Raoul Grasman

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Verified

Raoul verified their affiliation via an institutional email.

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• PhD
• Professor at University of Amsterdam

Purpose Cognitive vulnerability to anxiety can partly be explained by an interplay of attentional biases and control processes. This suggests that when aiming to reduce anxiety, simultaneously reducing an attentional bias for threat and strengthening control processes would be the optimal approach. We investigated whether a combined web-based Atten...

In this study, we propose to use Multidimensional Item Response Theory (MIRT) to model the rating matrix in a recommendation context. In contrast to most existing recommender systems, this enables an explainable low-dimensional latent profile and offers to efficiently explore user preferences through computerized adaptive testing (CAT). We use elas...

Recently Variational Autoencoders (VAEs) have been proposed as a method to estimate high dimensional Item Response Theory (IRT) models on large datasets. Although these improve the efficiency of estimation drastically compared to traditional methods, they have no natural way to deal with missing values. In this paper, we adapt three existing method...

Objective Motivation is considered a key factor in successful treatment. Unfortunately, detained youth typically show lower motivation for treatment and behavioral change. This pilot study examined the effects of a brief Motivational Interviewing (MI) protocol in conjunction with a Cognitive Bias Modification (CBM) intervention aimed at reducing su...

Background: Group schema therapy (GST) is increasingly popular as a treatment for personality disorders (PDs), including Cluster-C PDs. Individual ST has proven to be effective for Cluster-C PD patients, while the evidence for GST is limited. This study aimed to investigate the effectiveness of GST for Cluster-C PD. Moreover, differ- ences between...

A popular approach to recommender systems is to factor the ratingmatrix into two low rank matrices. Although this has proven to be agood way to predict ratings, the component scores in these matrix fac-torisation methods don’t serve as estimates of the underlying causesof the ratings. We propose to model the rating matrix using Multidi-mensional It...

Background: Dropout from psychotherapy for borderline personality disorder (BPD) is a notorious problem. We investigated whether treatment, treatment format, treatment setting, substance use exclusion criteria, proportion males, mean age, country, and other variables influenced dropout. Methods: From Pubmed, Embase, Cochrane, Psycinfo and other...

Despite it being widely acknowledged that the most important function of memory is to facilitate the prediction of significant events in a complex world, no studies to date have investigated how our ability to infer associations across distinct but overlapping experiences is affected by the inclusion of threat memories. To address this question, pa...

Background Given the high prevalence of Cluster-C Personality Disorders (PDs) in clinical populations, disease burden, high societal costs and poor prognosis of comorbid disorders, a major gain in health care can be achieved if Cluster-C PDs are adequately treated. The only controlled cost-effectiveness study published so far found Individual Schem...

AI large language models have (co-)produced amazing written works from newspaper articles to novels and poetry. These works meet the standards of the standard definition of creativity: being original and useful, and sometimes even the additional element of surprise. But can a large language model designed to predict the next text fragment provide c...

Background Specialized evidence-based treatments have been developed and evaluated for borderline personality disorder (BPD), including Dialectical Behavior Therapy (DBT) and Schema Therapy (ST). Individual differences in treatment response to both ST and DBT have been observed across studies, but the factors driving these differences are largely u...

Freezing to impending threat is a core defensive response. It has been studied primarily using fear conditioning in non-human animals, thwarting advances in translational human anxiety research that has used other indices, such as skin conductance responses. Here we examine postural freezing as a human conditioning index for translational anxiety r...

Citation: Rameckers, S.A.; Verhoef, R.E.J.; Grasman, R.P.P.P.; Cox, W.R.; van Emmerik, A.A.P.; Engelmoer, I.M.; Arntz, A. Effectiveness of Psychological Treatments for Borderline Personality Disorder and Predictors of Treatment Outcomes: A Multivariate Multilevel Meta-Analysis of Data from All Design Types. Abstract: We examined the effectiveness...

Introduction Web-based smoking interventions hold potential for smoking cessation; however, many of them report low intervention usage (i.e., high levels of non-usage attrition). One strategy to counter this issue is to tailor such interventions to user subtypes if these can be identified and related to non-usage attrition outcomes. The aim of this...

Moderate alcohol intake may impair stimulus-driven inhibition of motor actions in go/no-go and stop-signal tasks. Exposure to alcohol-related cues has been found to exacerbate this impairment. By contrast, the effect of alcohol use on intentional inhibition, or the capacity to voluntarily suspend an action, has rarely been investigated. We examined...

