Vascular risk factors, white matter microstructure, and depressive symptoms: a longitudinal analysis in the UK Biobank.

BACKGROUND: Cumulative burden from vascular risk factors (VRFs) has been associated with an increased risk of depressive symptoms in mid- and later life. It has been hypothesised that this association arises because VRFs disconnect fronto-subcortical white matter tracts involved in mood regulation, which puts older adults at higher risk of developing depressive symptoms. However, evidence for the hypothesis that disconnection of white matter tracts underlies the association between VRF burden and depressive symptoms from longitudinal studies is scarce. METHODS: This preregistered study analysed longitudinal data from 6,964 middle-aged and older adults from the UK Biobank who participated in consecutive assessments of VRFs, brain imaging, and depressive symptoms. Using mediation modelling, we directly tested to what extend white matter microstructure mediates the longitudinal association between VRF burden and depressive symptoms. RESULTS: VRF burden showed a small association with depressive symptoms at follow-up. However, there was no evidence that fractional anisotropy (FA) of white matter tracts mediated this association. Additional analyses also yielded no mediating effects using alternative operationalisations of VRF burden, mean diffusivity (MD) of single tracts, or overall average of tract-based white matter microstructure (global FA, global MD, white matter hyperintensity volume). CONCLUSIONS: Our results lend no support to the hypothesis that disconnection of white matter tracts underlies the association between VRF burden and depressive symptoms, while highlighting the relevance of using longitudinal data to directly test pathways linking vascular and mental health.

A latent variable mixed-effects location scale model that also considers between-person differences in the autocorrelation.

In public health research an increasing number of studies is conducted in which intensive longitudinal data is collected in an experience sampling or a daily diary design. Typically, the resulting data is analyzed with a mixed-effects model or mixed-effects location scale model because they allow one to examine a host of interesting longitudinal research questions. Here, we introduce an extension of the mixed-effects location scale model in which measurement error of the observed variables is considered by a latent factor model and in which-in addition to the mean-or location-related effects-the residual variance of the latent factor and the parameters of the autoregressive process of this latent factor can differ between persons. We show how to estimate the parameters of the model with a maximum likelihood approach, whose performance is also compared with a Bayesian approach in a small simulation study. We illustrate the models using a real data example and end with a discussion in which we suggest questions for future research.

A Mixed-Effects Model in Which the Parameters of the Autocorrelated Error Structure Can Differ between Individuals.

Research in psychology has seen a rapid increase in the usage of experience sampling methods and daily diary methods. The data that result from using these methods are typically analyzed with a mixed-effects or a multilevel model because it allows testing hypotheses about the time course of the longitudinally assessed variable or the influence of time-varying predictors in a simple way. Here, we describe an extension of this model that does not only allow to include random effects for the mean structure but also for the residual variance, for the parameter of an autoregressive process of order 1 and/or the parameter of a moving average process of order 1. After we have introduced this extension, we show how to estimate the parameters with maximum likelihood. Because the likelihood function contains complex integrals, we suggest using adaptive Gauss-Hermite quadrature and Quasi-Monte Carlo integration to approximate it. We illustrate the models using a real data example and also report the results of a small simulation study in which the two integral approximation methods are compared.

Comparing type 1 and type 2 error rates of different tests for heterogeneous treatment effects.

Psychologists are increasingly interested in whether treatment effects vary in randomized controlled trials. A number of tests have been proposed in the causal inference literature to test for such heterogeneity, which differ in the sample statistic they use (either using the variance terms of the experimental and control group, their empirical distribution functions, or specific quantiles), and in whether they make distributional assumptions or are based on a Fisher randomization procedure. In this manuscript, we present the results of a simulation study in which we examine the performance of the different tests while varying the amount of treatment effect heterogeneity, the type of underlying distribution, the sample size, and whether an additional covariate is considered. Altogether, our results suggest that researchers should use a randomization test to optimally control for type 1 errors. Furthermore, all tests studied are associated with low power in case of small and moderate samples even when the heterogeneity of the treatment effect is substantial. This suggests that current tests for treatment effect heterogeneity require much larger samples than those collected in current research.

