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  • Francis Tuerlinckx
Francis Tuerlinckx

Francis Tuerlinckx
KU Leuven | ku leuven · Research unit for Quantitative Psychology and Individual Differences

About

188
Publications
108,929
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15,295
Citations
Citations since 2017
54 Research Items
11690 Citations
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201720182019202020212022202305001,0001,5002,000
201720182019202020212022202305001,0001,5002,000
Introduction

Publications

Publications (188)
Preprint
Retrospective analyses of experience sampling (ESM) data have shown that changes in mean and variance levels may serve as early warning signs of an imminent depression. Detecting such early warning signs prospectively would pave the way for timely intervention and prevention. The exponentially weighted moving average (EWMA) procedure seems a promis...
Preprint
Full-text available
Human generalization research aims to understand the processes underlying the transfer of prior experiences to new contexts. Generalization research predominantly relies on descriptive statistics, assumes a single generalization mechanism, interprets generalization from mono-source data, and disregards individual differences. Unfortunately, such an...
Article
Emotion researchers that use experience sampling methods (ESM) study how emotions fluctuate in everyday life. To reach valid conclusions, confirming the reliability of momentary emotion measurements is essential. However, to minimize participant burden, ESM researchers often use single-item measures, preventing a reliability assessment of people's...
Article
The way in which emotional experiences change over time can be studied through the use of computational models. An important question with regard to such models is which characteristics of the data a model should account for in order to adequately describe these data. Recently, attention has been drawn on the potential importance of nonlinearity as...
Preprint
Full-text available
In this paper, we present m-Path (www.m-Path.io), an online platform that provides a user-friendly and flexible framework for implementing smartphone-based ecological momentary assessment (EMA) and intervention (EMI) in both research and clinical practice in the context of blended care. Because real-time monitoring and intervention in people’s ever...
Article
Detecting early warning signals of developing mood disorders in continuously collected affective experience sampling (ESM) data would pave the way for timely intervention and prevention of a mood disorder from occurring or to mitigate its severity. However, there is an urgent need for online statistical methods tailored to the specifics of ESM data...
Chapter
Computational models of affect dynamics are ubiquitous. These models are appropriate for either exploring intensive longitudinal data or testing theories about affect dynamics. In this chapter, we give a brief overview of some of the computational models that have been applied in the field of affect dynamics, focusing on both discrete-time and cont...
Article
Prior expectations can bias how we perceive pain. Using a drift diffusion model, we recently showed that this influence is primarily based on changes in perceptual decision-making (indexed as shift in starting point). Only during unexpected application of high-intensity noxious stimuli, altered information processing (indexed as increase in drift r...
Article
Full-text available
Preregistration is a method to increase research transparency by documenting research decisions on a public, third-party repository prior to any influence by data. It is becoming increasingly popular in all subfields of psychology and beyond. Adherence to the preregistration plan may not always be feasible and even is not necessarily desirable, but...
Article
In many scientific disciplines, researchers are interested in discovering when complex systems such as stock markets, the weather or the human body display abrupt changes. Essentially, this often comes down to detecting whether a multivariate time series contains abrupt changes in one or more statistics, such as means, variances or pairwise correla...
Article
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Computational modeling plays an important role in a gamut of research fields. In affect research, continuous-time stochastic models are becoming increasingly popular. Recently, a non-linear, continuous-time, stochastic model has been introduced for affect dynamics, called the Affective Ising Model (AIM). The drawback of non-linear models like the A...
Article
The AR(1) model has been shown to outperform the general VAR(1) model on typical affective time series. Even in combination with a lasso penalty, the reduced VAR(1) model (VAR-lasso) is generally outperformed. A reason for the AR dominance is that the VAR-lasso selects models that are still too complex-the space of all possible VAR models includes...
Article
Autoregressive and vector autoregressive models are a driving force in current psychological research. In affect research they are, for instance, frequently used to formalize affective processes and estimate affective dynamics. Discrete-time model variants are most commonly used, but continuous-time formulations are gaining popularity, because they...