Freezing to impending threat is a core defensive response. It has been studied primarily using fear-conditioning in non-human animals, thwarting advances in translational human anxiety-research. Here we examine postural freezing as a human conditioning-index for translational anxiety-research. We show (n=28) that human freezing is highly sensitive...

To successfully predict important events, the representations in memory on which we rely need to be constantly updated and transformed to best reflect a complex and dynamic world. Here we employed a novel paradigm to investigate how memories of threat learning affect the flexible recombination across distinct but overlapping experiences, an ability...

Background: Automatically activated cognitive motivational processes such as the tendency to attend to or approach smoking-related stimuli (ie, attentional and approach bias) have been related to smoking behaviors. Therefore, these cognitive biases are thought to play a role in maintaining smoking behaviors. Cognitive biases can be modified with co...

Background: Automatically activated cognitive motivational processes such as the tendency to attend to or approach smoking-related stimuli (ie, attentional and approach bias) have been related to smoking behaviors. Therefore, these cognitive biases are thought to play a role in maintaining smoking behaviors. Cognitive biases can be modified with c...

Objective: We aimed to empirically test whether schema modes are central to the change process in schema therapy, clarification-oriented psychotherapy, and treatment as usual, i.e., predictive of personality pathology, and global and social-occupational functioning. Method: A multicenter randomized controlled trial was conducted (N = 139 men, N...

Background and Objectives : In standard practice, sleep is classified into distinct stages by human observers according to specific rules as for instance specified in the AASM manual. We here show proof of principle for a conceptualization of sleep stages as attractor states in a nonlinear dynamical system in order to develop new empirical criteria...

BACKGROUND Automatically activated cognitive motivational processes such as the tendency to attend to or approach smoking-related stimuli (i.e., attentional and approach bias) have been related to smoking behaviors. Therefore, these cognitive biases are thought to play a role in maintaining smoking behaviors. Cognitive biases can be modified with C...

Research on money priming typically investigates whether exposure to money-related stimuli can affect people’s thoughts, feelings, motivations, and behaviors (for a review, see Vohs, 2015). Our study answers the call for a comprehensive meta-analysis examining the available evidence on money priming (Vadillo, Hardwicke, & Shanks, 2016). By conducti...

There is currently a lack of a 'Gold Standard' for quantification and modelling of the Pivot Shift test (PST) in anterior cruciate ligament (ACL) deficient knees. A sudden change in state resulting from a small change in a parameter is characteristic of systems that can be modelled using catastrophe theory. Analysis of data obtained from 50 consecu...

From a biopsychological perspective, social anxiety can be seen as a proneness to act submissively in order to reduce conflict and avoid rejection by others. Research into this framework so far specifically focused on the self-perceived social world. Less is known about the relation between social anxiety and one's actual position within a group, a...

In a unidimensional factor model it is assumed that the set of indicators that loads on this factor are conditionally independent given the latent factor. Two indicators are, however, never conditionally independent given (a set of) other indicators that load on this factor, as this would require one of the indicators that is conditioned on to corr...

Most data analyses rely on models. To complement statistical models, psychologists have developed cognitive models, which translate observed variables into psychologically interesting constructs. Response time models, in particular, assume that response time and accuracy are the observed expression of latent variables including 1) ease of processin...

We present a data set containing 705 between-study heterogeneity estimates τ2 as reported in 61 articles published in 'Psychological Bulletin' from 1990–2013. The data set also includes information about the number and type of effect sizes, the 'Q'- and 'I'2-statistics, and publication bias. The data set is stored in the Open Science Framework repo...

In many statistical applications that concern mathematical psychologists, the concept of Fisher information plays an important role. In this tutorial we clarify the concept of Fisher information as it manifests itself across three different statistical paradigms. First, in the frequentist paradigm, Fisher information is used to construct hypothesis...

In many statistical applications that concern mathematical psychologists, the concept of Fisher information plays an important role. In this tutorial we clarify the concept of Fisher information as it manifests itself across three different statistical paradigms. First, in the frequentist paradigm, Fisher information is used to construct hypothesis...

We present a data set containing 705 between-study heterogeneity estimates as reported in 61 articles published in Psychological Bulletin from 1990-2013. The data set also includes information about the number and type of effect sizes, the Q-statistic, and publication bias. The data set is stored in the Open Science Framework repository and can be...

In a unidimensional factor model it is assumed that the set of indicators that loads on this factor are conditionally independent given the latent factor. Two indicators are, however, never conditionally independent given (a set of) other indicators that load on this factor, as this would require one of the indicators that is conditioned on to corr...

