls. Highly compulsive individuals from the general population in contrast showed increased confidence and decoupling between action and confidence compared to low compulsive individuals, both confidence and decoupling were related to symptom severity. Overall, low confidence and error sensitivity characterize OCD in the interaction between action and confidence, which may not generalize to high compulsive samples from the general population.

Methods

Participants

This study included patients with OCD and healthy controls (HCs). An a-priori power analysis was not performed, as our sample size was based on an earlier clinical study in OCD using the same paradigm [9]. The study was approved by the Medical Ethics Committee of the Amsterdam University Medical Centre, and all subjects provided written informed consent before participating and were reimbursed for their time. The study was in accordance with the Declaration of Helsinki.

Patients with OCD

Recruitment through the psychiatry department of the Amsterdam University Medical Centre and OCD community websites resulted in the inclusion of 43 OCD patients between 18 and 65 years in the study whose diagnosis of OCD was confirmed using structured psychiatric interviews. Exclusion criteria included a current diagnosis of major depressive disorder, (hypo)mania, anxiety disorders, substance use disorders or psychotic disorders, and use of medication for the treatment of psychiatric symptoms during the time of inclusion.

Healthy controls

45 HCs were included and recruited through online advertisements and matched to OCD patients in terms of age, gender, and education.

High and low compulsive individuals from the general population

Data from high and low compulsive individuals from the general population that was used for comparison to our results was obtained from a previous study by Seow & Gillan [16]. This study collected data from a large general population sample (N = 589) through Amazon’s Mechanical Turk, with 427 participants remaining after applying exclusion criteria, using similar task-based exclusion criteria as the current study (see ‘Subject task-based exclusions’). These HComp and LComp participants completed the exact same online task as the OCD and HC groups, but with 150 trials per participant rather than 300.

We used propensity score matching to select participants from the sample to match the patient and healthy control samples in terms of OC symptom severity. We used the MatchIt package in R [19] to perform optimal pair matching per group to minimize the sum of the absolute pairwise distances in the matched sample. To balance the number of trials completed, we used a 1:2 ratio of our samples to the general population participants in the matching process, resulting in a similar number of data points for both groups. Matching was performed based on the Obsessive-Compulsive Inventory-Revised score (OCI-R [20]), age, and sex. Demographics were compared between groups using two-sample t-tests for continuous measures and Chi-square tests for categorical measures. Our final sample, after task-based exclusions (see ‘Subject task-based exclusions’ for more information) consisted of 38 patients with OCD, 37 HCs and 76 high and 73 low compulsive general populationparticipants.

Questionnaires

HC and OCD participants were assessed using the MINI structured psychiatric interview to screen for additional psychiatric disorders [21]. OC-symptoms were measured using the Obsessive-Compulsive Inventory - Revised (OCI-R) in all participants [20], and symptom severity was additionally assessed in patients with OCD using the Yale-Brown Obsessive-Compulsive Scale (Y-BOCS). Patients with a YBOCS score < 12 were not included in the study, which has been found to be the most sensitive cut-off for predicting remission Anxiety and depression symptoms were assessed using the Depression Anxiety and Stress Scale (DASS) [22] in OCD and HC and the STAI [23] and Zung’s self-rating depression scale [24] in HComp and LComp participants.

Predictive inference task

We used the web-based version of the predictive inference task, originally described by Nassar et al., [25], and modified by Seow & Gillan, [16]. The task involved error-driven learning, in which participants had to infer the landing location of a particle based on its previous landing locations. To do this, participants were shown a circle with a center dot and asked to place a “bucket” (represented by a curved rectangle) at the predicted landing location of the particle, which they could update after each trial. The placement action – deciding where to position the bucket based on the prediction of the particle’s landing – constitutes ‘action’. Action therefore confluences perceptual judgement (prediction of the landing location), and physical action. Action is operationalized as the absolute difference in bucket position from one trial to the next. After confirming their bucket placement, participants rated their confidence in the accuracy of the bucket placement on a scale of 1 (not at all confident) to 100 (extremely confident). This confidence rating about the bucket placement therefore reflects participants’ judgement about the adequacy of their action in relation to the task demands. Confidence ratings therefore reflect both trust in their perceptual accuracy as in their executed action. The confidence scale was randomly initiated at a rating of either 25 or 75, to stimulate participants usage of scale (Fig. 1).Fig. 1 Predictive inference task.

A Trial sequence of the task. Participants had to position their bucket (i.e. yellow bar on the edge of the circle) to catch a flying particle that was released from the center dot to the edge of the circle. After positioning their bucket, participants indicated their confidence in catching the particle. The particle was either caught (bar turned green) or missed (bar turned red), which resulted in gaining or losing points, respectively. B In every trial, the particle landing position was sampled from a random Gaussian distribution. The trajectories were always straight lines from the center to the landing position. Particles thus landed close together with a small amount of noise. Current trial particle trajectory is marked in black, previous trial particle trajectories are marked in blue. Over time participants thus learn about the Gaussian distribution from which the particle trajectories are drawn. C During a change-point the mean of the Gaussian distribution abruptly changes to another point in the circle. The particle landing locations are then sampled in a similar manner, until a new change-point occurs. Figure was adapted with permission from Seow et al. [16].

After participants decide where to place the bucket based on their perceptual judgment of the particle’s landing location, they execute an action by moving the bucket to that location. This process represents a confluence of perceptual judgment and physical action, with each trial’s decision encompassing both elements. The subsequent confidence rating not only reflects the participants’ trust in their perceptual accuracy but also in their executed action. This approach allows us to explore the intricate relationship between perceptual judgments, action-based decision-making, and confidence, emphasizing that these components are inherently interlinked within the context of our study.

After confirming the confidence rating, a particle was fired from the center dot. The landing location of the particle was sampled from a Gaussian distribution with a fixed standard deviation (SD) of 12. At certain trials, known as change-points (CP), a new mean was drawn from a uniform distribution over the full range of the circle U(1,360), with a probability of 0.125 (hazard rate, H). Optimal task performance thus required participants to distinguish between signal (change-point) and noise (SD of the generative distribution). Participants were rewarded for correctly catching the particle in their bucket and penalized for missing it via point summations and subtractions, respectively.

The task consisted of 4 blocks of 75 trials, with a practice round that was not included in the final score and not analyzed. Participants were given a quiz after practicing that they had to answer correctly before the task started to ensure they understood the task instructions. Participants were instructed to earn as many points as possible, which would be converted to monetary rewards and could be up to €5. In the instruction phase participants were told that the landing location of the particle would sometimes change. Confidence ratings were not directly incentivized, but participants were instructed to rate their confidence as accurately as possible. If participants had left their confidence rating as the default for more than 70% of the trials at the 20th and 50th trial mark, participants were reverted back to the instructions.