Estimating nonlinear effects of random slopes: A comparison of multilevel structural equation modeling with a two-step, a single-indicator, and a plausible values approach.

Multilevel structural equation modeling (MSEM) is a statistical framework of major relevance for research concerned with people's intrapersonal dynamics. An application domain that is rapidly gaining relevance is the study of individual differences in the within-person association (WPA) of variables that fluctuate over time. For instance, an individual's social reactivity - their emotional response to social situations - can be represented as the association between repeated measurements of the individual's social interaction quantity and momentary well-being. MSEM allows researchers to investigate the associations between WPAs and person-level outcome variables (e.g., life satisfaction) by specifying the WPAs as random slopes in the structural equation on level 1 and using the latent representations of the slopes to predict outcomes on level 2. Here, we are concerned with the case in which a researcher is interested in nonlinear effects of WPAs on person-level outcomes - a U-shaped effect of a WPA, a moderation effect of two WPAs, or an effect of congruence between two WPAs - such that the corresponding MSEM includes latent interactions between random slopes. We evaluate the nonlinear MSEM approach for the three classes of nonlinear effects (U-shaped, moderation, congruence) and compare it with three simpler approaches: a simple two-step approach, a single-indicator approach, and a plausible values approach. We use a simulation study to compare the approaches on accuracy of parameter estimates and inference. We derive recommendations for practice and provide code templates and an illustrative example to help researchers implement the approaches.

Parametric and nonparametric propensity score estimation in multilevel observational studies.

There has been growing interest in using nonparametric machine learning approaches for propensity score estimation in order to foster robustness against misspecification of the propensity score model. However, the vast majority of studies focused on single-level data settings, and research on nonparametric propensity score estimation in clustered data settings is scarce. In this article, we extend existing research by describing a general algorithm for incorporating random effects into a machine learning model, which we implemented for generalized boosted modeling (GBM). In a simulation study, we investigated the performance of logistic regression, GBM, and Bayesian additive regression trees for inverse probability of treatment weighting (IPW) when the data are clustered, the treatment exposure mechanism is nonlinear, and unmeasured cluster-level confounding is present. For each approach, we compared fixed and random effects propensity score models to single-level models and evaluated their use in both marginal and clustered IPW. We additionally investigated the performance of the standard Super Learner and the balance Super Learner. The results showed that when there was no unmeasured confounding, logistic regression resulted in moderate bias in both marginal and clustered IPW, whereas the nonparametric approaches were unbiased. In presence of cluster-level confounding, fixed and random effects models greatly reduced bias compared to single-level models in marginal IPW, with fixed effects GBM and fixed effects logistic regression performing best. Finally, clustered IPW was overall preferable to marginal IPW and the balance Super Learner outperformed the standard Super Learner, though neither worked as well as their best candidate model.

Routes to momentary self-esteem in adolescence: Links with interpersonal perceptions of liking and personality metaperceptions within social interactions.

OBJECTIVE: This study provides insights into the perceptual processes contributing to positive social experiences and momentary self-esteem in adolescents' initial social interactions. BACKGROUND: A person's self-esteem is shaped by their social experiences. However, little is known about which interpersonal perceptions are linked to momentary self-esteem within social interactions. Identifying two key interpersonal perceptions, we examined differential associations between an individual's meta-liking and other-perceptions of liking by interaction partners with momentary self-esteem. Further, we investigated how preceding personality metaperceptions (i.e., meta-accuracy and meta-positivity) extended these sociometer processes. METHOD: The current study used round-robin data from N = 296 adolescents participating in a virtual group interaction. Social accuracy modeling was used to estimate meta-accuracy and meta-positivity of personality metaperceptions, and path models were used to test associations with interpersonal perceptions of liking and momentary self-esteem. RESULTS: Two main findings emerged: First, meta-liking was consistently related to higher momentary self-esteem, whereas other-perceptions of liking were not. Second, meta-positivity of personality metaperceptions was linked to higher meta-liking and indirectly contributed to higher momentary self-esteem through meta-liking. CONCLUSION: These findings highlight the importance of considering different interpersonal perceptions to understand social interaction experiences.