The current study aimed to shed more light on the role of dopamine in temporal attention. To this end, we pharmacologically manipulated dopamine levels in a large sample of Parkinson's disease patients (n=63) while they performed an attentional blink (AB) task in which they had to identify two targets (T1 and T2) presented in close temporal proximi...

Time series analysis is a technique that can be used to analyze the data from a single subject and has great potential to investigate clinically relevant processes like affect regulation. This article uses time series models to investigate the assumed dysregulation of affect that is associated with bipolar disorder. By formulating a number of alter...

This study deals with addictive acts that exhibit a stable pattern not intervening with the normal routine of daily life. Nevertheless, in the long term such behaviour may result in health damage. Alcohol consumption is an example of such addictive habit. The aim is to describe the process of addiction as a dynamical system in the way this is done...

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Introduction: In neuropsychological research and clinical practice, a large battery of tests is often administered to determine whether an individual deviates from the norm. We formulate three criteria for such large battery normative comparisons. First, familywise false-positive error rate (i.e., the complement of specificity) should be controlle...

Multilevel autoregressive models are especially suited for modeling between-person differences in within-person processes. Fitting these models with Bayesian techniques requires the specification of prior distributions for all parameters. Often it is desirable to specify prior distributions that have negligible effects on the resulting parameter es...

Catastrophe theory describes how small, continuous changes in control parameters (i.e., independent variables that influence the state of a system) can have sudden, discontinuous effects on dependent variables. Such discontinuous, jumplike changes are called phase-transitions or catastrophes. Examples include the sudden collapse of a bridge under s...

The current article aims to provide an up-to-date synopsis of available techniques to study affect dynamics using intensive longitudinal data (ILD). We do so by introducing the following eight dichotomies that help elucidate what kind of data one has, what process aspects are of interest, and what research questions are being considered: (1) single...

Catastrophe theory (Thom, 1972, 1993) is the study of the many ways in which continuous changes in a system's parameters can result in discontinuous changes in 1 or several outcome variables of interest. Catastrophe theory-inspired models have been used to represent a variety of change phenomena in the realm of social and behavioral sciences. Despi...

The cross-lagged panel model (CLPM) is believed by many to overcome the problems associated with the use of cross-lagged correlations as a way to study causal influences in longitudinal panel data. The current article, however, shows that if stability of constructs is to some extent of a trait-like, time-invariant nature, the autoregressive relatio...

Whether level 1 predictors should be centered per cluster has received considerable attention in the multilevel literature. While most agree that there is no one preferred approach, it has also been argued that cluster mean centering is desirable when the within-cluster slope and the between-cluster slope are expected to deviate, and the main inter...

Many empirical researchers do not realize that the common multiway analysis of variance (ANOVA) harbors a multiple comparison problem. In the case of two factors, three separate null hypotheses are subject to test (i.e., two main effects and one interaction). Consequently, the probability of at least one Type I error (if all null hypotheses are tru...

In management research, empirical data are often analyzed using p-value null hypothesis significance testing (pNHST). Here we outline the conceptual and practical advantages of an alternative analysis method: Bayesian hypothesis testing and model selection using the Bayes factor. In contrast to pNHST, Bayes factors allow researchers to quantify evi...

The intake of nicotine by smoking cigarettes is modelled by a dynamical system of differential equations. The variables are the internal level of nicotine and the level of craving. The model is based on the dynamics of neural receptors and the way they enhance craving. Lighting of a cigarette is parametrised by a time-dependent Poisson process. The...

Stationary and non-stationary Poisson processes (DOCX)

People generally prefer their initials to the other letters of the alphabet, a phenomenon known as the name-letter effect. This effect, researchers have argued, makes people move to certain cities, buy particular brands of consumer products, and choose particular professions (e.g., Angela moves to Los Angeles, Phil buys a Philips TV, and Dennis bec...

The amplitude and latency of single-trial EEG/MEG signals may provide valuable information concerning human brain functioning. In this article we propose a new method to reliably estimate single-trial amplitude and latency of EEG/MEG signals. The advantages of the method are fourfold. First, no a-priori specified template function is required. Seco...

This article presents a Bayesian hypothesis test for ANOVA designs. The test is an application of standard Bayesian methods for variable selection in regression models. We illustrate the effect of various g-priors on the ANOVA hypothesis test. The Bayesian test for ANOVA designs is useful for empirical researchers and for students; both groups will...

