Statistical tools for modeling emotion dynamics

Emotions are dynamic entities, following the ebb and flow of daily life. Dynamic patterns reflect the habitual emotional responses of an individual to the environment. Sudden shifts in the way emotions fluctuate might be informative about the psychological health of an individual. To fully understand emotions, it is therefore important to get a grip on their dynamic characteristics.In psychology, the dynamic nature of emotions has been stressed in various process models. However, sophisticated statistical modeling of dynamic emotion processes holds some complex challenges. In this dissertation, we would like to offer researchers some statistical tools and guidelines for tackling these challenges.We start from existing dynamic modeling techniques in psychology and other disciplines. Our goal is to use these dynamic models for answering substantive questions in the literature on emotion. While applying the models, we should take into account the particular data characteristics. All our applications are based on Bayesian statistics, a statistical ideology rooted on Bayes theorem that uses distributions to estimateparameters.The dissertation consists of a general introduction and three stand-alone chapters.In chapter 2, we discuss linear dynamical system theory and state space modeling. In the state space approach, separate equations are formulated for observed and latent data. This technique is commonly used in engineering, in for instance the development of software for satellites and global positioning systems. Because of its generality and flexibility, the state space approach seems to be useful for analyzing psychological data. To take into account the hierarchical data structure, we introduce a hierarchical Bayesian implementation and apply it to physiological time series.In chapter 3, transdimensional model selection is introduced. In this model selection ideology, a model indicator is formulated as a parameter that can change values, hereby switching between different models. Following the Bayesian framework, prior information about the model probabilities is defined, and posterior probabilities are estimated, taking the observed data into account. Two examples of transdimensional techniques are reversible jump MCMC and the product space method. The reason why this way of model selection never gained popularity is because of certain implementational difficulties. In the chapter, we discuss the product space method and highlight how it should be used to obtain optimal performance. The technique is illustrated with a multiple-model selection problem in modeling circadian rhythms of emotions. To emphasize the flexibility of the method, the application is completed with other data examples in psychology.In chapter 4, we investigate the issue of physiological emotion specificity. This comprehensive research domain handles about the relation between physiological and experiential/behavioral components of emotion. In other words, the domain investigates whether and how our physiological systems are affected by our experienced emotional states. We introduce a conceptual framework that toghether graps the crucial ideas that are discussed in the domain. Further, we translate the concepts into a regime-switching time series model and apply it to data. Also here, a group-level analysis is used.1. Introduction 2. A hierarchical state space approach to affective dynamics 3. A tutorial on Bayes factor estimation with the product space method 4. An integrative framework for physiological emotion specificitynrpages: 179status: publishe

Bayesian hypothesis testing for hierarchical models using transdimensional Markov chain Monte Carlo methods

Bayesian hypothesis testing for hierarchical models is greatly facilitated by the use of sophisticated sampling methods such as transdimensional Markov chain Monte Carlo methods. These methods use a model indicator to switch from one model to another, sample values from the posterior distribution of the active model, and perform an operation to jump between models with different dimension. Here we explore the possibilities of a method proposed by Carlin and Chib (1995). This method is in fact a Gibbs sampler for the model parameters and the model indicator. The Bayes factor can be approximated from a posterior sample of the model indicator. We apply this method for hypothesis testing in the context of subliminal priming (Rouder, Morey, Speckman and Pratte, 2007; Zeelenberg, Wagenmakers and Raaijmakers, 2002), using graphical binomial models with probit-transformed rate parameters. Both non-hierarchical, subject-level comparisons and hierarchical, group-level comparisons are carried out and all hypotheses contain order restrictions. The results from the Carlin and Chib algorithm were found to closely match the results of an importance sampling algorithm. Because of its satisfactory performance and ease of implementation, this method is likely to be a prime candidate for the implementation of Bayesian hypothesis tests in experimental psychology.status: publishe

Bayesian hypothesis testing for psychologists: A tutorial on the Savage-Dickey method

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 conveyed by a Bayesian hypothesis test, which prefers the model with the highest average likelihood. One of the main problems with this Bayesian hypothesis test, however, is that it often requires relatively sophisticated numerical methods for its computation. Here we draw attention to the Savage-Dickey density ratio method, a method that can be used to compute the result of a Bayesian hypothesis test for nested models and under certain plausible restrictions on the parameter priors. Practical examples demonstrate the method's validity, generality, and flexibility. (C) 2009 Elsevier Inc. All rights reserved.status: publishe

Modeling physiological emotion specificity with regime switching state space models (Symposium on heterogeneity in dynamic models)