Gradient Tree Boosting for Hierarchical Data.

Gradient tree boosting is a powerful machine learning technique that has shown good performance in predicting a variety of outcomes. However, when applied to hierarchical (e.g., longitudinal or clustered) data, the predictive performance of gradient tree boosting may be harmed by ignoring the hierarchical structure, and may be improved by accounting for it. Tree-based methods such as regression trees and random forests have already been extended to hierarchical data settings by combining them with the linear mixed effects model (MEM). In the present article, we add to this literature by proposing two algorithms to estimate a combination of the MEM and gradient tree boosting. We report on two simulation studies that (i) investigate the predictive performance of the two MEM boosting algorithms and (ii) compare them to standard gradient tree boosting, standard random forest, and other existing methods for hierarchical data (MEM, MEM random forests, model-based boosting, Bayesian additive regression trees [BART]). We found substantial improvements in the predictive performance of our MEM boosting algorithms over standard boosting when the random effects were non-negligible. MEM boosting as well as BART showed a predictive performance similar to the correctly specified MEM (i.e., the benchmark model), and overall outperformed the model-based boosting and random forest approaches.

A Tutorial Introduction to Heterogeneous Treatment Effect Estimation with Meta-learners.

Psychotherapy has been proven to be effective on average, though patients respond very differently to treatment. Understanding which characteristics are associated with treatment effect heterogeneity can help to customize therapy to the individual patient. In this tutorial, we describe different meta-learners, which are flexible algorithms that can be used to estimate personalized treatment effects. More specifically, meta-learners decompose treatment effect estimation into multiple prediction tasks, each of which can be solved by any machine learning model. We begin by reviewing necessary assumptions for interpreting the estimated treatment effects as causal, and then give an overview over key concepts of machine learning. Throughout the article, we use an illustrative data example to show how the different meta-learners can be implemented in R. We also point out how current popular practices in psychotherapy research fit into the meta-learning framework. Finally, we show how heterogeneous treatment effects can be analyzed, and point out some challenges in the implementation of meta-learners.

An extension of the mixed-effects growth model that considers between-person differences in the within-subject variance and the autocorrelation.

Experience sampling methods have led to a significant increase in the availability of intensive longitudinal data. Typically, this type of data is analyzed with a mixed-effects model that allows to examine hypotheses concerning between-person differences in the mean structure by including multiple random effects per individual (eg, random intercept and random slopes). Here, we describe an extension of this model that-in addition to the random effects for the mean structure-also includes a random effect for the within-subject variance and a random effect for the autocorrelation. After the description of the model, we show how its parameters can be efficiently estimated using a marginal maximum likelihood (ML) approach. We then illustrate the model using a real data example. We also present the results of a small simulation study in which we compare the ML approach with a Bayesian estimation approach.

Integrated neuropsychological and cognitive behavioural therapy after acquired brain injury: A pragmatic randomized clinical trial.

After acquired brain injury (ABI) many patients suffer from persistent cognitive and emotional disturbances. The aim of this study was to investigate the treatment outcome of an integrated intervention, combining neuropsychological and cognitive behavioural therapy (nCBT), against waitlist (WL) in outpatients with ABI. Individuals seeking outpatient treatment for cognitive and emotional problems after ABI were randomly allocated to nCBT (n = 27) or WL (n = 29) and completed assessments at baseline, post-treatment/WL and at six-month follow-up. The primary outcome measures were general psychopathology and functional activity in daily life. The nCBT group showed significant improvement for general psychopathology post-treatment when compared to WL. nCBT was also superior to WL regarding the secondary outcomes, i.e., the reduction of negative affect and the improvement of quality of life. No significant differences for functional activity and community integration were observed. Significant pre-post effect sizes ranged between small for functional activity and medium for quality of life. The positive effects were maintained at follow-up. The majority of patients with cognitive and emotional problems after ABI benefit from an integrated approach that offers cognitive remediation and psychotherapy. However, the heterogeneous sequelae of ABI and the moderate sample sizes in clinical trials present a methodological challenge to ABI research.

