Boosting arousal and cognitive performance through alternating posture: Insights from a multi-method laboratory study.

This study investigated the role of arousal and effort costs in the cognitive benefits of alternating between sitting and standing postures using a sit-stand desk, while measuring executive functions, self-reports, physiology, and neural activity in a 2-h laboratory session aimed to induce mental fatigue. Two sessions were conducted with a one-week gap, during which participants alternated between sitting and standing postures each 20-min block in one session and remained seated in the other. In each block, inhibition, switching, and updating were assessed. We examined effects of time-on-task, acute (local) effects of standing versus sitting posture, and cumulative (global) effects of a standing posture that generalize to the subsequent block in which participants sit. Results (N = 43) confirmed that time-on-task increased mental fatigue and decreased arousal. Standing (versus sitting) led to acute increases in arousal levels, including self-reports, alpha oscillations, and cardiac responses. Standing also decreased physiological and perceived effort costs. Standing enhanced processing speed in the flanker task, attributable to shortened nondecision time and speeded evidence accumulation processes. No significant effects were observed on higher-level executive functions. Alternating postures also increased heart rate variability cumulatively over time. Exploratory mediation analyses indicated that the positive impact of acute posture on enhanced drift rate was mediated by self-reported arousal, whereas decreased nondecision time was mediated by reductions in alpha power. In conclusion, alternating between sitting and standing postures can enhance arousal, decrease effort costs, and improve specific cognitive and physiological outcomes.

Self-regulation profiles in addictive behaviors among adolescents: A transdiagnostic approach.

Dual-pathway models suggest that poor self-regulation (immature regulatory combined with strong reactive processes) is an important factor underlying addictive behaviors among adolescents. This study examined whether there are different self-regulation profiles among community adolescents, and how these profiles are related to the presence, severity and comorbidity of different addictive behaviors. A community sample of 341 adolescents (54.5% female; 13-17 years) was recruited. Participants self-reported on regulatory (inhibitory control) and reactive (reward and punishment sensitivity) processes, as well as on different addictive behaviors (binge eating, tobacco-, cannabis- and alcohol use, gaming, gambling and pathological buying). A model-based clustering analysis found evidence for three meaningful profiles: 'impulsive/under-controlled', 'anxious' and 'protective'. The 'impulsive/under-controlled' profile was characterized by the highest prevalence and severity of cannabis use and the most severe alcohol use. The 'impulsive/under-controlled' and 'protective' profiles demonstrated the highest prevalence and severity of tobacco use, whereas the 'impulsive/under-controlled' and 'anxious' profiles showed the highest binge eating scores. Adolescents who reported more than three types of addictive behaviors generally belonged to the 'impulsive/under-controlled' profile. The profiles did not differ for gaming, gambling and pathological buying. The 'impulsive/under-controlled' profile emerged as the most vulnerable profile in the context of addictive behaviors (especially for binge eating and substance use).

Neurobiological correlates of antisociality across adolescence and young adulthood: a multi-sample, multi-method study.

BACKGROUND: Antisociality across adolescence and young adulthood puts individuals at high risk of developing a variety of problems. Prior research has linked antisociality to autonomic nervous system and endocrinological functioning. However, there is large heterogeneity in antisocial behaviors, and these neurobiological measures are rarely studied conjointly, limited to small specific studies with narrow age ranges, and yield mixed findings due to the type of behavior examined. METHODS: We harmonized data from 1489 participants (9-27 years, 67% male), from six heterogeneous samples. In the resulting dataset, we tested relations between distinct dimensions of antisociality and heart rate, pre-ejection period (PEP), respiratory sinus arrhythmia, respiration rate, skin conductance levels, testosterone, basal cortisol, and the cortisol awakening response (CAR), and test the role of age throughout adolescence and young adulthood. RESULTS: Three dimensions of antisociality were uncovered: 'callous-unemotional (CU)/manipulative traits', 'intentional aggression/conduct', and 'reactivity/impulsivity/irritability'. Shorter PEPs and higher testosterone were related to CU/manipulative traits, and a higher CAR is related to both CU/manipulative traits and intentional aggression/conduct. These effects were stable across age. CONCLUSIONS: Across a heterogeneous sample and consistent across development, the CAR may be a valuable measure to link to CU/manipulative traits and intentional aggression, while sympathetic arousal and testosterone are additionally valuable to understand CU/manipulative traits. Together, these findings deepen our understanding of the fundamental mechanisms underlying different components of antisociality. Finally, we illustrate the potential of using current statistical techniques for combining multiple datasets to draw robust conclusions about biobehavioral associations.