All participants performed the same task, which was coded in JavaScript and hosted on Gorilla for the OCD and HC group [26], and on Amazon’s Mechanical Turk for the HComp and LComp groups. The HComp and LComp groups completed 150 trials per participant, while the OCD and HC groups completed 300 trials per participant.

Task-based exclusions

We preregistered task-based exclusions (https://osf.io/zury3), based on criteria set by Seow & Gillan [16]. Specifically, participants were excluded if they left their confidence rating at the default score on >60% of trials (n = 3; 1 OCD), if their mean confidence after hits was lower than their mean confidence after misses suggesting disengagement or misunderstanding of the task (n = 7, 3 OCD), or if the correlation between confidence rating and the default confidence was >0.5 (n = 7, 2 OCD) to ensure subjects sufficiently used the confidence scale. After applying these criteria, the final dataset included data from 75 participants (38 OCD, 37 HC; 26 females in each group). The same exclusion criteria were applied to the dataset from Seow & Gillan, [16]. In addition to subject-based exclusions, we also performed trial-based exclusions (see section ‘Computational Model’).

Analyses

All data preparation and analyses were conducted using MATLAB (version 2018b) and R (version 4.2.1). We compared the OCD and HC groups, as well as the HComp and LComp groups separately. Our analysis plan for the clinical case-control sample was pre-registered (https://osf.io/zury3), and we applied the same task-parameter analyses (but not model-based analyses) to the comparison between HComp and LComp groups. Additional control analyses can be found in the supplementary materials.

Action and confidence

Our first aim was to compare action and confidence between groups. We used two linear-mixed effects models using the package lme4 [27], with either action (absolute difference in bucket position from trial (t) to trial (t + 1)) or confidence as dependent variable and group as predictor, including random intercepts. Given the focus on group effects and to maintain model parsimony, random slopes were not included in these models.

Action-confidence coupling

Our second aim was to assess differences in the strength of action-confidence coupling (i.e., direct association between changes in confidence and action across trials) between groups. We constructed a linear mixed-effects model with trial-by-trial action as the dependent variable and trial-by-trial confidence (z-scored), group and their interaction as predictors. Random intercepts and slopes of the effect of confidence were added.

In addition, we conducted a Pearson’s correlation to examine the relationship between the strength of action-confidence coupling and OCI-R scores in the OCD group, using subject-level β coefficients of the action-confidence coupling.

Learning rate and prediction error

Large prediction errors signal a radical change in the environment, requiring strong belief updating with higher learning rates. Learning rates represent the extent to which individuals use new information (actual landing location vs. predicted landing location) to update their future action. While small prediction errors likely are noise and do not require belief updating, in which case learning rates are low.

Participants’ prediction error δˆt (PE) for each trial was calculated as the difference between the current bucket position bt and the particle landing location Xt.δ^t=Xt−bt

Participants’ learning rate αˆt (LR) was then calculated as the fraction of PE used for the subsequent action, which was calculated as the absolute difference in bucket position from trial (t) to trial (t + 1):α^t=∣bt+1−bt∣δ^t

Trials were excluded from all analyses (both model-free and model-based) if the LR exceeded the 95th percentile (4.9% (n = 553) of OCD trials, 5% (n = 555) of HC trials, 4.9% (n = 562) of HComp trials, 5% (n = 548) of LComp trials, which was calculated separately for each group [14]. Due to motor noise in bucket movement when using the keyboard, trials with very small prediction errors ( <5) are sensitive to measurement error [28]. In this way, trials with small PEs often resulted in very large LRs, even if action was minimal. Several trials in our sample revealed to have extremely high learning rates due to motor noise, we applied a more stringent exclusion threshold than we reported in our pre-registration, similar to the one used in the paper by Marzuki et al. [14]. In addition, trials with PE = 0 were excluded, since these trials do not drive error-driven learning (1.96% of OCD (n = 222) trials, 2.06% (n = 229) of HC trials, 1.99% (n = 227) of HComp trials, 2.04% (n = 223) of LComp trials. Additionally, the first and last trials within each block were excluded from analyses; in the first trials, there is no error-driven learning yet, and for the last trials no learning rate could be calculated. Finally, due to technical server failure, some trials were not properly recorded and therefore not analyzed (53 OCD trials, 2 HC trials), along with the trials following those corrupted trials, since action could not be calculated in these cases. In total, 8.98% (n = 1020) of OCD trials, 9.12% (n = 1012) of HC trials, 9.10% (n = 1037) of HComp and 9.36% (n = 1025) of LComp were excluded from all analyses.

Error sensitivity

To assess group differences in error sensitivity for learning, linear mixed models were constructed with participants’ LR as the dependent variable and participants’ PE, group and their interaction as predictors. We did not include random slopes due to convergence issues encountered. For visualization of the relationship, values of PE were binned into 20 quantiles that each contained an equal fraction of trials. For each quantile, the average LR was computed per subject.

Computational model analyses

A computational modeling approach was used to examine whether and how the relationship between behavior on the task (i.e., action or confidence) and various environmental parameters derived from a quasi-optimal Bayesian observer model that approximates optimal task behavior differed between groups

This model-based approach was also used in previous research [9, 14, 16]. Using the same model (publicly available from [9]) we fitted the particle landing locations of individual subjects to obtain model parameters for the OCD and HC groups.

The model parameters represent statistical characteristics of the environment experienced by participants during the task. In short, these statistical features included the prediction error δ (PE, the absolute difference between model belief and location of the particle), the probability that a change-point occurred (CPP, the likelihood that the sampling distribution of the particle’s location has changed, thus that a change-point has occurred), and relative uncertainty (RU, the fraction of uncertainty about the mean that is not due to noise). RU was expressed as its inverse, named model confidence (MC, related to the precision of the model’s own beliefs about the mean), to allow for a comparison with confidence reported in the task [9]. For more information on the model see supplementary materials.

We assessed how these different Bayesian parameters related to participant behavior, and whether this differed between the groups. Following previous studies, participant behavior (either action or confidence) was regressed against three latent variables computed by the Bayesian model: absolute PE, CPP and (1-CPP)(1-MC), and the categorical variable hits, indicating whether the particle was caught. While PE represents uncertainty regarding the most recent observation, CPP and (1-CPP)(1-MC) represent the model’s estimation that a change-point did or did not occur, given the sequence of past observations, respectively. The dependent variable action was calculated as: LR * PE, indicating the bucket update. The predictors in the action model were also interacted with PE for the regression on action [9, 16, 28, 29]. Mixed models were constructed with either action or confidence as the dependent variable, and the three model parameters and Hit as fixed-effect predictors (all z-scored), which were all interacted with group. Random intercepts and slopes of all predictors were also included in the model.

