Subjects

The goal of this study was to compare information gathering in subjects who differed in obsessive-compulsive symptoms (subsequently called ‘compulsivity’ for short, but this is not intended to imply an exclusion of obsessions), but were comparable on other psychiatric traits, in particular in relation to symptoms of depression and anxiety. This is important because a high comorbidity between OCD, depression and anxiety often renders a dissociation difficult for classic patient studies[17]. We thus used a large population-representative sample of young people in London and Cambridge (U-CHANGE study; N = 2409; www.nspn.org.uk 18,19,20), from whom we had collected questionnaire and health-related information. From this database, we recruited 20 adult subjects with low (21.40 ± 2.52 years) and 20 adults with high compulsivity scores (20.75 ± 2.34 years; group details see Table 1). Importantly, the low compulsive subjects were specifically selected so as to match the high compulsives on anxiety and depression scores, which allowed us to determine whether information gathering biases were specific to variation in the compulsivity spectrum.

Table 1 Subject characteristics Full size table

As an index of compulsivity, we used the total score of the revised Padua Inventory questionnaire (PI-WSUR)21. The PI-WSUR is an established questionnaire for assessing obsessive-compulsive traits with a high test-retest reliability and internal consistency21. The subjects with high compulsive scores were in the 91.62 ± 5.83 percentile of the U-CHANGE populations’ PI-WSUR distribution, whereas the low compulsive group scored on the 27.29 ± 16.55 percentile of this distribution.

To match groups for depressive mood, we recruited subjects based on the Mood and Feelings Questionnaire (MFQ)22, which has a high internal consistency and can be used as a screening for depression23. As a recruitment measure of anxiety, we used the Revised Children’s Manifest Anxiety Scale (RCMAS)24, again known to have good psychometric properties25. The recruited groups scored on both questionnaires well within the normal range of the population (MFQ: low compulsivity: 31.53 ± 13.64 percentile, high compulsivity: 30.13 ± 16.18; RCMAS: low compulsivity: 31.63 ± 12.06, high compulsivity: 28.71 ± 14.47), thus not being either particularly low or high in depressive and anxiety symptoms.

At the day of the assessment, we complemented these measurements with additional questionnaires of anxiety and depression that were specifically developed for >18 year olds, because both MFQ and RCMAS were primarily validated in children and adolescents. We thus collected the State and Trait Anxiety Inventory (STAI)26 and Beck Depression Inventory II (BDI-II)27. In addition, we collected further measures, such as impulsivity (Barratt Impulsiveness Scale, BIS)28, intelligence (matrix and vocabulary subtests of the Wechsler Abbreviated Scale of Intelligence, WASI)29, and handedness30. The groups differed in none of the measures (full details in Table 1), ensuring that a difference in compulsivity is the key discriminating feature between the groups.

Moreover, subjects were only recruited if they fulfilled the following additional inclusion criteria: no neurological or psychiatric diagnosis (self-reported screening question), over 18 years, living in London, absence of colour blindness. Data from these subjects has previously been reported, investigating a different task collected on the same occasion20. The study was approved by the UCL research ethics committee (No. 6218/001) and all subjects gave written informed consent. Subjects received monetary compensation for their participation, but this did not depend on the performance in the reported task.

Task

The subjects performed a paradigm based on the ‘information sampling task’7,11,31,32 (Fig. 1). In each game, subjects saw 25 covered cards (depicted by gray squares). Each of these cards could be uncovered using the computer mouse, to reveal one of two colours. In every game subjects were instructed to indicate whether they considered the majority of cards to be blue or yellow (the colours actually varied between games, the nomenclature here is used for simplicity). They were allowed to uncover as many cards as needed (a short delay of 250 ms was introduced between opening the tiles), without restriction on the time spent on the task. Once they felt ‘certain enough’ (detailed instructions are provided in Supplementary Information) they indicated their final decision by selecting the respective colour. After their decision, a short feedback screen (1000 ms) provided information about how many points were won (current and total points), and the next game followed immediately. Subjects had to open at least one card prior to deciding, but there was no maximum, so that subjects were allowed to sample all 25 cards possible before deciding.

Figure 1 Information gathering task. Subjects were asked to indicate whether the majority of the 25 covered cards (left panel) were of yellow or blue colour. In each game, they were allowed to uncover as many cards as they wished (middle panels) by clicking on a covered card. When they had decided they indicated their decision by selecting the respective colour (right panel) Full size image

We implemented two different reward schemes (the order of presentation of the schemes was kept constant across subjects). In the first set of 10 games (‘fixed’ condition), subjects won or lost 100 points by declaring the correct or incorrect colour, irrespective of the number of cards they uncovered. Then, in the second set of 10 games (‘decreasing’ condition), the potential win decreased as a function of sampling. The potential win started at 250 points and decreased by 10 points for every card that was uncovered (e.g., a correct decision after 4 opened cards would provide 250-4*10 = 210 points). Subjects lost 100 points for a wrong decision, irrespective of the number of uncovered cards. Before the start of the fixed condition, subjects performed one practice game to familiarise themselves with the task. Details about the sequences shown are provided in the Supplementary Information.