Effectiveness of an online self-help program, expert-patient support, and their combination for eating disorders: Results from a randomized controlled trial.

OBJECTIVE: Many individuals with an eating disorder do not receive appropriate care. Low-threshold interventions could help bridge this treatment gap. The study aim was to evaluate the effectiveness of Featback, a fully automated online self-help intervention, online expert-patient support and their combination. METHOD: A randomized controlled trial with a 12-month follow-up period was conducted. Participants aged 16 or older with at least mild eating disorder symptoms were randomized to four conditions: (1) Featback, a fully automated online self-help intervention, (2) chat or email support from a recovered expert patient, (3) Featback with expert-patient support and (4) a waiting list control condition. The intervention period was 8 weeks and there was a total of six online assessments. The main outcome constituted reduction of eating disorder symptoms over time. RESULTS: Three hundred fifty five participants, of whom 43% had never received eating disorder treatment, were randomized. The three active interventions were superior to a waitlist in reducing eating disorder symptoms (d = -0.38), with no significant difference in effectiveness between the three interventions. Participants in conditions with expert-patient support were more satisfied with the intervention. DISCUSSION: Internet-based self-help, expert-patient support and their combination were effective in reducing eating disorder symptoms compared to a waiting list control condition. Guidance improved satisfaction with the internet intervention but not its effectiveness. Low-threshold interventions such as Featback and expert-patient support can reduce eating disorder symptoms and reach the large group of underserved individuals, complementing existing forms of eating disorder treatment. PUBLIC SIGNIFICANCE STATEMENT: Individuals with eating-related problems who received (1) a fully automated internet-based intervention, (2) chat and e-mail support by a recovered individual or (3) their combination, experienced stronger reductions in eating disorder symptoms than those who received (4) usual care. Such brief and easy-access interventions play an important role in reaching individuals who are currently not reached by other forms of treatment.

The Different Faces of (High) Sensitivity, Toward a More Comprehensive Measurement Instrument. Development and Validation of the Sensory Processing Sensitivity Questionnaire (SPSQ).

The main purpose of the study was the development of the Sensory Processing Sensitivity Questionnaire (SPSQ), designed to measure Sensory Processing Sensitivity, defined as a person's sensitivity to subtle stimuli, the depth with which these stimuli are processed, and its impact on emotional reactivity. The item pool generated for the development of the SPSQ consisted of 60 items. After exploratory factor analysis, 43 items remained, divided into six specific factors: (1) Sensory Sensitivity to Subtle Internal and External Stimuli, (2) Emotional and Physiological Reactivity, (3) Sensory Discomfort, (4) Sensory Comfort, (5) Social-Affective Sensitivity, and (6) Esthetic Sensitivity. Confirmatory factor analysis indicated that a higher-order bi-factor model consisting of two higher-order factors (a positive and negative dimension), a general sensitivity factor and six specific factors had the best fit. Strong positive associations were found between Emotional and Physiological Reactivity, the negative higher-order dimension, and Neuroticism; the same holds for the association between Esthetic Sensitivity, the positive higher-order dimension, and Openness. Emotional and Physiological Reactivity and the negative higher-order dimension showed clear associations with clinical outcomes. The relationships between the SPSQ and similar scales - the Highly Sensitive Person Scale and part of the Adult Temperament Questionnaire - were in the expected direction.

Assessing the Effects of a Real-Life Contact Intervention on Prejudice Toward LGBT People.