In addition, we also performed sensitivity analyses where we calculated the best fitting hazard rate parameter for each subject based on the fit of the model on the participant’s behavior. The fitted hazard rate indicates how often participants expect change points (i.e., their perception of the task’s volatility). With these sensitivity analyses we account for the individual differences in these expectations in our primary findings since hazard rates vary across subjects [25] (see Supplementary Material).

Results

Demographics

There were no differences in age (t74 = 1.02, p = 0.31) or gender (X2 = 0.03, p = 0.86) distribution between HC and OCD groups, or between HComp and LComp groups in age (t147 = −1.57, p = 0.12) or gender (X2 = 0.49, p = 0.48). OCD patients had significantly higher OCI-R scores than HCs (t40.3 = −12.19, p < 0.001), and Hcomp higher OCI scores than LComp (t79.5 = 12.86, p < 0.001). For details on demographics and clinical data, see Table 1.Table 1 Demographics, clinical and task-based variables.

	OCD	HC	HComp	LComp	OCD vs. HC	LComp vs HComp	
Age	36.3 (10.9)	38.9 (10.9)	36.8 (11.1)	39.6 (10.7)	t73 = 1.02

p = 0.31

	t147 = −1.57

p = 0.12

	
Females (%)	26 (68.4%)	26 (70.3%)	49 (64.4%)	51 (69.9%)	X2 = 0.03p = 0.86	X2 = 0.49 p = 0.48	
Years of education	3.9 (0.9)	3.8 (0.8)			t73 = −0.72

p = 0.47

		
OCI-R	24.0 (10.6)	2.6 (2.2)	24.8 (14.7)	2.6(2.5)	t40.3 = −12.19

p < 0.001

	T79.5 = 12.68

p < 0.001

	
OCD Obsessive-Compulsive Disorder, HC Healthy Controls, HComp High-Compulsive subjects, OCI-R Obsessive-Compulsive Inventory-Revised. Data are reported as mean (standard deviation). Welch’s t-tests were used to compare OCI-R scores between OCD and HC groups, since variances were not equal.

Comparing OCD patients to healthy control subjects

Model-free results

Lower confidence in OCD but no differences in action

To investigate whether our groups differed in terms of task behavior, we conducted mixed-model analyses investigating group difference on action and confidence. First the analyses on confidence showed that patients with OCD had significantly lower confidence than HCs (β = −18.9 (4.9), t = −3.83, p < 0.001) (Fig. 2A). Second, no group differences were found in the amount of action between groups (β = 0.8 (1.5), t = 0.56, p = 0.579) (Fig. 5A).Fig. 2 Confidence across groups.

A Mean confidence per group for both comparisons: OCD showed lower confidence compared to HC, while HComp showed higher confidence compared to LComp. Dots show data from individual participants, boxplots show median and upper/lower quantile with whiskers indicating the 1.5 interquartile range, distributions show the probability density function of all data points per group. Significance starts represent the main effects of group in the respective mixed-effects models. *p < 0.05, ***p < 0.001. B The relationship between symptom severity as measured by the OCI-R and mean confidence in OCD (grey) and HComp (blue) groups. The shaded areas represent the 95% confidence intervals. A correlation was found for HComp subjects (r = 0.52, p < 0.001), but not for OCD patients. HC healthy control subjects, OCD obsessive-compulsive disorder patients, HComp= high compulsive subjects from the general population, LComp low compulsive subjects from general population, OCI-R obsessive-compulsive inventory revised.

To rule out the possibility that the decrease in confidence in OCD was due to comorbid anxiety and depression symptoms, we conducted a similar mixed-model analysis while controlling for DASS scores. The effect of group on confidence remained significant (β = −27.1 (7.8), t = −3.48, p < 0.001), while there was no effect of DASS score (β = 0.30 (0.22) t = 1.36, p = 0.179). This suggests that the lower confidence in OCD compared to HCs was not explained by comorbid anxiety and depression symptoms.

No differences in action-confidence coupling

Next, we evaluated whether the coupling between action and confidence differed between the groups. As expected, a significant negative relationship between confidence and action existed across groups, such that higher confidence was related to less action (i.e. action-confidence coupling) (β = −9.06 (0.85), t = −10.66, p < 0.001). However, there was no evidence for a distortion of this action-confidence coupling in OCD, as no interaction between group and confidence on action was found (β = 1.06 (1.19), t = 0.89, p = 0.379) (Fig. 3A). The same results were found when using confidence update from trial t-1 to t as a predictor.Fig. 3 Action-confidence coupling across groups.

A Results from a regression model where action was predicted by confidence for both comparisons: No significant difference was found between OCD and HC, while HComp showed more decoupling compared to LComp. As expected, across all groups, regression coefficients were negative indicating that higher confidence was associated with smaller action of the bucket location. Dots represent regression coefficients of individual subjects, boxplots show median and upper/lower quantile with whiskers indicating the 1.5 interquartile range, distributions show the probability density function of all data points per group. Significance stars represent the main effects of group in the respective mixed-effects models. *p < 0.05. B The relationship between symptom severity as measured by the OCI-R and action-confidence coupling in OCD (grey) and HComp (blue) groups. The shaded areas represent the 95% confidence intervals. A correlation was found for HComp subjects (r = 0.50, p < 0.001), but not for OCD patients. HC Healthy control subjects, OCD obsessive-compulsive disorder patients, HComp high compulsive subjects from the general population, LComp low compulsive subjects from general population, OCI-R obsessive-compulsive inventory revised.

Higher learning rates for small prediction errors in OCD

We also assessed differences in error sensitivity between the OCD and HC groups by investigating the relation between learning rate (i.e., the extent to which new information is used for subsequent action) and prediction error. Higher prediction errors require increased learning rate as they are more likely to signal a change in environment. As expected, across both groups, learning rates increased as a function of prediction error magnitude (β = 0.004 (0.0002), t = 24.00, p < 0.001), and thus learning rates were highest after large errors. This effect was less pronounced in OCD (significant PE x group interaction effect: β = −0.001 (0.0002), t = −4.89, p < 0.001) (Fig. 4B). To unpack this effect, a post-hoc mixed-model analysis binning the distribution of prediction errors in 3 equal sized quantiles using quantile function in R (i.e. low [1–8, 10, 11], medium [9, 11–23] and high error magnitude [23-180]), showed that OCD patients specifically had increased learning rates when error magnitude was small (HC-OCD estimate = −0.16 (0.06), Z-ratio: −2.57, p = 0.01) (Fig. 4A). Learning rates were not higher for OCD in general (β = 0.113 (0.06), t = 1.78, p = 0.080) (Fig. 5B). This indicates that only when errors were small, the influence of the most recent outcome on subsequent action (i.e., PE) was higher in the OCD compared to the HC group. This may indicate excessive action (i.e., increased action while this does not improve outcome) in the OCD group as low prediction errors are more likely to represent noise. Note however, that increases in LR in small prediction errors did not lead to worse performance in OCD patients compared to controls (interaction group and LR on performance on trial t + 1: β = 0.047 (0.043), Z = 1.08, p = 0.279). Moreover, the learning rate at small error magnitude was significantly positively related to OCI-R score in OCD patients (r = 0.34, p = 0.039).Fig. 4 Action update & learning rate.