Article
Full-text available
Network analysis is an increasingly popular approach to study mental disorders in all their complexity. Multiple methods have been developed to extract networks from cross-sectional data, with these data being either continuous or binary. However, when it comes to time series data, most efforts have focused on continuous data. We therefore propose...
Article
Full-text available
Intra-individual processes are thought to continuously unfold across time. For equally spaced time intervals, the discrete-time lag-1 vector autoregressive (VAR(1)) model and the continuous-time Ornstein–Uhlenbeck (OU) model are equivalent. It is expected that by taking into account the unequal spacings of the time intervals in real data between ob...
Article
Subjective well-being changes over time. While the causes of these changes have been investigated extensively, few attempts have been made to capture these changes through computational modelling. One notable exception is the study by Rutledge et al. [Rutledge, R. B., Skandali, N., Dayan, P., & Dolan, R. J. (2014). A computational and neural model...
Article
Many theories have been put forward on how people become synchronized or co-regulate each other in daily interactions. These theories are often tested by observing a dyad and coding the presence of multiple target behaviours in small time intervals. The sequencing and co-occurrence of the partners' behaviours across time are then quantified by mean...
Article
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We investigated the reproducibility of the major statistical conclusions drawn in 46 articles published in 2012 in three APA journals. After having identified 232 key statistical claims, we tried to reproduce, for each claim, the test statistic, its degrees of freedom, and the corresponding p value, starting from the raw data that were provided by...
Article
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The correlational structure of a set of variables is often conveniently described by the pairwise partial correlations as they contain the same information as the Pearson correlations with the advantage of straightforward identifications of conditional linear independence. For mathematical convenience, multiple matrix representations of the pairwis...
Article
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Risen and Gilovich (2008) found that subjects believed that “tempting fate” would be punished with ironic bad outcomes (a main effect), and that this effect was magnified when subjects were under cognitive load (an interaction). A previous replication study (Frank & Mathur, 2016) that used an online implementation of the protocol on Amazon Mechanic...
Chapter
Re-sampling methods are popular for assessing uncertainty, for testing hypotheses, or for cross-validation because of their simplicity. They all rely on a similar scheme: generating replicated datasets by sampling data points from an original dataset, fitting a model or conducting a statistical test on each of these, and aggregating the results. Ho...
Preprint
Full-text available
Autoregressive and vector autoregressive models are a driving force in current psychological research. In affect research they are for instance frequently used to formalize affective processes and estimate affective dynamics. Discrete-time model variants are most commonly used, but continuous-time formulations are gaining popularity, because they c...
Article
Full-text available
The human affect system is responsible for producing the positive and negative feelings that color and guide our lives. At the same time, when disrupted, its workings lie at the basis of the occurrence of mood disorder. Understanding the functioning and dynamics of the affect system is therefore crucial to understand the feelings that people experi...
Article
Full-text available
Multivariate multigroup data are collected in many fields of science, where the so‐called groups pertain to, for instance, experimental groups or countries the participants are nested in. To summarize the main information in such data, principal component analysis (PCA) is highly popular. PCA reduces the variables to a few components that are linea...
Article
Full-text available
Gaussian Graphical Models (GGMs) are extensively used in many research areas, such as genomics, proteomics, neuroimaging, and psychology, to study the partial correlation structure of a set of variables. This structure is visualized by drawing an undirected network, in which the variables constitute the nodes and the partial correlations the edges....
Preprint
Full-text available
Autoregressive models are a driving force in current psychological research, for instance in affect research, where they are frequently used to formalize affective processes and estimate affective dynamics. Continuous‐time model variants are gaining popularity, because they can be fitted to and compared across data sets with different or varying sa...
Article
Full-text available
In various fields, statistical models of interest are analytically intractable and inference is usually performed using a simulation-based method. However elegant these methods are, they are often painstakingly slow and convergence is difficult to assess. As a result, statistical inference is greatly hampered by computational constraints. However,...
