Ready for the Worst? Negative Affect in Anticipation of a Stressor Does Not Protect Against Affective Reactivity.

Lay wisdom suggests feeling negative while awaiting an upcoming stressor - anticipatory negative affect - shields against the blow of the subsequent stressor. However, evidence is mixed, with different lines of research and theory indirectly suggesting that anticipatory negative affect is helpful, harmful, or has no effect on emotional outcomes. In two studies, we aimed to reconcile these competing views by examining the affective trajectory across hours, days, and months, separating affective reactivity and recovery. In Study 1, first-year students (N=101) completed 9 days of experience sampling (10 surveys/day) as they received their first-semester exam grades, and a follow-up survey 5 months later. In Study 2, participants (N=73) completed 2 days of experience sampling (60 surveys/day) before and after a Trier Social Stress Test. We investigated the association between anticipatory negative affect and the subsequent affective trajectory, investigating (1) reactivity immediately after the stressor, (2) recovery across hours (Study 2) and days (Study 1), and (3) recovery after 5 months (Study 1). Across the two studies, feeling more negative in anticipation of a stressor was either associated with increased negative affective reactivity, or unassociated with affective outcomes. These results run counter to the idea that being affectively ready for the worst has psychological benefits, suggesting that instead, anticipatory negative affect can come with affective costs

For Better or for Worse? Visualizing Previous Intensity Levels Improves Emotion (Dynamic) Measurement in Experience Sampling

It is a long known reality that humans have difficulty to accurately rate the absolute intensity of internal experiences, yet the predominant way experience sampling (ESM) researchers assess participants’ momentary emotion levels is by means of an absolute measurement scale. In a daily-life experiment (n = 178), we evaluate the efficacy of two alternative measurement methods that solicit a simpler, relative emotional evaluation: (1) the visualization of a relative anchor point on the absolute rating scale that depicts people’s previous emotion rating, and (2) the phrasing of relative emotion items that ask for a comparison with earlier emotion levels by means of a relative rating scale. Using six quality criteria relevant for ESM, we conclude that the use of a visual ‘Last’ anchor significantly improves emotion measurement in daily life: (a) Theoretically, this method has the best perceived user experience, which suggests that it better aligns with people’s emotional rating experience. Methodologically, this type of measurement generates ESM time series that (b) carried a stronger emotional signal, (c) exhibited less measurement error, produced person-level emotion dynamic measures that were (d) more stable, and generally showed stronger (e) unique and (f) incremental relations with external criteria like neuroticism and borderline personality. In sum, we see great value in the addition of a relative ‘Last’ anchor to the absolute measurement scales of future ESM studies on emotions, as it structures the ambiguous rating space and introduces more standardization within and between individuals. In contrast, using relatively phrased emotion items is not recommended

Reply to: Context matters for affective chronometry.

status: accepte

VAR(1) based models do not always outpredict AR(1) models in typical psychological applications.

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 question thus rises whether the VAR(1) model is not too complex and overfits the data. If the latter is the case, the estimated model will not properly predict new unseen data. As a consequence, it cannot be trusted that the estimated parameters adequately characterize the individual from which the data at hand were sampled. In this article, we evaluate for current psychological applications whether the VAR(1) model outpredicts simpler models, using cross-validation (CV) techniques to determine the predictive accuracy. As it is unclear whether one should use standard CV techniques (leave-one-out CV or K-fold CV) or variants that take time dependence into account (blocked CV, hv-block CV, or accumulated prediction errors), we first compare the relative performance of these five CV techniques in a simulation study. The simulation settings mimic the data characteristics of current psychological VAR(1) applications and show that blocked CV has the best performance in general. Subsequently, we use blocked CV to assess to what extent the VAR(1) models predict unseen data for three recent psychological applications. We show that the VAR(1) based models do not outperform the AR(1) based ones for the three presented psychological applications. (PsycINFO Database Recordstatus: publishe

Ready for the Worst? Negative Affect in Anticipation of a Stressor Does Not Protect Against Affective Reactivity

Lay wisdom suggests feeling negative while awaiting an upcoming stressor – anticipatory negative affect – shields against the blow of the subsequent stressor. However, evidence is mixed, with different lines of research and theory indirectly suggesting that anticipatory negative affect is helpful, harmful, or has no effect on emotional outcomes. In two studies, we aimed to reconcile these competing views by examining the affective trajectory across hours, days, and months, separating affective reactivity and recovery. In Study 1, first-year students (N=101) completed 9 days of experience sampling (10 surveys/day) as they received their first-semester exam grades, and a follow-up survey 5 months later. In Study 2, participants (N=73) completed 2 days of experience sampling (60 surveys/day) before and after a Trier Social Stress Test. We investigated the association between anticipatory negative affect and the subsequent affective trajectory, investigating (1) reactivity immediately after the stressor, (2) recovery across hours (Study 2) and days (Study 1), and (3) recovery after 5 months (Study 1). Across the two studies, feeling more negative in anticipation of a stressor was either associated with increased negative affective reactivity, or unassociated with affective outcomes. These results run counter to the idea that being affectively ready for the worst has psychological benefits, suggesting that instead, anticipatory negative affect can come with affective costs

Prepaid parameter estimation without likelihoods.