A Mean learning rates and (B) Mean action per group (αˆt) for both comparisons: No difference in LR or action was found between OCD and HC or between HComp and LComp. Dots represent regression coefficients of individual subjects, boxplots show median and upper/lower quantile with whiskers indicating the 1.5 interquartile range, distributions show the probability density function of all data points per group.

Fig. 5 Error sensitivity.

The relationship between prediction error magnitude (δˆt) and learning rate for (A) OCD patients and healthy controls and (B) HComp and LComp. Prediction errors were divided in 20 quantiles, of which 18 quantiles are shown here for visualization purposes. Dots represent mean learning rates per group, error bars represent the SEM. All groups’ learning rates were higher when prediction errors were larger. Learning rates were higher in the OCD group compared to the HC group at low error magnitudes. An interaction effect with group was found in the HComp and LComp comparison, no significant differences in post hoc analyses for the three bins separate. Plot shows an more consistent increase of LR with increased PE. HC healthy control subjects, OCD obsessive-compulsive disorder patients, HComp high compulsive subjects from the general population, LComp low compulsive subjects from general population.

Model-based results no differences in the effect of Bayesian parameters on behavior

Finally, we assessed whether behavior (action and confidence) was differently predicted by the Bayesian parameters, which represent different forms of uncertainty and feedback. As expected, action was significantly predicted by all model-derived parameters and hit, such that increases in PE, CPP and (1-CPP)*(1-MC) predicted an increase in action, while a successful catch of the particle predicted a decrease in action (see Table 2). We did not find any evidence for group differences in the strength of these effects (Fig. 6). Confidence was, as expected, negatively predicted by both PE, CPP and (1-CPP)*(1-MC), and increased with a successful catch of the particle. Again, we did not find any evidence for group differences in the strength of these effects (Fig. 5 and Table 2).Table 2 Results of linear mixed-effects models predicting the effects of computational variables and group on action and confidence.

OCD versus HC	
	Dependent Variable: Action	Dependent Variable: Confidence	
Predictors	Beta (SE)	t	p	Beta (SE)	t	p	
PE	20.01 (2.00)	9.99	< 0.001	−0.96 (0.61)	−1.58	0.117	
CPP	9.44 (2.02)	4.67	< 0.001	−3.22 (0.80)	−4.04	< 0.001	
(1-MC)*(1-CPP)	1.72 (0.44)	3.92	< 0.001	−3.15 (0.61)	−5.12	< 0.001	
Hit	−6.36 (0.48)	−13.28	< 0.001	5.84 (0.69)	8.45	< 0.001	
PE * Group (OCD)	0.60 (2.82)	0.21	0.831	0.53 (0.85)	0.62	0.534	
CPP * Group (OCD)	−0.52 (2.84)	−0.18	0.831	−1.04 (1.11)	−0.93	0.356	
(1-MC)*(1-CPP) * Group (OCD)	0.10 (0.62)	0.17	0.867	−0.26 (0.86)	−0.31	0.761	
Hit * Group (OCD)	0.09 (0.67)	0.14	0.890	−1.14 (0.97)	−1.17	0.245	
The p values in bold indicate a significant result of p < 0.05.

Fig. 6 Model-based results on action and confidence.

Regression coefficients of the regressions assessing the relationship between the parameters from the computational model and (A) participants’ action (i.e. learning rate * absolute prediction error), or (B) participants’ confidence. Small dots represent individual regression coefficients, big dots represent mean regression coefficients per group, error bars denote SEM per group. Predictors included absolute prediction error (PE), change-point probability (CPP), model confidence (MC) and a categorical variable representing hits/misses. C Participants’ learning rate, model learning rate, participants’ confidence and model confidence aligned to change-points (vertical line). Learning rates increased and confidence decreased after a change-point. Confidence was decreased in the OCD group across the entire range of trials.

No differences in perceived hazard rates

Sensitivity analyses in which we performed the same analyses as described above, including the subject-specific perceived hazard rate as a covariate (for calculation see Supplementary Materials) indicated that the perceived hazard rate did not differ between the OCD and HC groups (Figure S1). Moreover, none of the significant group differences found between OCD patients and HCs were influenced by differences in perceived hazard rate between groups. For more details, see Supplementary Materials.

Secondary analyses

Comparing findings in task parameters with high and low compulsive subjects from the general population

Higher confidence in HComp but no differences in action

High compulsive individuals show higher confidence than low compulsive individuals (β = −7.72 (3.5), t = −2.19, p = 0.030) (Fig. 2A) which is the opposite pattern from OCD patients and healthy controls. While no differences in action (β = 0.085 (0.88), t = 0.097, p = 0.92) were found similar to the OCD-HC comparison (Fig. 5A).

More action-confidence decoupling in HComp

As expected higher confidence was related to less action across groups (i.e. action-confidence coupling) (β = −9.76 (0.53), t = −18.32, p < 0.001). Results show an interaction effect between group and confidence on action, indicating that high compulsive individuals showed decreased action-confidence coupling compared to low compulsive individuals (β = −2.27 (1.05), t = −2.16, p = 0.033) (Fig. 3B). This is in contrast to the OCD-HC comparison where no differences in coupling were found.

No differences in learning rates but a different interaction pattern between learning rate and prediction error

In terms of error sensitivity, as expected learning rates increased as a function of prediction error magnitude across groups (β = 0.004 (0.0001), t = 43.62, p < 0.001). In line with the OCD healthy controls comparison we found that this effect was less pronounced in high compulsive compared to low compulsive individuals (β = 0.0003(0.0002), t = 2.21, p = 0.027) (Fig. 4B). Post hoc analyses revealed in contrast to OCD –healthy controls comparison this effect was not significantly different for the low prediction error (HComp-LComp estimate = −0.04 (0.04), Z-ratio: 1.13, p = 0.26) nor for the middle or high prediction error, the plot shows an overall less steep increase of LR in high compulsive individuals (see Fig. 4B). Moreover, no overall differences in LR were found learning rate (β = −0.002 (0.04), t = −0.055, p = 0.96) (Fig. 5B).