Preprint
Full-text available
The human affect system is responsible for producing the positive and negative feelings that color and guide our lives. At the same time, when disrupted, its workings lie at the basis of the occurrence of mood disorder. Understanding the functioning and dynamics of the affect system is therefore crucial to understand the feelings that people experi...
Preprint
Doing research inevitably involves making numerous decisions that can influence research outcomes in such a way that it leads to overconfidence in statistical conclusions. One proposed method to increase the interpretability of a research finding is preregistration, which involves documenting analytic choices on a public, third-party repository pri...
Article
Full-text available
Over the years, many studies have demonstrated a relation between emotion dynamics and psychological well-being¹. Because our emotional life is inherently time-dynamic2–6, affective scientists argue that, next to how positive or negative we feel on average, patterns of emotional change are informative for mental health7–10. This growing interest in...
Article
Full-text available
The decision process in choice reaction time data is traditionally described in detail with diffusion models. However, the total reaction time is assumed to consist of the sum of a decision time (as modeled by the diffusion process) and the time devoted to nondecision processes (e.g., perceptual and motor processes). It has become standard practice...
Article
Full-text available
In psychology, many studies measure the same variables in different groups. In case of a large number of variables and when a strong a priori idea about underlying latent constructs is lacking, researchers often start with reducing the variables to a few principal components in an exploratory way. Herewith, one often wants to evaluate whether the c...
Preprint
In various fields, statistical models of interest are analytically intractable. As a result, statistical inference is greatly hampered by computational constraints. However, given a model, different users with different data are likely to perform similar computations. Computations done by one user are potentially useful for other users with differe...
Article
Full-text available
To understand within-person psychological processes, one may fit VAR(1) models (or continuous-time variants thereof) to multivariate time series and display the VAR(1) coefficients as a network. This approach has two major problems. First, the contemporaneous correlations between the variables will frequently be substantial, yielding multicollinear...
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Received: July 16, 2018 Accepted: October 04, 2018 Published online: November 16, 2018 Issue release date: June 2019
Article
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Long-lived simultaneous changes in the autodependency of dynamic system variables characterize crucial events as epileptic seizures and volcanic eruptions and are expected to precede psychiatric conditions. To understand and predict such phenomena, methods are needed that detect such changes in multivariate time series. We put forward two methods:...
Preprint
Full-text available
Perceptual decision-making is commonly studied using stimuli with different physical properties but of comparable affective value. Here, we investigate neural processes underlying human perceptual decisions in the affectively rich domain of pain using a drift-diffusion model in combination with a probabilistic cueing paradigm. This allowed us to ch...
Article
Full-text available
In this paper the seminal Jeffreys–Lindley paradox is regarded from a mathematical point of view. We show that in certain scenarios the paradox may emerge in a reverse direction. © 2018, Wydawnictwo Uniwersytetu Wroclawskiego Sp. z o.o. All rights reserved.
Article
In psychology, modeling multivariate dynamical processes within a person is gaining ground. A popular model is the lag-one vector autoregressive or VAR(1) model and its variants, in which each variable is regressed on all variables (including itself) at the previous time point. Many parameters have to be estimated in the VAR(1) model, however. The...
Article
Full-text available
Variability indices are a key measure of interest across diverse fields, in and outside psychology. A crucial problem for any research relying on variability measures however is that variability is severely confounded with the mean, especially when measurements are bounded, which is often the case in psychology (e.g., participants are asked “rate h...
Article
Full-text available
Emotion dynamics are likely to arise in an interpersonal context. Standard methods to study emotions in interpersonal interaction are limited because stationarity is assumed. This means that the dynamics, for example, time-lagged relations, are invariant across time periods. However, this is generally an unrealistic assumption. Whether caused by an...
Article
Change point detection methods signal the occurrence of abrupt changes in a time series. Non-parametric approaches, such as the Gaussian kernel based change point (KCP) detection (Arlot et al., 2012), are especially attractive because they impose less assumptions on the data. Yet, a drawback of these methods is that most of them are sensitive to ch...