Measuring motivational relationship processes in experience sampling: A reliability model for moments, days, and persons nested in couples.

The investigation of within-person process models, often done in experience sampling designs, requires a reliable assessment of within-person change. In this paper, we focus on dyadic intensive longitudinal designs where both partners of a couple are assessed multiple times each day across several days. We introduce a statistical model for variance decomposition based on generalizability theory (extending P. E. Shrout & S. P. Lane, 2012), which can estimate the relative proportion of variability on four hierarchical levels: moments within a day, days, persons, and couples. Based on these variance estimates, four reliability coefficients are derived: between-couples, between-persons, within-persons/between-days, and within-persons/between-moments. We apply the model to two dyadic intensive experience sampling studies (n1 = 130 persons, 5 surveys each day for 14 days, ≥ 7508 unique surveys; n2 = 508 persons, 5 surveys each day for 28 days, ≥ 47764 unique surveys). Five different scales in the domain of motivational processes and relationship quality were assessed with 2 to 5 items: State relationship satisfaction, communal motivation, and agentic motivation; the latter consists of two subscales, namely power and independence motivation. Largest variance components were on the level of persons, moments, couples, and days, where within-day variance was generally larger than between-day variance. Reliabilities ranged from .32 to .76 (couple level), .93 to .98 (person level), .61 to .88 (day level), and .28 to .72 (moment level). Scale intercorrelations reveal differential structures between and within persons, which has consequences for theory building and statistical modeling.

Mimicry in psychotherapy - an actor partner model of therapists' and patients' non-verbal behavior and its effects on the working alliance.

Objective: The alliance between patient and therapist is central for therapeutic progress. Social mimicry, the automatic imitation of another person, has been linked to building relationships. This is the first preregistered study to systematically investigate mimicry and its effects on the working alliance in real psychotherapy sessions.Method: Sixty-four different patient-therapist dyads were filmed during a cognitive behavioral therapy (CBT) treatment for obsessive-compulsive disorder (OCD). By using actor-partner interdependence models, we analyzed the extent to which therapists' and patients' earlier mimicry predicted their own mimicry (actor effect), how they influenced each other in their mimicry (partner effect) and the effects of mimicry on the patients' alliance. Videos of 151 sessions were rated by two observers.Results: Patients showed stable low mimicry levels. Furthermore, the patients' mimicry predicted that of the therapists, indicating that therapists adapt their mimicry levels to patients. Only the patients' but not the therapists' mimicry at the beginning of treatment predicted the patients' alliance.Conclusion: The results suggest that the role of the active patient is underestimated. Furthermore, results offer new treatment targets by shedding light on low mimicry levels displayed by patients, which might lead to social costs.

Wikipedia outperforms individuals when it comes to hindsight bias.

Hindsight bias-the tendency to overestimate in hindsight what one knew in foresight-is a robust and pervasive human error. A recent study with Wikipedia articles, however, found evidence for a hindsight bias only for disasters but not for any other event category (e.g., elections). Although this might suggest Wikipedia articles to be less biased than individuals, alternative explanations had not been ruled out. The present study set out to answer this question by comparing individuals' and Wikipedia's representation of the very same event in foresight and hindsight. In particular, we made use of a state election and surveyed one part of participants before and after the outcome and had other participants rate the corresponding Wikipedia article versions with regard to the extent to which the article was suggestive of a particular outcome and presented it as foreseeable and inevitable. In line with prior research and our hypotheses, we found a hindsight bias at the individual level but not in Wikipedia articles. Applying Bayesian statistics, there was substantial support for the null hypothesis (i.e., no hindsight bias) in Wikipedia. By controlling for the potential impact of participants' own hindsight bias on their article ratings we can rule out alternative explanations of our findings. Therefore, our findings are the first to demonstrate Wikipedia's superiority over individuals when it comes to hindsight bias.