Relationship between task behavior and symptom severity between high compulsive individuals and OCD patients

To get more insight into the relationship between task behavior and symptom severity, we looked the correlation between confidence, action and action-confidence coupling and symptom severity, for both OCD patients and high compulsive individuals from the general population.

There was no correlation between confidence and symptom severity in OCD patients for either the YBOCS (r = 0.08, p = 0.61) or the OCI (r = 0.05, p = 0.78) [YBOCS: r = 0.08, p = 0.61) questionnaire. Contrast to the OCD patients but in line with previous results from this sample, the high compulsive individuals did show a clear correlation between confidence and symptoms (OCI: r = 0.52, p < 0.001) (Fig. 2B). This indicates that HComp subjects with higher symptom severity were more confident compared to those with low symptom severity, whereas this is not the case in the patient group. We did not find correlations between symptoms and action for either group (HComp: r = 0.15, p = 0.21; OCD:r = 0.23, p = 0.17).

Moreover, we found no correlation between action-confidence coupling and symptom severity for OCD patients for either the YBOCS (r = 0.007, r = 0.97) or OCI (r = 0.03, p = 0.87) while there was for the high compulsive individuals (r = 0.50, p = < 0.001) (Fig. 3B) again in line with previous reports on this sample. This suggests that subjects from the HComp group with more severe obsessive-compulsive symptoms had a weaker coupling between action and confidence.

Discussion

In this study we sought to extend our understanding of the relationship between action and confidence in a volatile learning environment, comparing individuals with OCD to healthy controls. Moreover, as secondary analyses we investigated high compulsive (compared to low compulsive) individuals from the general population matched to our groups, to better understand how their confidence action interaction relates to OCD patients. The current paradigm has been previously used to investigate OCD adult patients [9], OCD adolescent patients [14] and general population samples with varying obsessive-compulsive symptoms [16]. Here, we specifically included medication-free OCD patients without comorbid diagnoses to obtain a more specific profile of disturbances in action, confidence and their coupling, which we further compared to a matched healthy control group, and see how these results would compare with findings in a general population sample in those with reporting high versus low compulsive symptoms.

Previous findings are inconsistent and discrepant between studies examining clinical OCD samples. In concordance with Marzuki et al., [14], but in contrast to Vaghi et al., [9] the current study did not find evidence that the coupling between action and confidence was disturbed in OCD. Model-based results of previous studies are also inconsistent; while earlier studies showed that OCD patients were more [9] or less [14] influenced by prediction errors in adapting their action or confidence, respectively, than healthy controls, we did not find any differences in the effects of the model-based parameters on action or confidence between OCD patients and the control group.

A consistent finding between the various studies is an increased learning rate in OCD patients compared to controls [9], specifically when prediction error is low [14]. The finding that OCD patients are especially prone to excessive action in response to non-relevant small errors, without a beneficial effect on their performance, could be indicative of hyperactive error signaling [30, 31]. Increased error sensitivity is a well-known endophenotype of OCD [32], which has been related to an increased risk for developing OCD [33]. Overly precise action also resembles excessive checking and information gathering behavior typical for OCD, which has been found especially when actions come with no external cost [34–36].

OCD patients were less confident than controls in their actions, while accuracy and action were equal, corroborating previous work [8, 37]. Even when controlling for anxiety and depression symptoms, OCD patients still showed lower confidence than controls, refuting the idea that decreases in confidence in OCD might be driven by comorbid anxiety and depression symptoms [16].

After comparing our comorbid and medication-free OCD sample to healthy controls, we compared high and low compulsive individuals of a general population sample to examine whether action-confidence pattern in OCD are similar to those of high compulsive individuals. From this sample we matched a group of high and low compulsive individuals to each other and the healthy control and OCD groups in order to be comparable in symptom severity, demographics and number of trials. Many studies have used analogue samples from the general population to study the relationship between (meta)cognitive phenomena and psychiatric symptoms, with the assumption that these relationships resemble those found in clinical patient samples. However, we recently showed divergent relationships between metacognition and obsessive-compulsive symptoms in patients with OCD and highly compulsive individuals from the general population (HComp) with similar OCD symptom severity [18]. Here, we corroborate our previous findings, and find that while OCD patients were characterized by lower confidence than healthy control groups, the HComp group instead showed increased confidence compared to low compulsive individuals (LComp). In the HComp group confidence was positively related to their symptom severity, in contrast to OCD patients, where no relation was found. Moreover, while no decoupling was found in OCD patients, HComp showed weaker action-confidence coupling than LComp individuals which was also related to the severity of symptoms.

These findings point to the idea that there are different behavioral and (meta)cognitive profiles that go together with obsessive-compulsive symptoms, which might be contingent on the clinical or sub-clinical nature of the sample in question. In line, it is plausible that different mechanisms could relate to similar obsessive-compulsive symptom severity, but to different behavioral manifestations. Our findings support a recent model of OCD proposed by Fradkin et al. [38] which suggests that OCD patients experience difficulty in using past experiences to inform future actions, resulting in excessive uncertainty about their own actions (e.g. low confidence) and the state of the world. This low confidence may lead to increased reliance on immediate sensory feedback at the expense of prior beliefs. This profile of behavior seems consistent with our profile of lower confidence and higher learning rates during non-relevant small errors in OCD patients. The positive correlation between learning rates during small errors and OCD symptoms further supports this interpretation. On the other hand, Fradkin’s model suggests that compulsive behavior can also result from overreliance on prior beliefs at the expense of new evidence, leading to habitual behavior taking precedence. This interpretation is more in line with the profile of the HComp group, showing higher confidence and a positive relationship between compulsive symptoms and the decoupling between action and confidence. It suggests that while OCD patients and HComp individuals may present with similar symptoms, the underlying mechanisms of their behavior may differ substantially. However, we acknowledge that alternative explanations for the differences exist, and more research is needed to establish whether indeed compulsive behavior of these two groups can have different origins. Furthermore, it is worth noting that even though the symptom severity as measured with the OCI-R is similar between groups, the extent to which their symptoms impacts daily life may be different. Since the OCI-R measures distress induced by specific and select types of obsessions and compulsions, it can confound severity with the type and range of symptoms [39]. It is plausible that individuals suffering from OCD exhibit a greater frequency of compulsive behaviours on a daily basis, leading to a more pronounced impact on their work, social interactions, and family life compared to those in the HComp group, even if their OCI-R scores are similar. The comparability of the burden of the compulsive symptoms in clinical and analogue samples is a topic worth exploring further using more comprehensive assessment of OCD symptoms.