Article
Full-text available
Detecting abrupt correlation changes in multivariate time series is crucial in many application fields such as signal processing, functional neuroimaging, climate studies, and financial analysis. To detect such changes, several promising correlation change tests exist, but they may suffer from severe loss of power when there is actually more than o...
Article
Full-text available
In psychology, modeling multivariate dynamical processes within a person is gaining ground. A popular model is the lag-one vector autoregressive or VAR(1) model and its variants, in which each variable is regressed on all variables (including itself) at the previous time point. Many parameters have to be estimated in the VAR(1) model, however. The...
Article
Expectations about an impeding pain stimulus strongly shape its perception, yet the degree that uncertainty might affect perception is far less understood. To explore the influence of uncertainty on pain ratings , we performed a close replication of the study of Yoshida, Seymour, Koltzenburg, and Dolan (2013), who manipulated vicarious information...
Article
We present a case study of hierarchical Bayesian explanatory cognitive psychometrics, examining information processing characteristics of individuals with high-functioning autism spectrum disorder (HFASD). On the basis of previously published data, we compare the classification behavior of a group of children with HFASD with that of typically devel...
Article
Full-text available
In psychology, studying multivariate dynamical processes within a person is gaining ground. An increasingly often used method is vector autoregressive (VAR) modeling, in which each variable is regressed on all variables (including itself) at the previous time points. This approach reveals the temporal dynamics of a system of related variables acros...
Article
Full-text available
In psychology, the use of intensive longitudinal data has steeply increased during the past decade. As a result, studying temporal dependencies in such data with autoregressive modeling is becoming common practice. However, standard autoregressive models are often suboptimal as they assume that parameters are time-invariant. This is problematic if...
Article
Full-text available
Empirical research inevitably includes constructing a data set by processing raw data into a form ready for statistical analysis. Data processing often involves choices among several reasonable options for excluding, transforming, and coding data. We suggest that instead of performing only one analysis, researchers could perform a multiverse analys...
Article
Parameter space partitioning (PSP) is a versatile tool for model analysis that detects the qualitatively distinctive data patterns a model can generate, and partitions a model’s parameter space into regions corresponding to these patterns. In this paper, we propose a PSP fit measure that summarizes the outcome of a PSP analysis into a single number...
Article
Change point detection in multivariate time series is a complex task since next to the mean, the correlation structure of the monitored variables may also alter when change occurs. DeCon was recently developed to detect such changes in mean and\or correlation by combining a moving windows approach and robust PCA. However, in the literature, several...
Article
Full-text available
Background Affective dysregulation is widely regarded as being the core problem in patients with borderline personality disorder (BPD). Moreover, BPD is the disorder mainly associated with affective dysregulation. However, the empirical confirmation of the specificity of affective dysregulation for BPD is still pending. We used a validated approach...
Article
Full-text available
Multivariate psychological processes have recently been studied, visualized, and analyzed as networks. In this network approach, psychological constructs are represented as complex systems of interacting components. In addition to insightful visualization of dynamics, a network perspective leads to a new way of thinking about the nature of psycholo...
Article
Full-text available
Objective: While in general arousal increases with positive or negative valence (a so-called V-shape relation), there are large differences among individuals in how these two fundamental dimensions of affect are related in people's experience. In two studies, we examined two possible sources of this variation: personality and culture. Method: In...
Article
Many questions in the behavioral sciences focus on the causal interplay of a number of variables across time. To reveal the dynamic relations between the variables, their (auto- or cross-) regressive effects across time may be inspected by fitting a lag-one vector autoregressive, or VAR(1), model and visualizing the resulting regression coefficient...
Article
Full-text available
In depression research, symptoms are routinely assessed via rating scales and added to construct sum-scores. These scores are used as a proxy for depression severity in cross-sectional research, and differences in sum-scores over time are taken to reflect changes in an underlying depression construct. To allow for such interpretations, rating scale...