Prejudice against sexual and gender minorities (e.g., LGBT people) is quite prevalent and is harmful. We examined an existing-and often-used-contact intervention in pre-existing groups in an educational setting and assessed its effectiveness in reducing different forms of LGBT negativity. We focused particularly on modern LGBT negativity: a relatively subtle form of prejudice, involving ambivalence, denial, and/or the belief that there is too much attention for LGBT prejudice. We used a mixed design in which condition (experimental vs. control group) was the between-participants factor, which was randomized at the group level, and time (pretest vs. posttest vs. follow-up) was the within-participants factor (N = 117). Interventions were video recorded and the behavior of LGBT educators and participants was coded. Participants responded positively to the intervention, especially to the LGBT educator's "coming-out story." Exploratory analysis of the video data indicated that the perceived effectiveness of the intervention was higher in groups where participants were more engaged, although caution is necessary in interpreting this finding. The most important measure indicated that modern LGBT negativity decreased in the intervention groups directly after the intervention, but returned to baseline levels one week later. However, in the control condition, modern LGBT negativity had increased over time. Taken together, this suggests that an actual reduction in modern LGBT negativity was short-lived (i.e., the intervention effect disappeared within 7 days).

Predictors of quality of life among youths in foster care-a 5-year prospective follow-up study.

PURPOSE: Few studies have investigated possible predictors of positive outcomes for youths in foster care. The aim of this prospective follow-up study was to examine quality of life (QoL) among youths in foster care and to assess whether contextual and child factors predicted QoL. METHODS: Online questionnaires were completed by carers in Norway in 2012 (T1, n = 236, child age 6-12 years) and by youths and carers in 2017 (T2, n = 405, youth age 11-18 years). We received responses on 116 of the youths at both T1 and T2, and our final sample consisted of 525 youths with responses from T1 and/or T2. Child welfare caseworkers reported preplacement maltreatment and service use at T1. We assessed mental health and prosocial behavior at T1 by having carers complete the Strength and Difficulties Questionnaire and QoL at T2 with youth-reported KIDSCREEN-27. We analyzed the data using descriptive statistics, t-tests and multiple linear regressions, and we used multiple imputation to handle missing data. RESULTS: Youths in foster care had lower QoL across all dimensions compared to a Swedish general youth sample. QoL scores among our sample were similar to Norwegian youths with ill or substance abusing parents and to European norm data. Youths reported the highest QoL scores on the parent relations and autonomy dimension. Male gender, younger age, kinship care and prosocial behavior five years earlier predicted higher QoL. CONCLUSION: Similar to other at-risk youths, youths in foster care seem to have lower QoL than the general Scandinavian population. Despite early adversities, they had good relations with their current carers. Adolescent girls seem especially vulnerable to low QoL and might need extra support to have good lives in foster care.

Placebo Effects in the Neuroendocrine System: Conditioning of the Oxytocin Responses.

OBJECTIVE: There is evidence that placebo effects may influence hormone secretion. However, few studies have examined placebo effects in the endocrine system, including oxytocin placebo effects. We studied whether it is possible to trigger oxytocin placebo effects using a classical conditioning paradigm. METHODS: Ninety-nine women were assigned to a conditioned, control, or drug control group. In the two-phase conditioning paradigm, participants in the conditioned and drug control groups received an oxytocin nasal spray combined with a distinctive smell (conditioned stimulus [CS]) for three acquisition days, whereas the control group received placebo spray. Subsequently, the conditioned and control groups received placebo spray with the CS and the drug control group received oxytocin spray for three evocation days. Salivary oxytocin was measured several times during each day. Pain sensitivity and facial evaluation tests previously used in oxytocin research were also administered. RESULTS: On evocation day 1, in the conditioned group, oxytocin significantly increased from baseline to 5 minutes after CS (B[slope] = 19.55, SE = 5.88, p < .001) and remained increased from 5 to 20 (B = -10.42, SE = 5.81, p = .071) and 50 minutes (B = -0.70, SE = 3.37, p = .84). On evocation day 2, a trend for increase in oxytocin was found at 5 minutes (B = 15.22, SE = 8.14, p = .062). No placebo effect was found on evocation day 3 (B = 3.57, SE = 3.26, p = .28). Neither exogenous nor conditioned oxytocin affected pain or facial tasks. CONCLUSIONS: Results indicate that oxytocin release can be conditioned and that this response extinguishes over time. Triggering hormonal release by placebo manipulation offers various clinical possibilities, such as enhancing effects of pharmacological treatments or reducing dosages of medications. TRIAL REGISTRATION: The study was registered as a clinical trial on www.trialregister.nl (number NTR5596).