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Estimation of state space models for affective dynamics using Markov chain Monte Carlo methods

Lately, emotion research has been focusing on the conceptualization of emotions as multicomponential, dynamical systems. This evolution led to new challenging research questions, concerning for instance autoregressive dependencies (related to concepts of emotional homeostasis) or cross-lagged relations (related to the mutual influence of emotion components). We want to discuss a basic linear Gaussian state-space approach for the dynamical modeling of emotion components. It will be shown how Markov chain Monte Carlo methods are used to estimate the model parameters. Various model extensions are discussed, such as estimating the influence of external covariates, regime-switching of parameters, etc. In a second part, we apply this framework to high resolution psychophysiological and behavioral data obtained during emotionally evocative adolescent-parent interactions and illustrate how it can be used to obtain new insights in the dynamical nature of emotions.status: publishe

Bayesian state space models for affective dynamics

In the last years, emotion research has been focusing on the conceptualization of emotions as multicomponential, dynamical systems. This development created a new set of challenging research questions, concerning for instance autoregressive dependencies (related to concepts of emotional homeostasis) or cross-lagged relations (related to the mutual influence of emotion components). In a first part, we want to introduce a state-space approach for the dynamical modeling of emotion components. It will be shown how Markov chain Monte Carlo methods are used to estimate the model parameters. Various model extensions are discussed (e.g. external covariates, regime-switching). In a second part, we apply this framework to high resolution psychophysiological and behavioral data obtained during emotionally evocative adolescent-parent interactions and illustrate how it can be used to obtain new insights in the dynamical nature of emotions.status: publishe

Studying latent affective dynamics with a Bayesian state-space approach

In the last years, emotion research has been focusing on the conceptualization of emotions as multicomponential, dynamical systems. This development created a new set of challenging research questions, concerning for instance autoregressive dependencies (related to concepts of emotional homeostasis) or cross-lagged relations (related to the mutual influence of emotion components). In a first part, we want to introduce a state-space approach for the dynamical modeling of emotion components. It will be shown how Markov chain Monte Carlo methods are used to estimate the model parameters. Various model extensions are discussed (e.g. external covariates, regime-switching). In a second part, we apply this framework to high resolution psychophysiological and behavioral data obtained during emotionally evocative adolescent-parent interactions and illustrate how it can be used to obtain new insights in the dynamical nature of emotions.status: publishe

A hierarchical state space approach to affective dynamics

Linear dynamical system theory is a broad theoretical framework that has been applied in various research areas such as engineering, econometrics and recently in psychology. It quantifies the relations between observed inputs and outputs that are connected through a set of latent state variables. State space models are used to investigate the dynamical properties of these latent quantities. These models are especially of interest in the study of emotion dynamics, with the system representing the evolving emotion components of an individual. However, for simultaneous modeling of individual and population differences, a hierarchical extension of the basic state space model is necessary. Therefore, we introduce a Bayesian hierarchical model with random effects for the system parameters. Further, we apply our model to data that were collected using the Oregon adolescent interaction task: 66 normal and 67 depressed adolescents engaged in a conflict-oriented interaction with their parents and second-to-second physiological and behavioral measures were obtained. System parameters in normal and depressed adolescents were compared, which led to interesting discussions in the light of findings in recent literature on the links between cardiovascular processes, emotion dynamics and depression. We illustrate that our approach is flexible and general: The model can be applied to any time series for multiple systems (where a system can represent any entity) and moreover, one is free to focus on various components of this versatile model.status: publishe

Poster session: An empirical comparison of emotion theories on physiological activation and dynamics using a state space approach

The question of the specificity of psychophysiological activation patterns in emotions has a long and debated history. Some have argued for the presence of specific activation patterns for discrete emotions, others have propagated physiological specificity to align with more general emotion systems based on a valence or approach/avoidance categorization, whereas still others have defended the notion of unspecified general emotional arousal. This paper aims to approach this issue by presenting a hierarchical state space modeling framework with the flexibility to represent each of the theoretical positions. These models not only capture psychophysiological activation levels but also temporal dynamics of the physiological processes. We applied this framework to ecologically valid data consisting of multiple cardiovascular and respiratory measures that were collected on a second-to-second basis from adolescents during interactions with their parents. The verbal and non-verbal behavior in the interactions was coded into emotion categories (neutral, angry, dysphoric or happy emotion). The results show the relative fit of the theoretical models to the psychophysiological data and inform us about possible organizational principles underlying emotional physiological activation and dynamics. The results have implications for emotion theories and our understanding of their physiological underpinnings.status: publishe

An integrative regime-switching time series approach for physiological emotion specificity

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