Compulsivity is a broad concept that is defined as “repetitive acts that are characterized by the feeling that one ‘has to’ perform them while one is aware that these acts are not in line with one’s overall goal” [40]. Compulsive behavior is observed in other disorders than OCD, such as (gambling) addiction [41], where it instead goes hand-in-hand with increased confidence [37]. Moreover, multiple previous studies have shown that a transdiagnostic factor incorporating compulsivity and intrusive thoughts related to increased confidence in sub-clinical samples [16, 42–44]. In future studies it would be of interest to compare relationships between transdiagnostic symptom scores and (meta)cognition between general population and clinical samples.

This study has to be seen in light of its limitations. All groups were tested online, but nevertheless received extensive instructions. The OCD and HC groups were not recruited via specialized online research platforms, whereas the HComp and LComp groups were. It is likely that the HComp and LComp groups consisted of subjects with more experience in participating in online research, which could relate to a better to estimate the volatility of the task (i.e., lower hazard rates, see supplementary materials). With small prediction errors small movements due to motor or decision noise can result in high learning rates which we corrected for by removing the highest 5% of learning rates. However, additive noise may still increase learning rates specifically with lower PE’s. For reasons of consistency with previous studies, we included the model-based analyses. However, while a recent study indicated that the main measures of confidence and learning rates yield good internal consistency and test-retest reliability, the Bayesian model parameters had poorer psychometric quality [17]. Therefore, the model-based measures should be used and interpreted with caution, especially for studying between-subject differences. Improving the ecological validity of the paradigm, using a task where excessive action is penalized, or where the context is more symptom-specific, could provide insight into behavior of OCD patients when excessive precision is costly. Finally, in line with previous studies [9, 14, 16], we made choices about the exclusion of data (top 5% LR and PE = 0). While our analyses and pre-registrated criteria aimed to mitigate bias, the exclusion of data inherently impacts study results.

Together, we showed that OCD patients have lower confidence and increased error sensitivity than healthy controls, without a dissociation between action and confidence. While an opposite pattern was found when comparing highly compulsive individuals versus individuals scoring low on compulsive symptoms from the general population. It is likely that the underlying mechanisms of compulsive behavior differ substantially between these groups, resulting in contrasting (meta)cognitive behavioral manifestations despite equal OC symptom severity.

Supplementary information

Supplementary material

Supplementary information

The online version contains supplementary material available at 10.1038/s41398-024-03042-3.

Acknowledgements

We would like to thank Katja Cornelissen and Fabiënne Meijboom for help with data collection. We also would like to thank Nathan Evans and Eric-Jan Wagenmakers for their helpful comments and suggestions.

Author contributions

JL conceived the study. MH and TM acquired the data. MH, TM and JL analyzed the data. MH, JL, RH interpreted the results. MH and JL drafted the manuscript. All authors revised and contributed to the article and approved the final version

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Competing interests

None of the authors declare any conflict of interest. This study was funded by NWO VENI fellowship granted to JL (number 916-18-119).

Ethical apprroval

The study was approved by the Medical Ethics Committee of the Amsterdam University Medical Centre, and all subjects provided written informed consent before. The study was in accordance with the Declaration of Helsinki.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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References