Neuroticism and interpersonal perception: Evidence for positive, but not negative, biases.

OBJECTIVE: Personality dispositions predict how individuals perceive, interpret, and react to social interactions with others. A still unresolved question is (a) whether these personality-congruent interpersonal perceptions reflect perception biases, which occur when perceivers' dispositions systematically predict deviations between perceivers' and other people's perceptions of the same interaction, and/or selection effects, which occur when perceivers' dispositions predict their selection of interaction partners, and (b) whether these effects feed back into perceivers' personality. METHOD: Data from 110 psychology freshmen involving repeated assessments of Neuroticism and repeated interpersonal perceptions of social interactions with fellow students were analyzed to address these questions, focusing on Neuroticism. RESULTS: There is evidence for a Neuroticism-related positivity bias in interpersonal perceptions (i.e., perceivers high in Neuroticism tended to make more positive judgments of others' sociability and warmth), but little evidence for personality-congruent selection effects (i.e., Neuroticism-related preferences for interaction partners). The positivity bias did not predict intrapersonal changes in Neuroticism over time, but the selection of specific interaction partners did. CONCLUSIONS: These findings help to shed light on the interpersonal perception dynamics of Neuroticism in a real-life context and add to our understanding of the psychological mechanisms underlying the interplay of personality and interpersonal perceptions.

Disentangling the Sources of Mimicry: Social Relations Analyses of the Link Between Mimicry and Liking.

Mimicry is an important interpersonal behavior for initiating and maintaining relationships. By observing the same participants ( N = 139) in multiple dyadic interactions (618 data points) in a round-robin design, we disentangled the extent to which mimicry is due to (a) the mimicker's general tendency to mimic (imitativity), (b) the mimickee's general tendency to evoke mimicry (imitatability), and (c) the unique dyadic relationship between the mimicker and the mimickee. We explored how these mimicry components affected liking and metaperceptions of liking (i.e., metaliking). Employing social relations models, we found substantial interindividual differences in imitativity, which predicted popularity. However, we found only small interindividual differences in imitatability. We found support for our proposition that mimicry is a substantially dyadic construct explained mostly by the unique relationship between two people. Finally, we explored the link between dyadic mimicry and liking, and we found that a person's initial liking of his or her interaction partner led to mimicry, which in turn increased the partner's liking of the mimicker.

Biases in the production and reception of collective knowledge: the case of hindsight bias in Wikipedia.

The Web 2.0 enabled collaboration at an unprecedented level. In one of the flagships of mass collaboration-Wikipedia-a large number of authors socially negotiate the world's largest compendium of knowledge. Several guidelines in Wikipedia restrict contributions to verifiable information from reliable sources to ensure recognized knowledge. Much psychological research demonstrates, however, that individual information processing is biased. This poses the question whether individual biases translate to Wikipedia articles or whether they are prevented by its guidelines. The present research makes use of hindsight bias to examine this question. To this end, we analyzed foresight and hindsight versions of Wikipedia articles regarding a broad variety of events (Study 1). We found the majority of articles not to contain traces of hindsight bias-contrary to prior individual research. However, for a particular category of events-disasters-we found robust evidence for hindsight bias. In a lab experiment (Study 2), we then examined whether individuals' hindsight bias is translated into articles under controlled conditions and tested whether collaborative writing-as present in Wikipedia-affects the resultant bias (vs. individual writing). Finally, we investigated the impact of biased Wikipedia articles on readers (Study 3). As predicted, biased articles elicited a hindsight bias in readers, who had not known of the event previously. Moreover, biased articles also affected individuals who knew about the event already, and who had already developed a hindsight bias: biased articles further increased their hindsight.