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, for a given statistical model, different users, even with different data, are likely to perform similar computations. Computations done by one user are potentially useful for other users with different data sets. We propose a pooling of resources across researchers to capitalize on this. More specifically, we preemptively chart out the entire space of possible model outcomes in a prepaid database. Using advanced interpolation techniques, any individual estimation problem can now be solved on the spot. The prepaid method can easily accommodate different priors as well as constraints on the parameters. We created prepaid databases for three challenging models and demonstrate how they can be distributed through an online parameter estimation service. Our method outperforms state-of-the-art estimation techniques in both speed (with a 23,000 to 100,000-fold speed up) and accuracy, and is able to handle previously quasi inestimable models

Prepaid parameter estimation without likelihoods.

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, for a given statistical model, different users, even with different data, are likely to perform similar computations. Computations done by one user are potentially useful for other users with different data sets. We propose a pooling of resources across researchers to capitalize on this. More specifically, we preemptively chart out the entire space of possible model outcomes in a prepaid database. Using advanced interpolation techniques, any individual estimation problem can now be solved on the spot. The prepaid method can easily accommodate different priors as well as constraints on the parameters. We created prepaid databases for three challenging models and demonstrate how they can be distributed through an online parameter estimation service. Our method outperforms state-of-the-art estimation techniques in both speed (with a 23,000 to 100,000-fold speed up) and accuracy, and is able to handle previously quasi inestimable models.status: publishe

Sidelining the mean: The relative variability index as a generic mean-corrected variability measure for bounded variables

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 how happy you feel now between 0 and 100?"). While a number of solutions to this problem have been proposed, none of these are sufficient or generic. As a result, conclusions on the basis of research relying on variability measures may be unjustified. Here, we introduce a generic solution to this problem by proposing a relative variability index that is not confounded with the mean by taking into account the maximum possible variance given an observed mean. The proposed index is studied theoretically and we offer an analytical solution for the proposed index. Associated software tools (in R and MATLAB) have been developed to compute the relative index for measures of standard deviation, relative range, relative interquartile distance and relative root mean squared successive difference. In five data examples, we show how the relative variability index solves the problem of confound with the mean, and document how the use of the relative variability measure can lead to different conclusions, compared with when conventional variability measures are used. Among others, we show that the variability of negative emotions, a core feature of patients with borderline disorder, may be an effect solely driven by the mean of these negative emotions. (PsycINFO Database Record (c) 2018 APA, all rights reserved).status: Published onlin

The affective ising model: A computational account of human affect dynamics

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 experience on a daily basis, their dynamics across time, and how they can become dysregulated in mood disorder. In this paper, a nonlinear stochastic model for the dynamics of positive and negative affect is proposed called the Affective Ising Model (AIM). It incorporates principles of statistical mechanics, is inspired by neurophysiological and behavioral evidence about auto-excitation and mutual inhibition of the positive and negative affect dimensions, and is intended to better explain empirical phenomena such as skewness, multimodality, and non-linear relations of positive and negative affect. The AIM is applied to two large experience sampling studies on the occurrence of positive and negative affect in daily life in both normality and mood disorder. It is examined to what extent the model is able to reproduce the aforementioned non-Gaussian features observed in the data, using two sightly different continuous-time vector autoregressive (VAR) models as benchmarks. The predictive performance of the models is also compared by means of leave-one-out cross-validation. The results indicate that the AIM is better at reproducing non-Gaussian features while their performance is comparable for strictly Gaussian features. The predictive performance of the AIM is also shown to be better for the majority of the affect time series. The potential and limitations of the AIM as a computational model approximating the workings of the human affect system are discussed.status: publishe

The Affective Ising Model: A computational account of human affect dynamics.

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 experience on a daily basis, their dynamics across time, and how they can become dysregulated in mood disorder. In this paper, a nonlinear stochastic model for the dynamics of positive and negative affect is proposed called the Affective Ising Model (AIM). It incorporates principles of statistical mechanics, is inspired by neurophysiological and behavioral evidence about auto-excitation and mutual inhibition of the positive and negative affect dimensions, and is intended to better explain empirical phenomena such as skewness, multimodality, and non-linear relations of positive and negative affect. The AIM is applied to two large experience sampling studies on the occurrence of positive and negative affect in daily life in both normality and mood disorder. It is examined to what extent the model is able to reproduce the aforementioned non-Gaussian features observed in the data, using two sightly different continuous-time vector autoregressive (VAR) models as benchmarks. The predictive performance of the models is also compared by means of leave-one-out cross-validation. The results indicate that the AIM is better at reproducing non-Gaussian features while their performance is comparable for strictly Gaussian features. The predictive performance of the AIM is also shown to be better for the majority of the affect time series. The potential and limitations of the AIM as a computational model approximating the workings of the human affect system are discussed