Heart work after errors: Behavioral adjustment following error commission involves cardiac effort.

Posterror slowing (PES) is the observation that people respond slower on trials subsequent to error commissions than on trials subsequent to correct responses. Different accounts have been proposed to explain PES. On the one hand, it has been suggested that PES arises from an adaptive increase in cognitive control following error commission, thereby making people more cautious after making an error. On the other hand, PES has been attributed to an orienting response, indicating that attention is shifted toward the error. In the present study we tested these accounts by investigating the effects of error commission in both flanker and switch tasks on two task-evoked cardiac measures: the interbeat interval-that is, the interval between two consecutive R peaks-and the RZ interval-that is, the interval between the R peak and the Z point-as measured using electro- and impedance cardiography, respectively. These measures allowed us to measure cardiac deceleration (autonomic orienting) and cardiac effort mobilization, respectively. Our results revealed a shorter RZ interval during posterror trials, indicating increased effort mobilization following errors. In addition, we replicated earlier studies that have shown cardiac slowing during error trials. However, multilevel analyses showed that only the posterror decrease in RZ interval predicted posterror reaction times, whereas there was no positive relationship between error-related cardiac deceleration and posterror reaction times. Our results suggest that PES is related to increased cardiac effort, supporting a cognitive-control account of PES.

Psychological distress and cognitive coping in pregnant women diagnosed with cancer and their partners.

OBJECTIVE: A cancer diagnosis during pregnancy may be considered as an emotional challenge for pregnant women and their partners. We aimed to identify women and partners at risk for high levels of distress based on their coping profile. METHODS: Sixty-one pregnant women diagnosed with cancer and their partners filled out the Cognitive Emotion Regulation Questionnaire (CERQ) and the newly constructed Cancer and Pregnancy Questionnaire (CPQ). K-means cluster analysis was performed on the CERQ scales. Scores on the CPQ were compared between the women and their partners and between the CERQ-clusters. RESULTS: Comparison of women and partners on the CPQ did not reveal significant differences on distress about the child's health, the cancer disease, and the pregnancy or on information satisfaction (P = .16, P = .44, P = .50, and P = .47, respectively). However, women were more inclined to maintain the pregnancy than their partners (P = .011). Three clusters were retrieved based on the CERQ scales, characterized by positive coping, internalizing coping, and blaming. Women and partners using internalizing strategies had significantly higher scores on concerns about the child's health (P = .039), the disease and treatment (P < .001), and the pregnancy and delivery (P = .009) compared with positive and blaming strategies. No cluster differences were found for information satisfaction (P = .71) and tendency to maintain the pregnancy (P = .35). CONCLUSION: Women and partners using internalizing coping strategies deal with the highest levels of distress and may benefit from additional psychosocial support.

MultiLevel simultaneous component analysis: A computational shortcut and software package.

MultiLevel Simultaneous Component Analysis (MLSCA) is a data-analytical technique for multivariate two-level data. MLSCA sheds light on the associations between the variables at both levels by specifying separate submodels for each level. Each submodel consists of a component model. Although MLSCA has already been successfully applied in diverse areas within and outside the behavioral sciences, its use is hampered by two issues. First, as MLSCA solutions are fitted by means of iterative algorithms, analyzing large data sets (i.e., data sets with many level one units) may take a lot of computation time. Second, easily accessible software for estimating MLSCA models is lacking so far. In this paper, we address both issues. Specifically, we discuss a computational shortcut for MLSCA fitting. Moreover, we present the MLSCA package, which was built in MATLAB, but is also available in a version that can be used on any Windows computer, without having MATLAB installed.