How Do Different Ways of Measuring Individual Differences in Zero-Acquaintance Personality Judgment Accuracy Correlate With Each Other?

OBJECTIVE: This research compares two different approaches that are commonly used to measure accuracy of personality judgment: the trait accuracy approach wherein participants discriminate among targets on a given trait, thus making intertarget comparisons, and the profile accuracy approach wherein participants discriminate between traits for a given target, thus making intratarget comparisons. We examined correlations between these methods as well as correlations among accuracies for judging specific traits. METHOD: The present article documents relations among these approaches based on meta-analysis of five studies of zero-acquaintance impressions of the Big Five traits. RESULTS: Trait accuracies correlated only weakly with overall and normative profile accuracy. Substantial convergence between the trait and profile accuracy methods was only found when an aggregate of all five trait accuracies was correlated with distinctive profile accuracy. Importantly, however, correlations between the trait and profile accuracy approaches were reduced to negligibility when statistical overlap was corrected by removing the respective trait from the profile correlations. Moreover, correlations of the separate trait accuracies with each other were very weak. CONCLUSIONS: Different ways of measuring individual differences in personality judgment accuracy are not conceptually and empirically the same, but rather represent distinct abilities that rely on different judgment processes.

What IAPT CBT High-Intensity Trainees Do After Training.

BACKGROUND: The UK Department of Health Improving Access to Psychological Therapies (IAPT) initiative set out to train a large number of therapists in cognitive behaviour therapies (CBT) for depression and anxiety disorders. Little is currently known about the retention of IAPT CBT trainees, or the use of CBT skills acquired on the course in the workplace after training has finished. AIMS: This study set out to conduct a follow-up survey of past CBT trainees on the IAPT High Intensity CBT Course at the Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King's College London (KCL), one of the largest IAPT High Intensity courses in the UK. METHOD: Past trainees (n = 212) across 6 cohorts (2008-2014 intakes) were contacted and invited to participate in a follow-up survey. A response rate of 92.5% (n = 196) was achieved. RESULTS: The vast majority of IAPT trainees continue to work in IAPT services posttraining (79%) and to practise CBT as their main therapy modality (94%); 61% have become CBT supervisors. A minority (23%) have progressed to other senior roles in the services. Shortcomings are reported in the use of out-of-office CBT interventions, the use of disorder-specific outcome measures and therapy recordings to inform therapy and supervision. CONCLUSIONS: Past trainees stay working in IAPT services and continue to use CBT methods taught on the course. Some NICE recommended treatment procedures that are likely to facilitate patients' recovery are not being routinely implemented across IAPT services. The results have implications for the continued roll out of the IAPT programme, and other future large scale training initiatives.

Accurate judgments of neuroticism at zero acquaintance: a question of relevance.

Prior studies have consistently found a surprising inaccuracy of people's neuroticism judgments at zero acquaintance. Based on the Realistic Accuracy Model (Funder, 1995), we hypothesize that this is due to a lack of relevance of the situation in which targets are typically observed. Fifty participants were videotaped in a highly trait-relevant (i.e., socially stressful) situation as well as three less relevant situations. An aggregate of self-reports and informant reports was used as the accuracy criterion. Four independent groups of unacquainted observers judged participants' neuroticism based on these short video sequences. Results showed that neuroticism judgments were significantly more accurate for the most trait-relevant situation compared with the other three situations. This finding can be explained using lens model analyses: Only in the most relevant situation did neuroticism predict both visual nervousness and vocal nervousness, both of which in turn predicted neuroticism judgments by lay observers. Our findings show that strangers are sensitive to interindividual differences in neuroticism as long as targets are observed in a trait-relevant situation.