Many psychological processes are characterized by recurrent shifts between distinct regimes or states. Examples that are considered in this paper are the switches between different states associated with premenstrual syndrome, hourly fluctuations in affect during a major depressive episode, and shifts between a “hot hand” and a “cold hand” in a top...

The popular random-dot motion (RDM) task has recently been applied to multiple-choice perceptual decision-making. However, changes in the number of alternatives on an RDM display lead to changes in the similarity between the alternatives, complicating the study of multiple-choice effects. To disentangle the effects of similarity and number of alter...

Piéron’s Law is a psychophysical regularity in signal detection tasks that states that mean response times decrease as a power function of stimulus intensity. In this article, we extend Piéron’s Law to perceptual two-choice decision-making tasks, and demonstrate that the law holds as the discriminability between two competing choices is manipulated...

In standard fMRI analysis all voxels are tested in a massive univariate approach, that is, each voxel is tested independently. This requires stringent corrections for multiple comparisons to control the number of false positive tests (i.e., marking voxels as active while they are actually not). As a result, fMRI analyses may su�er from low power to...

The relationship between linear factor models and latent profile models is addressed within the context of maximum likelihood estimation based on the joint distribution of the manifest variables. Although the two models are well known to imply equivalent covariance decompositions, in general they do not yield equivalent estimates of the uncondition...

Connectivity analysis of fMRI data requires correct specification of regions-of-interest (ROIs). Selection of ROIs based on outcomes of a GLM analysis may be hindered by conservativeness of the multiple comparison correction, while selection based on brain anatomy may be biased due to inconsistent structure-to-function mapping. To alleviate these p...

An encompassing prior (EP) approach to facilitate Bayesian model selection for nested models with inequality constraints has been previously proposed. In this approach, samples are drawn from the prior and posterior distributions of an encompassing model that contains an inequality restricted version as a special case. The Bayes factor in favor of...

In the field of cognitive psychology, the p-value hypothesis test has established a stranglehold on statistical reporting. This is unfortunate, as the p-value provides at best a rough estimate of the evidence that the data provide for the presence of an experimental effect. An alternative and arguably more appropriate measure of evidence is conveye...

Crawford and Howell (1998) have pointed out that the common practice of z-score inference on cognitive disability is inappropriate if a patient's performance on a task is compared with relatively few typical control individuals. Appropriate univariate and multivariate statistical tests have been proposed for these studies, but these are only valid...

Many psychological processes are characterized by recurrent shifts between different states. To model these processes at the level of the individual, regime-switching models may prove useful. In this chapter we discuss two of these models: the threshold autoregressive model and the Markov switching autoregressive model. We discuss their main featur...

We consider power calculation in structural equation modeling with data missing completely at random (MCAR). Muthén and Muthén (2002) recently demonstrated how power calculations with data MCAR can be carried out by means of a Monte Carlo study. Here we show that the method of Satorra and Saris (1985), which is based on the nonnull distribution of...

Of the seven elementary catastrophes in catastrophe theory, the “cusp” model is the most widely applied. Most applications are however qualitative. Quantitative techniques for catastrophe modeling have been developed, but so far the limited availability of flexible software has hindered quantitative assessment. We present a package that implements...

We give closed form expressions for the mean and variance of RTs for Ratcliff’s diffusion model [Ratcliff, R. (1978). A theory of memory retrieval. Psychological Review, 85, 59–108] under the simplifying assumption that there is no variability across trials in the parameters. These expressions are more general than those currently available. As an...

Of the seven elementary catastrophes in catastrophe theory, the âÂÂcuspâ model is the most widely applied. Most applications are however qualitative. Quantitative techniques for catastrophe modeling have been developed, but so far the limited availability of flexible software has hindered quantitative assessment. We present a package that imple...

In this rejoinder, we address two of Ratcliff's main concerns with respect to the EZ-diffusion model (Ratcliff, 2008). First, we introduce "robust-EZ," a mixture model approach to achieve robustness against the presence of response contaminants that might otherwise distort parameter estimates. Second, we discuss an extension of the EZ model that al...

The potential for response variability to serve as an endophenotype for attention deficit hyperactivity disorders (ADHD) rests, in part, upon the development of reliable and valid methods to decompose variability. This study investigated the specificity of intra-individual variability (IIV) in 53 children with ADHD by comparing them with 25 childre...

In neuropsychological evaluations and single case research generally a number of tests are administered, since the interest is not in a single, but in multiple characteristics of a patient. The typical problem is to decide whether or not a patient is different from normal controls with respect to one or more of these characteristics. Consideration...

