Emotion as a Thermostat: Representing Emotion Regulation Using a Damped Oscillator Model.

Chow, Sy-Miin; Ram, Nilam; Boker, Steven M.; Fujita, Frank; Clore, Gerald L.

doi:10.1037/1528-3542.5.2.208

Cited by 192 publications

(199 citation statements)

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“…A linear model of affective change implies that changes in affect start at a certain level of intensity and then increase or decrease at a relatively constant rate over the time period of interest. Recent models on affect progression and experience (e.g., Chow, Ram, Boker, Fujita, & Clore, 2005;Larsen, 2000) challenge such a linear conceptualization of affective change. To illustrate, affect may begin at a high level of intensity and may then decrease rapidly over several points of time before tapering off to an asymptote at later points of time.…”

Section: Temporal Influences On Forecasting Accuracymentioning

confidence: 99%

Investigating the Role of Time in Affective Forecasting: Temporal Influences on Forecasting Accuracy

Finkenauer

Gallucci

Dijk

et al. 2007

Pers Soc Psychol Bull

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Using extensive diary data from people taking their driver's license exam, the authors investigated the role of time in affective forecasting accuracy. Replicating existing findings, participants grossly overestimated the intensity and duration of their negative affect after failure and only slightly overestimated the intensity and duration of their positive affect after success. Extending existing findings, participants accurately predicted a decrease of their affective reactions over time but underestimated the speed with which this decrease would occur. In addition, they showed greater forecasting accuracy for positive affect than negative affect when the exam was distant and greater forecasting accuracy for negative affect than positive affect when the exam was close. The motivational processes underlying these findings are being discussed.Keywords: affective experiences; affective forecasting; temporal biases P redicting how we will feel in response to future events is a central component of our self-knowledge. Small and big decisions-whether to drink another beer, whom to marry, whether to have children-often depend on our predictions about how pleasant or unpleasant these events would make us feel, specifically our affective forecasts . The accuracy of these affective forecasts has recently at Vrije Universiteit 34820 on November 28, 2010 psp.sagepub.com Downloaded from forecasting accuracy. By providing participants with an opportunity to forecast their affective reactions on several days before the focal event and by following their affective experiences for several days after the event had happened, we compared people's affective forecasts and their affective experiences over time. This approach allows us to recognize time as an important determinant of forecasting accuracy through the investigation of both the time course of affective forecasts and experiences and the influence of temporal distance to the focal event on forecasting accuracy. Temporal Influences on Forecasting AccuracyResearch on forecasting accuracy compares people's predictions about how they will feel in response to an event in the future with their actual affective experiences (Buehler & McFarland, 2001;Gilbert et al., 1998;Wilson, Wheatley, Meyers, Gilbert, & Axsom, 2000). Typically, this research takes what we would like to call a time slice approach. It computes the difference between an affective forecast made at one point in time before a focal event takes place and an assessment of the affective experience at one (e.g., Gilbert et al., 1998) or several points in time after the focal event has taken place (e.g., Wilson et al., 2000).Two basic findings have emerged from this approach. First, people overestimate the intensity and duration of their affective reactions to a variety of focal events, committing the impact bias . Second, the impact bias shows a positivenegative asymmetry, because it is much more pronounced for negative events than positive events (e.g., Buehler & McFarland, 2001;Gilbert et al., 1998).Often the impact...

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Section: Temporal Influences On Forecasting Accuracymentioning

confidence: 99%

Investigating the Role of Time in Affective Forecasting: Temporal Influences on Forecasting Accuracy

Finkenauer

Gallucci

Dijk

et al. 2007

Pers Soc Psychol Bull

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“…The OU model's self-regulation parameter controls for the autocorrelation in the changes, and turned into a random effect. Besides the correspondence with the LMM framework, the OU model falls into the class of dynamical models termed as stochastic differential equations (SDEs; see e.g., Oud & Jansen, 2000;Oud, 2007;Molenaar & Newell, 2003;Chow, Ferrer, & Nesselroade, 2007), which extend ordinary differential equations (ODEs; e.g., the oscillator model; Chow, Ram, Boker, Fujita, & Clore, 2005; and the reservoir model; Deboeck & Bergeman, 2013) in allowing for process noises or uncertainties in how the latent processes change over time. When compared to oscillatory models of change, the OU model does not reinforce an oscillatory pattern on the dynamics itself, but can include cyclic changes in baseline levels (as TVCs), while at the same time capturing stochastic variation that is separate from measurement noise.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Data Analysis with the Bivariate Hierarchical Ornstein-Uhlenbeck Process Model

Oravecz

Tuerlinckx

Vandekerckhove

2016

Multivariate Behavioral Research

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In this paper, we propose a multilevel process modeling approach to describing individual differences in within-person changes over time. To characterize changes within an individual, repeated measurements over time are modeled in terms of three person-specific parameters: a baseline level, intra-individual variation around the baseline and regulatory mechanisms adjusting towards baseline. Variation due to measurement error is separated from meaningful intra-individual vari- can be studied in a one-stage analysis, meaning that model parameters and regression coefficients are estimated in the same step. Mathematical details of the approach, including a description of the core process model-the Ornstein-Uhlenbeck model-are provided. We also describe a user friendly, freely accessible software program that provides a straightforward graphical interface to carry out parameter estimation and inference. The proposed approach is illustrated by analyzing data collected via self-reports on affective states.

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“…That is, after the second decrease in amplitude, the peaks of the cycle were no longer considered extreme event (i.e., "0") and information from this region was not given much emphasis in other layers of the HS hierarchy. Cyclic processes that show continual or abrupt shifts in amplitude are commonly observed in social and behavioral sciences (Bisconti, Bergeman, & Boker 2004;Chow, Ram, Boker, Fujita, & Clore, 2005;Chow, Hamaker, Fujita, & Boker, 2009;Molenaar & Newell, 2003). While the current formulation of the HS algorithm is meant to extract certain kinds of systematic patterns, this formulation can certainly be adapted and new layers can be added to tailor to the nature of empirical applications.…”

Section: Methodological Issues and Extensionsmentioning

confidence: 99%

Exploring the Dynamics of Dyadic Interactions via Hierarchical Segmentation

et al. 2010

Self Cite

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In this article we present an exploratory tool for extracting systematic patterns from multivariate data. The technique, hierarchical segmentation (HS), can be used to group multivariate time series into segments with similar discrete-state recurrence patterns and it is not restricted by the stationarity assumption. We use a simulation study to describe the steps and properties of HS. We then use empirical data on daily affect from one couple to illustrate the use of HS for describing the affective dynamics of the dyad. First, we partition the data into three periods that represent different affective states and show different dynamics between both individuals' affect. We then examine the synchrony between both individuals' affective states and identify different patterns of coherence across the periods. Finally, we discuss the possibilities of using results from HS to construct confirmatory dynamic models with multiple change points or regime-specific dynamics.

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Emotion as a Thermostat: Representing Emotion Regulation Using a Damped Oscillator Model.

Cited by 192 publications

References 64 publications

Investigating the Role of Time in Affective Forecasting: Temporal Influences on Forecasting Accuracy

Investigating the Role of Time in Affective Forecasting: Temporal Influences on Forecasting Accuracy

Bayesian Data Analysis with the Bivariate Hierarchical Ornstein-Uhlenbeck Process Model

Exploring the Dynamics of Dyadic Interactions via Hierarchical Segmentation

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