1. Knill DC Pouget A The Bayesian brain: the role of uncertainty in neural coding and computation Trends Neurosci 2004 27 712 9 15541511
Knill DC, Pouget A. The Bayesian brain: the role of uncertainty in neural coding and computation. Trends Neurosci. 2004;27:712–9.15541511
2. Meyniel F Dehaene S Brain networks for confidence weighting and hierarchical inference during probabilistic learning Proc Natl Acad Sci USA 2017 114 3859 68
Meyniel F, Dehaene S. Brain networks for confidence weighting and hierarchical inference during probabilistic learning. Proc Natl Acad Sci USA. 2017;114:3859–68.
3. Parr T, Friston KJ. Uncertainty, epistemics and active inference. J R Soc Interface. (2017) 14. 10.1016/j.neuron.2005.04.026
4. Desender K Boldt A Yeung N Subjective Confidence Predicts Information Seeking in Decision Making Psychol Sci 2018 29 761 78 29608411
Desender K, Boldt A, Yeung N. Subjective Confidence Predicts Information Seeking in Decision Making. Psychol Sci. 2018;29:761–78.29608411
5. Desender K Murphy P Boldt A Verguts T Yeung N A postdecisional neural marker of confidence predicts information-seeking in decision-making J Neurosci 2019 39 3309 19 30804091
Desender K, Murphy P, Boldt A, Verguts T, Yeung N. A postdecisional neural marker of confidence predicts information-seeking in decision-making. J Neurosci. 2019;39:3309–19.30804091
6. Boldt A Blundell C De Martino B Confidence modulates exploration and exploitation in value-based learning Neurosci Conscious 2019 5 1 12
Boldt A, Blundell C, De Martino B. Confidence modulates exploration and exploitation in value-based learning. Neurosci Conscious. 2019;5:1–12.
7. American Psychiatric Association (2013): Diagnostic and Statistical Manual of Mental Disorders. American Psychiatric Association. 10.1176/appi.books.9780890425596
8. Dar R, Sarna N, Yardeni G, Lazarov A (2022, July 18): Are people with obsessive-compulsive disorder under-confident in their memory and perception? A review and meta-analysis. Psychological Medicine, vol. 52. Cambridge University Press, pp 2404-12.
9. Vaghi MM Luyckx F Sule A Fineberg NA Robbins TW De Martino B Compulsivity Reveals a Novel Dissociation between Action and Confidence Neuron 2017 96 348 54 28965997
Vaghi MM, Luyckx F, Sule A, Fineberg NA, Robbins TW, De Martino B. Compulsivity Reveals a Novel Dissociation between Action and Confidence. Neuron. 2017;96:348–54.28965997
10. Gillan CM Kalanthroff E Evans M Weingarden HM Jacoby RJ Gershkovich M Comparison of the Association between Goal-Directed Planning and Self-reported Compulsivity vs Obsessive-Compulsive Disorder Diagnosis JAMA Psychiatry 2020 77 77 85 31596434
Gillan CM, Kalanthroff E, Evans M, Weingarden HM, Jacoby RJ, Gershkovich M, et al. Comparison of the Association between Goal-Directed Planning and Self-reported Compulsivity vs Obsessive-Compulsive Disorder Diagnosis. JAMA Psychiatry. 2020;77:77–85.31596434
11. Gillan CM Papmeyer M Morein-Zamir S Sahakian BJ Fineberg NA Robbins TW Disruption in the balance between goal-directed behavior and habit learning in obsessive-compulsive disorder Am J Psychiatry 2011 168 718 26 21572165
Gillan CM, Papmeyer M, Morein-Zamir S, Sahakian BJ, Fineberg NA, Robbins TW, et al. Disruption in the balance between goal-directed behavior and habit learning in obsessive-compulsive disorder. Am J Psychiatry. 2011;168:718–26.21572165
12. Gillan CM Robbins TW Goal-directed learning and obsessive–compulsive disorder Philos Trans R Soc B Biol Sci 2014 369 1 11
Gillan CM, Robbins TW. Goal-directed learning and obsessive–compulsive disorder. Philos Trans R Soc B Biol Sci. 2014;369:1–11.
13. Kashyap H Kumar JK Kandavel T Reddy YCJ The dysfunctional inner mirror: Poor insight in obsessive-compulsive disorder, contributions to heterogeneity and outcome CNS Spectr 2014 20 460 2
Kashyap H, Kumar JK, Kandavel T, Reddy YCJ. The dysfunctional inner mirror: Poor insight in obsessive-compulsive disorder, contributions to heterogeneity and outcome. CNS Spectr. 2014;20:460–2.
14. Marzuki AA Vaghi MM Conway-Morris A Kaser M Sule A Apergis-Schoute A Atypical action updating in a dynamic environment associated with adolescent obsessive–compulsive disorder J Child Psychol Psychiatry Allied Discip 2022 63 1591 601
Marzuki AA, Vaghi MM, Conway-Morris A, Kaser M, Sule A, Apergis-Schoute A, et al. Atypical action updating in a dynamic environment associated with adolescent obsessive–compulsive disorder. J Child Psychol Psychiatry Allied Discip. 2022;63:1591–601.
15. Abramowitz JS Fabricant LE Taylor S Deacon BJ McKay D Storch EA The relevance of analogue studies for understanding obsessions and compulsions Clin Psychol Rev 2014 34 206 17 24561743
Abramowitz JS, Fabricant LE, Taylor S, Deacon BJ, McKay D, Storch EA. The relevance of analogue studies for understanding obsessions and compulsions. Clin Psychol Rev. 2014;34:206–17.24561743
16. Seow TXF Gillan CM Transdiagnostic Phenotyping Reveals a Host of Metacognitive Deficits Implicated in Compulsivity Sci Rep 2020 10 1 11 31913322
Seow TXF, Gillan CM. Transdiagnostic Phenotyping Reveals a Host of Metacognitive Deficits Implicated in Compulsivity. Sci Rep. 2020;10:1–11.31913322
17. Loosen A, Seow TXF, Hauser TU (2023): Consistency within change: Evaluating the psychometric properties of a widely-used predictive-inference task. PsyArXiv.
18. Hoven M, Rouault M, van Holst R, Luigjes J (2022): Differences in metacognitive functioning between obsessive- compulsive disorder patients and highly compulsive individuals from the general population. PsyArXiv Prepr. 1–22.
19. Ho D Imai K Imai MK Package ‘MatchIt RStudio 2013 23 2014
Ho D, Imai K, Imai MK. Package ‘MatchIt. RStudio. 2013;23:2014.
20. Foa EB Huppert JD Leiberg S Langner R Kichic R Hajcak G The obsessive-compulsive inventory: Development and validation of a short version Psychol Assess 2002 14 485 96 12501574
Foa EB, Huppert JD, Leiberg S, Langner R, Kichic R, Hajcak G, et al. The obsessive-compulsive inventory: Development and validation of a short version. Psychol Assess. 2002;14:485–96.12501574
21. Sheehan DV Lecrubier Y Sheehan KH Amorim P Janavs J Weiller E The Mini-International Neuropsychiatric Interview (M.I.N.I.): The development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10 J Clin Psychiatry 1998 59 22 33 9881538
Sheehan DV, Lecrubier Y, Sheehan KH, Amorim P, Janavs J, Weiller E, et al. The Mini-International Neuropsychiatric Interview (M.I.N.I.): The development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. J Clin Psychiatry. 1998;59:22–33.9881538
22. Parkitny L McAuley J The depression anxiety stress scale (DASS) J Physiother 2010 56 204 20795931
Parkitny L, McAuley J. The depression anxiety stress scale (DASS). J Physiother. 2010;56:204.20795931
23. Marteau TM Bekker H The development of a six-item short-form of the state scale of the Spielberger State-Trait Anxiety Inventory (STAI) Br J Clin Psychol 1992 31 301 6 1393159
Marteau TM, Bekker H. The development of a six-item short-form of the state scale of the Spielberger State-Trait Anxiety Inventory (STAI). Br J Clin Psychol. 1992;31:301–6.1393159
24. Zung WWK A Self-Rating Depression Scale Arch Gen Psychiatry 1965 12 63 70 14221692