The EZ-diffusion model for two-choice response time tasks takes mean response time, the variance of response time, and response accuracy as inputs. The model transforms these data via three simple equations to produce unique values for the quality of information, response conservativeness, and nondecision time. This transformation of observed data...

Scores on cognitive tasks used in intelligence tests correlate positively with each other, that is, they display a positive manifold of correlations. The positive manifold is often explained by positing a dominant latent variable, the g factor, associated with a single quantitative cognitive or biological process or capacity. In this article, a new...

To test whether the model fits the data well, a goodness-of-fit (GOF) test can be used. The chi-square GOF test is often used to test the null hypothesis that a func-tion describes the mean of the data well. The null hypothesis with this test is re-jected too often, however, because the nominal significance level (usually 0.05) is exceeded. Alterna...

Hypothesis testing in distributed source models for the electro- or magnetoencephalogram is generally performed for each voxel separately. Derived from the analysis of functional magnetic resonance imaging data, such a statistical parametric map (SPM) ignores the spatial smoothing in hypothesis testing with distributed source models. For example, w...

Catastrophe theory is a mathematical formalism for modeling nonlinear systems whose discontinuous behavior is determined by smooth changes in a small number of driving parameters. Fitting a catastrophe model to noisy data constitutes a serious challenge, however, because catastrophe theory was formulated specifically for deterministic systems. Lore...

By using signal processing techniques, an estimate of activity in the brain from the electro- or magneto-encephalogram (EEG or MEG) can be obtained. For a proper analysis, a test is required to indicate whether the model for brain activity fits. A problem in using such tests is that often, not all assumptions are satisfied, like the assumption of t...

Maximum likelihood estimation of emitter source parameters in array signal processing for stochastic source signals is well established in the literature. Currently available results in the literature however, have relied on the assumption that the array response matrix has full column rank. In certain models used in for example chemistry, telecomm...

Almost every empirical psychological study finds that the variance of a response time (RT) distribution increases with the mean. Here we present a theoretical analysis of the nature of the relationship between RT mean and RT variance, based on the assumption that a diffusion model (e.g., Ratcliff (1978) Psychological Review, 85, 59–108; Ratcliff (2...

Several methods [model selection procedures (MSPs)] to determine the number of sources in electroencephalogram (EEG) and magnetoencphalogram (MEG) data have previously been investigated in an instantaneous analysis. In this paper, these MSPs are extended to a spatio-temporal analysis if possible. It is seen that the residual variance (RV) tends to...

In [R.P.P.P. Grasman et al., Frequency domain simultaneous source and source coherence estimation with an application to MEG, IEEE Trans. Biomed. Eng. 51 (1) (2004) 45–55] we proposed to analyze cross-spectrum matrices obtained from electro- or magnetoencephalographic (EEG/MEG) signals, to obtain estimates of the EEG/MEG sources and their coherence...

In electromagnetic source analysis it is necessary to determine how many sources are required to describe the EEG or MEG adequately. Model selection procedures (MSP's, or goodness of fit procedures) give an estimate of the required number of sources. Existing and new MSP's are evaluated in di#erent source and noise settings: two sources which are c...

In [1] we proposed to analyze cross-spectrum matrices obtained from electro- or magneto-encephalographic (EEG/MEG) signals, to obtain estimates of the EEG/MEG sources and their coherence. In this paper we extend this method in two ways. First, by modelling such interactions as linear filters, and second, by taking the mean of the signals across dif...

Interactions between cortical areas are crucial for cognitive functioning. Methods currently in use to assess such interactions are not well suited for this task because they lack timing precision, localization precision, or both. We present a method for simultaneous estimation of source location and orientation parameters and cross-spectral parame...

A method is described for finding alternative spatial linear filters in the neuro-electromagnetic inverse problem. The method is based both on the theory of es-timable functions in over parameterized regression models, and on the idea of finding linear combinations that have "least complex" interpretation. Measures of complexity were obtained from...

In electromagnetic source analysis, it is necessary to determine how many sources are required to describe the electroencephalogram or magnetoencephalogram adequately. Model selection procedures (MSPs) or goodness of fit procedures give an estimate of the required number of sources. Existing and new MSPs are evaluated in different source and noise...

A method is described to incorporate the spatiotemporal noise covariance matrix into a spatiotemporal source analysis. The essential feature is that the estimation problem is split into two parts. First, a model is fitted to the observed noise covariance matrix. This model is a Kronecker product of a spatial and a temporal matrix. The spatial matri...

This paper examines with a simulation study to what extent the results of criteria on linear models are robust to nonlinear functions