Transdiagnostic Phenotyping Reveals a Host of Metacognitive Deficits Implicated in Compulsivity

Recent work suggests that obsessive-compulsive disorder (OCD) patients have a breakdown in the relationship between explicit beliefs (i.e. confidence about states) and updates to behaviour. The precise computations underlying this disconnection are unclear because case-control and transdiagnostic studies yield conflicting results. Here, a large online population sample (N = 437) completed a predictive inference task previously studied in the context of OCD. We tested if confidence, and its relationship to action and environmental evidence, were specifically associated with self-reported OCD symptoms or common to an array of psychiatric phenomena. We then investigated if a transdiagnostic approach would reveal a stronger and more specific match between metacognitive deficits and clinical phenotypes. Consistent with prior case-control work, we found that decreases in action-confidence coupling were associated with OCD symptoms, but also 5/8 of the other clinical phenotypes tested (8/8 with no correction applied). This non-specific pattern was explained by a single transdiagnostic symptom dimension characterized by compulsivity that was linked to inflated confidence and several deficits in utilizing evidence to update confidence. These data highlight the importance of metacognitive deficits for our understanding of compulsivity and underscore how transdiagnostic methods may prove a more powerful alternative over studies examining single disorders.

trial t+1) was the independent variable in a trial-by-trial regression analysis with age, gender and IQ as fixed effects co-variates (as with all subsequent analyses). Within-subject factors (the intercept and main effect of Confidence) were taken as random effects (i.e., allowed to vary across subjects). Confidence was z-scored within-participant, while the fixed effect predictors were z-scored across participant. If action and confidence are appropriately coupled, participants should move the bucket more (larger Action) when their confidence levels were low, producing a significant negative main effect of Confidence on Action. In the syntax of the lmer function, the regression was: Action ~ Confidence * (Age + IQ + Gender) + (1 + Confidence | Subject).
We then tested if psychiatric symptom severity was associated to changes in action-confidence coupling by including the total score for each questionnaire (QuestionnaireScore, z-scored) as a between-subjects predictor in the model above. Separate regressions were performed for each individual symptom due to high correlations across the different psychiatric questionnaires.
The extent to which questionnaire total scores contribute to changes in action-confidence coupling is indicated by the presence of a significant Confidence*QuestionnaireScore interaction. A positive interaction effect indicates decreased action-confidence coupling (i.e., decoupling), while a negative interaction effect indicates greater action-confidence coupling.
The model was specified as: Action ~ Confidence * (QuestionnaireScore + Age + IQ + Gender) + (1 + Confidence | Subject). For the transdiagnostic analysis, we included all three dimensions in the same model, as correlation across variables was lessened in this formulation and thus more interpretable (only 3 moderately correlated variables r = 0.34 -0.52, instead of 9 that ranged from r = 0.13 -0.84). We replaced QuestionnaireScore in the model formula described previously with three psychiatric dimensions (AD, CIT, SW) entered as z-scored fixed effect predictors. The model was: Action ~ Confidence * (AD + CIT + SW + Age + IQ + Gender) + (1 + Confidence | Subject).

Action and confidence.
To analyse the basic relationship between task-related variables and psychiatric dimensions, the analysis approach was the same, but simpler. Dependent variables were: 1) Size of bucket updates (Action) and 2) reported confidence (Confidence). The models were simply: Task Variable ~ AD + CIT + SW + Age + IQ + Gender + (1 | Subject).
Computation model describing behaviour dynamics. In the behavioural task, participants were required to learn the mean of the underlying generative distribution in order to position their bucket at where they hope to catch the greatest number of particles. Their belief on where the particle landing distribution mean could be guided by 1) information gained from the most recent outcome (i.e. moving the bucket with every small shift in particle location), 2) surprising large changes signalling a change in mean distribution (i.e. change-points) and 3) their uncertainty of the distribution mean based on particle landing location experience over trials.
To separate these contributions, a quasi-optimal Bayesian computational learning model was used to estimate these parameters thought to underlie task dynamics with MATLAB R2018a (The MathWorks, Natick, MA) using functions from Vaghi et al. 1 . This included PE b (model prediction error, an index of recent outcomes), CPP (probability that a trial was a change-point, a measure representing the belief of a surprising outcome) and RU (relative uncertainty, the uncertainty owing to the imprecise estimation of the distribution mean; labelled as (1-CPP)*(1-MC) in Vaghi et al. (Vaghi et al., 2017)). These parameters (where PE b is taken as its absolute) together with a Hit categorical predictor (previous trial was a hit or miss) were used to regress participant adjustments against the benchmark Bayesian model to investigate participant adjustments in reported confidence (Confidence; z-scored confidence level on trial t) and bucket movements (Action) according to the particle landing locations experienced. For visualization purposes, the main effects of the four predictors were correlated with CIT severity, where Spearman's correlation was used to measure the association between symptom dimension severity and the influence of the learning parameters on action update/confidence ( Figure S5).