Temperament subtypes in treatment seeking obese individuals: a latent profile analysis.

OBJECTIVE: This study aimed to investigate temperament subtypes in obese patients. METHODS: Ninety-three bariatric surgery candidates and 63 obese inpatients from a psychotherapy unit answered the Behavioral Inhibition System/Behavioral Activation System Scale (BIS/BAS), the Effortful Control subscale of the Adult Temperament Questionnaire-Short Form (ATQ-EC), and questionnaires for eating disorder, depressive and attention deficit hyperactivity disorder (ADHD) symptoms and completed neurocognitive testing for executive functions. Binge eating disorder and impulse control disorders were diagnosed using interviews. RESULTS: A latent profile analysis using BIS/BAS and ATQ-EC scores revealed a 'resilient/high functioning' cluster (n = 88) showing high ATQ-EC and low BIS/BAS scores and an 'emotionally dysregulated/undercontrolled' cluster (n = 68) with low ATQ-EC and high BIS/BAS scores. Patients from the 'emotionally dysregulated/undercontrolled' cluster showed more eating disorder, depressive and ADHD symptoms, and poorer performance in the labyrinth task. CONCLUSION: The findings support the assumptions regarding the heterogeneity of obesity and the association between temperament subtypes and psychopathology.

Performing DISCO-SCA to search for distinctive and common information in linked data.

Behavioral researchers often obtain information about the same set of entities from different sources. A main challenge in the analysis of such data is to reveal, on the one hand, the mechanisms underlying all of the data blocks under study and, on the other hand, the mechanisms underlying a single data block or a few such blocks only (i.e., common and distinctive mechanisms, respectively). A method called DISCO-SCA has been proposed by which such mechanisms can be found. The goal of this article is to make the DISCO-SCA method more accessible, in particular for applied researchers. To this end, first we will illustrate the different steps in a DISCO-SCA analysis, with data stemming from the domain of psychiatric diagnosis. Second, we will present in this article the DISCO-SCA graphical user interface (GUI). The main benefits of the DISCO-SCA GUI are that it is easy to use, strongly facilitates the choice of model selection parameters (such as the number of mechanisms and their status as being common or distinctive), and is freely available.

CHull: a generic convex-hull-based model selection method.

When analyzing data, researchers are often confronted with a model selection problem (e.g., determining the number of components/factors in principal components analysis [PCA]/factor analysis or identifying the most important predictors in a regression analysis). To tackle such a problem, researchers may apply some objective procedure, like parallel analysis in PCA/factor analysis or stepwise selection methods in regression analysis. A drawback of these procedures is that they can only be applied to the model selection problem at hand. An interesting alternative is the CHull model selection procedure, which was originally developed for multiway analysis (e.g., multimode partitioning). However, the key idea behind the CHull procedure--identifying a model that optimally balances model goodness of fit/misfit and model complexity--is quite generic. Therefore, the procedure may also be used when applying many other analysis techniques. The aim of this article is twofold. First, we demonstrate the wide applicability of the CHull method by showing how it can be used to solve various model selection problems in the context of PCA, reduced K-means, best-subset regression, and partial least squares regression. Moreover, a comparison of CHull with standard model selection methods for these problems is performed. Second, we present the CHULL software, which may be downloaded from http://ppw.kuleuven.be/okp/software/CHULL/, to assist the user in applying the CHull procedure.

CHull as an alternative to AIC and BIC in the context of mixtures of factor analyzers.