Zung WWK. A Self-Rating Depression Scale. Arch Gen Psychiatry. 1965;12:63–70.14221692
25. Nassar MR Wilson RC Heasly B Gold JI An approximately Bayesian delta-rule model explains the dynamics of belief updating in a changing environment J Neurosci 2010 30 12366 78 20844132
Nassar MR, Wilson RC, Heasly B, Gold JI. An approximately Bayesian delta-rule model explains the dynamics of belief updating in a changing environment. J Neurosci. 2010;30:12366–78.20844132
26. Anwyl-Irvine AL Massonnié J Flitton A Kirkham N Evershed JK Gorilla in our midst: An online behavioral experiment builder Behav Res Methods 2020 52 388 407 31016684
Anwyl-Irvine AL, Massonnié J, Flitton A, Kirkham N, Evershed JK. Gorilla in our midst: An online behavioral experiment builder. Behav Res Methods. 2020;52:388–407.31016684
27. Bates D Mächler M Bolker B Walker S Fitting Linear Mixed-Effects Models Using lme4 J Stat Softw 2015 67 1 48
Bates D, Mächler M, Bolker B, Walker S. Fitting Linear Mixed-Effects Models Using lme4. J Stat Softw. 2015;67:1–48.
28. McGuire JT Nassar MR Gold JI Kable JW Functionally Dissociable Influences on Learning Rate in a Dynamic Environment Neuron 2014 84 870 81 25459409
McGuire JT, Nassar MR, Gold JI, Kable JW. Functionally Dissociable Influences on Learning Rate in a Dynamic Environment. Neuron. 2014;84:870–81.25459409
29. Nassar MR McGuire JT Ritz H Kable JW Dissociable Forms of Uncertainty-Driven Representational Change Across the Human Brain J Neurosci 2019 39 1688 98 30523066
Nassar MR, McGuire JT, Ritz H, Kable JW. Dissociable Forms of Uncertainty-Driven Representational Change Across the Human Brain. J Neurosci. 2019;39:1688–98.30523066
30. Norman LJ Taylor SF Liu Y Radua J Chye Y De Wit SJ Error Processing and Inhibitory Control in Obsessive-Compulsive Disorder: A Meta-analysis Using Statistical Parametric Maps Biol Psychiatry 2019 85 713 25 30595231
Norman LJ, Taylor SF, Liu Y, Radua J, Chye Y, De Wit SJ, et al. Error Processing and Inhibitory Control in Obsessive-Compulsive Disorder: A Meta-analysis Using Statistical Parametric Maps. Biol Psychiatry. 2019;85:713–25.30595231
31. Stern ER Welsh RC Fitzgerald KD Gehring WJ Lister JJ Himle JA Hyperactive Error Responses and Altered Connectivity in Ventromedial and Frontoinsular Cortices in Obsessive-Compulsive Disorder Biol Psychiatry 2011 69 583 91 21144497
Stern ER, Welsh RC, Fitzgerald KD, Gehring WJ, Lister JJ, Himle JA, et al. Hyperactive Error Responses and Altered Connectivity in Ventromedial and Frontoinsular Cortices in Obsessive-Compulsive Disorder. Biol Psychiatry. 2011;69:583–91.21144497
32. Riesel A The erring brain: Error‐related negativity as an endophenotype for OCD—A review and meta‐analysis Psychophysiology 2019 56 1 22
Riesel A. The erring brain: Error‐related negativity as an endophenotype for OCD—A review and meta‐analysis. Psychophysiology. 2019;56:1–22.
33. Riesel A Endrass T Kaufmann C Kathmann N Overactive Error-Related Brain Activity as a Candidate Endophenotype … Am J Psychiatry 2011 168 317 24 21123314
Riesel A, Endrass T, Kaufmann C, Kathmann N. Overactive Error-Related Brain Activity as a Candidate Endophenotype …. Am J Psychiatry. 2011;168:317–24.21123314
34. Banca P Vestergaard MD Rankov V Baek K Mitchell S Lapa T Evidence Accumulation in Obsessive-Compulsive Disorder: The Role of Uncertainty and Monetary Reward on Perceptual Decision-Making Thresholds Neuropsychopharmacology 2015 40 1192 202 25425323
Banca P, Vestergaard MD, Rankov V, Baek K, Mitchell S, Lapa T, et al. Evidence Accumulation in Obsessive-Compulsive Disorder: The Role of Uncertainty and Monetary Reward on Perceptual Decision-Making Thresholds. Neuropsychopharmacology. 2015;40:1192–202.25425323
35. Toffolo MBJ van den Hout MA Engelhard IM Hooge ITC Cath DC Patients With Obsessive-Compulsive Disorder Check Excessively in Response to Mild Uncertainty Behav Ther 2016 47 550 9 27423170
Toffolo MBJ, van den Hout MA, Engelhard IM, Hooge ITC, Cath DC. Patients With Obsessive-Compulsive Disorder Check Excessively in Response to Mild Uncertainty. Behav Ther. 2016;47:550–9.27423170
36. Hauser TU, Moutoussis M, Dayan P, Dolan RJ. Increased decision thresholds trigger extended information gathering across the compulsivity spectrum. Transl Psychiatry (2017);7. 10.1038/s41398-017-0040-3
37. Hoven M Lebreton M Engelmann JB Denys D Luigjes J van Holst RJ Abnormalities of confidence in psychiatry: an overview and future perspectives Transl Psychiatry 2019 9 1 18 30664621
Hoven M, Lebreton M, Engelmann JB, Denys D, Luigjes J, van Holst RJ. Abnormalities of confidence in psychiatry: an overview and future perspectives. Transl Psychiatry. 2019;9:1–18.30664621
38. Fradkin I Adams RA Parr T Roiser JP Huppert JD Searching for an anchor in an unpredictable world: A computational model of obsessive compulsive disorder Psychol Rev 2020 127 672 699 32105115
Fradkin I, Adams RA, Parr T, Roiser JP, Huppert JD. Searching for an anchor in an unpredictable world: A computational model of obsessive compulsive disorder. Psychol Rev. 2020;127:672–699.32105115
39. Abramovitch A Abramowitz JS Riemann BC McKay D Severity benchmarks and contemporary clinical norms for the Obsessive-Compulsive Inventory-Revised (OCI-R) J Obsessive Compuls Relat Disord 2020 27 1 8
Abramovitch A, Abramowitz JS, Riemann BC, McKay D. Severity benchmarks and contemporary clinical norms for the Obsessive-Compulsive Inventory-Revised (OCI-R). J Obsessive Compuls Relat Disord. 2020;27:1–8.
40. Luigjes J Lorenzetti V de Haan S Youssef GJ Murawski C Sjoerds Z Defining Compulsive Behavior Neuropsychol Rev 2019 29 4 13 31016439
Luigjes J, Lorenzetti V, de Haan S, Youssef GJ, Murawski C, Sjoerds Z, et al. Defining Compulsive Behavior. Neuropsychol Rev. 2019;29:4–13.31016439
41. Figee M Pattij T Willuhn I Luigjes J van den Brink W Goudriaan A Compulsivity in obsessive-compulsive disorder and addictions Eur Neuropsychopharmacol 2016 26 856 68 26774279
Figee M, Pattij T, Willuhn I, Luigjes J, van den Brink W, Goudriaan A, et al. Compulsivity in obsessive-compulsive disorder and addictions. Eur Neuropsychopharmacol. 2016;26:856–68.26774279
42. Rouault M Seow T Gillan CM Fleming SM Psychiatric Symptom Dimensions Are Associated With Dissociable Shifts in Metacognition but Not Task Performance Biol Psychiatry 2018 84 443 51 29458997
Rouault M, Seow T, Gillan CM, Fleming SM. Psychiatric Symptom Dimensions Are Associated With Dissociable Shifts in Metacognition but Not Task Performance. Biol Psychiatry. 2018;84:443–51.29458997
43. Benwell CSY Mohr G Wallberg J Kouadio A Ince RAA Psychiatrically relevant signatures of domain-general decision-making and metacognition in the general population npj Ment Heal Res 2022 1 1 17
Benwell CSY, Mohr G, Wallberg J, Kouadio A, Ince RAA. Psychiatrically relevant signatures of domain-general decision-making and metacognition in the general population. npj Ment Heal Res. 2022;1:1–17.
44. Hoven M Luigjes J Denys D Rouault M van Holst RJ How do confidence and self-beliefs relate in psychopathology: a transdiagnostic approach Nat Ment Heal 2023 1 337 45
Hoven M, Luigjes J, Denys D, Rouault M, van Holst RJ. How do confidence and self-beliefs relate in psychopathology: a transdiagnostic approach. Nat Ment Heal. 2023;1:337–45.