Influence of metacognitive parameters on action-confidence coupling in compulsivity.
We

Supplemental Figures and Tables
Supplementary Figure S1. Behavioural results. Across participants, the distribution of:   Figure S3. Associations between age, gender and IQ with accuracy, action update, reported confidence, action-confidence coupling or the influence of the model predictors (PE b , CPP, RU) and Hit on confidence/action update. Error bars denote standard errors. The Y-axes indicates the change/percentage change in each dependent variable as a function of 1 standard deviation increase of demographic scores. *p < 0.05, **p < 0.01, ***p < 0.001.
We tested in an exploratory fashion for relationships of task accuracy, action and confidence with age, IQ and gender ( Figure S4). IQ was found to predict better performance (β = 0.07, SE   in the present study were derived from weights obtained from a prior larger study (N = 1413) 5 .
This 3-factor structure was previously reproduced in a smaller independent sample (N = 497) 6 , and here we again replicated similar psychiatric dimensions with our current data (N = 437) with the factor analysis (Supplementary Figure S6). For further details of the factor analysis methodology, see Gillan et al. 5 .
Supplementary Figure S7. Regression analyses of (a) human learning rate (ratio of bucket movement and task prediction error) and (b) action adjustments in OCD, in a model that controlled for age, IQ and gender and in a model that did not. Error bars denote standard errors. The Y-axes indicate the change/percentage change in dependent variable as a function of 1 standard deviation of OCD symptom scores. ^p < 0.07, **p < 0.01, ***p < 0.001. Results are not Bonferroni corrected for multiple comparisons.
Action updating effects in OCD with/without controlling for demographics. Vaghi et al. 1 reported that OCD patients exhibited a higher mean learning rate and that their action updates were more strongly influenced by recent information (PE b ) and less to large unexpected environmental changes (CPP). In the course of exploring the source of this discrepancy with our data, we found that when we repeated our analysis without controlling for age, gender and IQ, some of their effects were recovered here. OCD symptoms were associated with changes in learning and sensitivity to both PE b and CPP in action updating. Specifically, LR h (human  Figure S7). Figure S8. Regression model where confidence updating was predicted by action updating. Dots represent coefficient estimates for individual participants. Red marker indicates mean and SD. These coefficients were correlated with OCD symptom severity, where confidence-action updating coupling was observed to decrease with increasing OCD symptom severity (r = -0.18, p < 0.001).

Supplementary
Action-confidence decoupling analysis. Although this has no bearing on our results (or theirs), we note that Vaghi et al. 1 defined action-confidence coupling slightly differently to how we chose to define it in the present paper -they used confidence updating (i.e. absolute difference between z-scored confidence from trial t and t-1), instead of the reported confidence level on trial t. We suggest that z-scored confidence ratings (rather than their change from trial to trial) are more appropriate because this accounts better for instances where a person has several relatively large PEs in a row (as they figure out where to place the bucket), and should thus not rationally 'change' their confidence rating in response to these PEs, but maintain it at a low level. Although we flag this for the interested reader, we underscore that the two measures are correlated and indeed when we use their definition, we similarly show that selfreported OCD symptom severity predicts confidence-action updating decoupling (r = -0.17, t = -3.58, 95% CI [-0.26, -0.07], p < 0.001, Supplementary Figure S8).