Mixture analysis is commonly used for clustering objects on the basis of multivariate data. When the data contain a large number of variables, regular mixture analysis may become problematic, because a large number of parameters need to be estimated for each cluster. To tackle this problem, the mixtures-of-factor-analyzers (MFA) model was proposed, which combines clustering with exploratory factor analysis. MFA model selection is rather intricate, as both the number of clusters and the number of underlying factors have to be determined. To this end, the Akaike (AIC) and Bayesian (BIC) information criteria are often used. AIC and BIC try to identify a model that optimally balances model fit and model complexity. In this article, the CHull (Ceulemans & Kiers, 2006) method, which also balances model fit and complexity, is presented as an interesting alternative model selection strategy for MFA. In an extensive simulation study, the performances of AIC, BIC, and CHull were compared. AIC performs poorly and systematically selects overly complex models, whereas BIC performs slightly better than CHull when considering the best model only. However, when taking model selection uncertainty into account by looking at the first three models retained, CHull outperforms BIC. This especially holds in more complex, and thus more realistic, situations (e.g., more clusters, factors, noise in the data, and overlap among clusters).

Clusterwise Independent Component Analysis (C-ICA): Using fMRI resting state networks to cluster subjects and find neurofunctional subtypes.

BACKGROUND: FMRI resting state networks (RSNs) are used to characterize brain disorders. They also show extensive heterogeneity across patients. Identifying systematic differences between RSNs in patients, i.e. discovering neurofunctional subtypes, may further increase our understanding of disease heterogeneity. Currently, no methodology is available to estimate neurofunctional subtypes and their associated RSNs simultaneously. NEW METHOD: We present an unsupervised learning method for fMRI data, called Clusterwise Independent Component Analysis (C-ICA). This enables the clustering of patients into neurofunctional subtypes based on differences in shared ICA-derived RSNs. The parameters are estimated simultaneously, which leads to an improved estimation of subtypes and their associated RSNs. RESULTS: In five simulation studies, the C-ICA model is successfully validated using both artificially and realistically simulated data (N = 30-40). The successful performance of the C-ICA model is also illustrated on an empirical data set consisting of Alzheimer's disease patients and elderly control subjects (N = 250). C-ICA is able to uncover a meaningful clustering that partially matches (balanced accuracy = .72) the diagnostic labels and identifies differences in RSNs between the Alzheimer and control cluster. COMPARISON WITH OTHER METHODS: Both in the simulation study and the empirical application, C-ICA yields better results compared to competing clustering methods (i.e., a two step clustering procedure based on single subject ICA's and a Group ICA plus dual regression variant thereof) that do not simultaneously estimate a clustering and associated RSNs. Indeed, the overall mean adjusted Rand Index, a measure for cluster recovery, equals 0.65 for C-ICA and ranges from 0.27 to 0.46 for competing methods. CONCLUSIONS: The successful performance of C-ICA indicates that it is a promising method to extract neurofunctional subtypes from multi-subject resting state-fMRI data. This method can be applied on fMRI scans of patient groups to study (neurofunctional) subtypes, which may eventually further increase understanding of disease heterogeneity.

A flexible framework for sparse simultaneous component based data integration.

BACKGROUND: High throughput data are complex and methods that reveal structure underlying the data are most useful. Principal component analysis, frequently implemented as a singular value decomposition, is a popular technique in this respect. Nowadays often the challenge is to reveal structure in several sources of information (e.g., transcriptomics, proteomics) that are available for the same biological entities under study. Simultaneous component methods are most promising in this respect. However, the interpretation of the principal and simultaneous components is often daunting because contributions of each of the biomolecules (transcripts, proteins) have to be taken into account. RESULTS: We propose a sparse simultaneous component method that makes many of the parameters redundant by shrinking them to zero. It includes principal component analysis, sparse principal component analysis, and ordinary simultaneous component analysis as special cases. Several penalties can be tuned that account in different ways for the block structure present in the integrated data. This yields known sparse approaches as the lasso, the ridge penalty, the elastic net, the group lasso, sparse group lasso, and elitist lasso. In addition, the algorithmic results can be easily transposed to the context of regression. Metabolomics data obtained with two measurement platforms for the same set of Escherichia coli samples are used to illustrate the proposed methodology and the properties of different penalties with respect to sparseness across and within data blocks. CONCLUSION: Sparse simultaneous component analysis is a useful method for data integration: First, simultaneous analyses of multiple blocks offer advantages over sequential and separate analyses and second, interpretation of the results is highly facilitated by their sparseness. The approach offered is flexible and allows to take the block structure in different ways into account. As such, structures can be found that are exclusively tied to one data platform (group lasso approach) as well as structures that involve all data platforms (Elitist lasso approach). AVAILABILITY: The additional file contains a MATLAB implementation of the sparse simultaneous component method.