Behavioural analysis

We analysed performance in this task using repeated-measures ANOVAs with within-subject factor condition (fixed, decreasing) and between-subject factor group (high, low compulsives). These analyses were complemented by independent-sample t-tests.

Based on previous findings that patients with OCD sampled more in the fixed condition and won more points7, a priori we focused our analyses on performance differences (esp. number of draws, points won) between the groups in the fixed condition. In addition, we explored how information gathering in the fixed condition was related to a questionnaire-based report of intolerance of uncertainty33 and whether this additionally explains information gathering, over and above the compulsivity group. Effect size metrics (partial eta-squared η p 2, Cohen’s d) and 95% confidence intervals (CI) are reported where appropriate.

Computational modelling

To understand the cognitive processes that were driving the behavioural differences, we fitted a previously developed Bayesian computational model7,34. A description of the model, the data fitting procedure and model comparison can be found in the supplementary material. In the best-fitting model, subjects form a (Bayesian) belief about which of the colours is more likely to form a majority based on the cards they have unveiled so far (i.e., ‘how likely is it that there are 13 or more yellow cards?’). Subjects then use this belief to arbitrate whether to sample more cards (non-deciding) or to decide in favour of one of the two colours. This arbitration is formed by computing state-action values (Q-values)35 that indicate the worth of taking each possible action. The Q-values for choosing the colours are computed based on the belief about whether a particular colour forms a majority, weighted by the outcomes of choosing the (in-)correct colour. The Q-value for non-deciding consists of two factors. One is the belief about how certain one will be if one continues with sampling (i.e., weighted Q-values of future states, computed using backwards induction). The second factor is a subjective cost per step (or urgency signal36,37,38,39) that promotes earlier decisions. The ultimate arbitration between these three Q-values is determined by a softmax choice rule40 with an additional lapse rate41.

In the model comparison (cf Supplementary Information), we found that the subjective costs per step followed a nonlinear, sigmoidal, function. This means the subjective costs of sampling were small initially but increased markedly as more information was gathered, similar to a previously described urgency signal36. This process is mainly controlled by an ‘impatience’ parameter p that describes the stage at which these costs start escalating. The model comparison also revealed that subjects did not correctly represent the external costs in the decreasing condition, leading to suboptimal oversampling in this condition (cf Fig. 2, Supplementary Information). This is why the winning model absorbs both external and internal costs in the urgency signal.

Figure 2 High compulsives sample more information when no external costs apply. a High compulsive subjects gather more information before making a decision in the fixed condition, where sampling came at no external costs. b This increased information gathering led to higher winnings in the fixed condition, with no difference evident in the decreasing condition. c Subjects did not differ in their choice consistency, i.e., whether they picked the colour that was more plentiful at the time of choice. Computational modelling revealed that performance high compulsive subjects was more similar to an optimal agent (green diamonds) in the fixed condition, whereas in the decreasing condition, the low compulsive subjects were slightly more optimal in turns of draws and points won. **p < .01, *p < .05 Full size image

Model-based analyses

To examine the cognitive mechanisms behind the group differences we fitted a set of recently developed models to each subject’s data (computational model is detailed in Supplementary Information). Using the best fitting model (determined using Bayesian Information Criterion), we then compared the model predictions between the two groups. First, we compared decision thresholds so as to formalize any variation in information gathering behaviour. Decision thresholds are well known from passive evidence accumulation models that use a fixed stopping rule42,43. At each stage of information gathering, these thresholds characterise the mean difference in the evidence for the two colours at which subjects are willing to make a decision. In our task the threshold is influenced by the finiteness of the problem (i.e., that there are only 25 squares to sample and a majority of 13 cards is sufficient to be 100% certain) and the subjective cost of sampling, which reduces the Q-values for non-deciding (cf. Fig. S3). We used our computational model to compute the decision thresholds, computed as the mean evidence difference when a simulated agent (using the best-fitting subject-specific parameters) made a decision, separated for each sampling stage. With this measure we can assess how much evidence a (simulated) subject needs at each stage39, but also examine how a decision criterion might collapse as a function of sampling.

In our task, the collapsing decision threshold is driven by two factors: the finite horizon of the task, and a subjective urgency signal. The former factor is independent of subjects’ preferences and captures the fact that as one gets close to opening all cards, a smaller evidence difference is needed to reach an absolute majority (i.e., 13 cards). The second factor, which we term urgency, is based on the observation that subjects express subjective costs of sampling information and these costs escalate as sampling proceeds. The stage when these costs escalate is determined by the impatience parameter p (midpoint of a sigmoid cost function).

Group differences for decision thresholds and urgency signals were assessed using a cluster-extent permutation test (p < .05, height threshold t = 1, 1000 iterations)44.

To understand which aspects of the model were giving rise to the observed differences, we compared model parameters between groups using non-parametric Wilcoxon rank-sum tests, focusing on the impatience parameter p in the fixed condition, which our previous work suggested was diagnostic for patients with OCD7.