Simultaneous analysis of coupled data matrices subject to different amounts of noise.

In many areas of science, research questions imply the analysis of a set of coupled data blocks, with, for instance, each block being an experimental unit by variable matrix, and the variables being the same in all matrices. To obtain an overall picture of the mechanisms that play a role in the different data matrices, the information in these matrices needs to be integrated. This may be achieved by applying a data-analytic strategy in which a global model is fitted to all data matrices simultaneously, as in some forms of simultaneous component analysis (SCA). Since such a strategy implies that all data entries, regardless the matrix they belong to, contribute equally to the analysis, it may obfuscate the overall picture of the mechanisms underlying the data when the different data matrices are subject to different amounts of noise. One way out is to downweight entries from noisy data matrices in favour of entries from less noisy matrices. Information regarding the amount of noise that is present in each matrix, however, is, in most cases, not available. To deal with these problems, in this paper a novel maximum-likelihood-based simultaneous component analysis method, referred to as MxLSCA, is proposed. Being a stochastic extension of SCA, in MxLSCA the amount of noise in each data matrix is estimated and entries from noisy data matrices are downweighted. Both in an extensive simulation study and in an application to data stemming from cross-cultural emotion psychology, it is shown that the novel MxLSCA strategy outperforms the SCA strategy with respect to disclosing the mechanisms underlying the coupled data.

ADPROCLUS: a graphical user interface for fitting additive profile clustering models to object by variable data matrices.

In many areas of psychology, one is interested in disclosing the underlying structural mechanisms that generated an object by variable data set. Often, based on theoretical or empirical arguments, it may be expected that these underlying mechanisms imply that the objects are grouped into clusters that are allowed to overlap (i.e., an object may belong to more than one cluster). In such cases, analyzing the data with Mirkin's additive profile clustering model may be appropriate. In this model: (1) each object may belong to no, one or several clusters, (2) there is a specific variable profile associated with each cluster, and (3) the scores of the objects on the variables can be reconstructed by adding the cluster-specific variable profiles of the clusters the object in question belongs to. Until now, however, no software program has been publicly available to perform an additive profile clustering analysis. For this purpose, in this article, the ADPROCLUS program, steered by a graphical user interface, is presented. We further illustrate its use by means of the analysis of a patient by symptom data matrix.

Hybrid Harmony: A Multi-Person Neurofeedback Application for Interpersonal Synchrony.

Recent years have seen a dramatic increase in studies measuring brain activity, physiological responses, and/or movement data from multiple individuals during social interaction. For example, so-called "hyperscanning" research has demonstrated that brain activity may become synchronized across people as a function of a range of factors. Such findings not only underscore the potential of hyperscanning techniques to capture meaningful aspects of naturalistic interactions, but also raise the possibility that hyperscanning can be leveraged as a tool to help improve such naturalistic interactions. Building on our previous work showing that exposing dyads to real-time inter-brain synchrony neurofeedback may help boost their interpersonal connectedness, we describe the biofeedback application Hybrid Harmony, a Brain-Computer Interface (BCI) that supports the simultaneous recording of multiple neurophysiological datastreams and the real-time visualization and sonification of inter-subject synchrony. We report results from 236 dyads experiencing synchrony neurofeedback during naturalistic face-to-face interactions, and show that pairs' social closeness and affective personality traits can be reliably captured with the inter-brain synchrony neurofeedback protocol, which incorporates several different online inter-subject connectivity analyses that can be applied interchangeably. Hybrid Harmony can be used by researchers who wish to study the effects of synchrony biofeedback, and by biofeedback artists and serious game developers who wish to incorporate multiplayer situations into their practice.