Equilibria in Personality States: A Conceptual Primer for Dynamics in Personality States

Dynamic Associations Among Sleep, Emotion Dysregulation, and Desire to Live in a Perinatal Sample

OBJECTIVE

The present study prospectively examined dynamic associations among sleep, emotion dysregulation, and desire to live during the perinatal transition, as it was theorized that these factors may contribute to the emergence of postpartum suicide risk.

METHOD

Ninety-four women ( Mage = 29.2 years; 23.4% Latina) wore wrist actigraphs and completed twice daily surveys for 7 days during the third trimester of pregnancy, 6 weeks postpartum, and 4 months postpartum. Multilevel, change-as-outcome models were built to examine changes in attractor dynamics among sleep, emotion dysregulation, and desire to live, as well as if sleep-emotion dysregulation dynamics differed based on participants' desires to live.

RESULTS

From pregnancy to 6 weeks postpartum, emotion dysregulation ( B = -0.09, p = .032) and desire to live ( B = -0.16, p < .001) exhibited more stable temporal patterns around higher emotion dysregulation and lower desire to live. Compared to women who reported consistently high desires to live, those who experienced fluctuations in their desires to live exhibited lower, more stable sleep efficiency during pregnancy ( B = -0.90, p < .001). At 4 months postpartum, those with fluctuating desires to live exhibited a coupling dynamic whereby low sleep efficiency predicted increases in emotion dysregulation ( B = -0.16, p = .020).

CONCLUSIONS

This study was the first to examine nonlinear dynamics among risk factors for postpartum suicide, which may be evident as early as pregnancy and 6 weeks postpartum. Sleep health, in particular, warrants further exploration as a key susceptibility factor in the emergence of postpartum suicide risk.

PREREGISTRATION

Open Science Framework ( https://osf.io/qxb75/?view_only=799ffe5c048842dfb89d3ddfebaa420d ).

Toward a dynamic approach of person perception at zero acquaintance: Applying recurrence quantification analysis to thin slices

Thin-slice methodology has provided us with abundant behavioral streams that self-reported measures would fail to capture, but traditional analytical paradigms in social and personality psychology cannot fully capture the temporal trajectories of person perception at zero acquaintance. At the same time, empirical investigations into how persons and situations jointly predict behavior enacted in situ are scarce, despite the importance of examining real-world behavior to understand any phenomenon of interest. To complement existing theoretical models and analyses, we propose the dynamic latent state-trait model blending dynamical systems theory and person perception. We present a data-driven case study using thin-slice methodology to demonstrate the model. This study provides direct empirical support for the proposed theoretical model on person perception at zero acquaintance highlighting the target, the perceiver, the situation, and time. The results of the study demonstrate that dynamical systems theory approaches can be leveraged to provide information about person perception at zero acquaintance above and beyond that of more traditional approaches. CLASSIFICATION CODE: 3040 (Social Perception & Cognition).

Extracting Nonlinear Dynamics from Psychological and Behavioral Time Series Through HAVOK Analysis

Analytical methods derived from nonlinear dynamical systems, complexity, and chaos theories offer researchers a framework for in-depth analysis of time series data. However, relatively few studies involving time series data obtained from psychological and behavioral research employ such methods. This paucity of application is due to a lack of general analysis frameworks for modeling time series data with strong nonlinear components. In this article, we describe the potential of Hankel alternative view of Koopman (HAVOK) analysis for solving this issue. HAVOK analysis is a unified framework for nonlinear dynamical systems analysis of time series data. By utilizing HAVOK analysis, researchers may model nonlinear time series data in a linear framework while simultaneously reconstructing attractor manifolds and obtaining a secondary time series representing the amount of nonlinear forcing occurring in a system at any given time. We begin by showing the mathematical underpinnings of HAVOK analysis and then show example applications of HAVOK analysis for modeling time series data derived from real psychological and behavioral studies.

Does it pay off to act conscientiously, both now and later? Examining concurrent, lagged, and cumulative effects of state conscientiousness

Although previous research has shown that both trait and state conscientiousness are positively associated with a wide range of positive life and work outcomes, some studies indicate that acting in a conscientious way is effortful, and that behaving outside one’s conscientiousness related comfort zone (i.e., acting counterhabitual) may lead to cognitive or affective cost. Because these costs are not likely to be evident immediately, we examine how within-person fluctuations in conscientiousness relate to within-person fluctuations in emotional exhaustion, resource depletion, and negative affect, not only concurrently, but also in a delayed fashion and cumulated over time. In two experience sampling studies, we found that higher levels of conscientiousness are concurrently related to lower levels of emotional exhaustion, resource depletion, and negative affect. When looking at delayed effects, no conclusive evidence was found for affective or cognitive costs of (counterhabitual) conscientiousness. Finally, analyzing cumulative effects revealed that repeated negative deviations from one’s typical level of conscientiousness were positively associated to exhaustion, depletion, and negative affect, while repeated positive deviations were negatively associated with depletion and unrelated to exhaustion and negative affect. Altogether, our findings suggest that self-rated conscientious behavior is generally beneficial, even if this behavior goes against one’s typical behavior.

Modeling Dynamic Personality Theories in a Continuous-time Framework:An Illustration

Personality psychology has traditionally focused on stable between-person differences. Yet, recent theoretical developments and empirical insights have led to a new conceptualization of personality as a dynamic system (e.g., Cybernetic Big Five Theory). Such dynamic systems comprise several components that need to be conceptually distinguished and mapped to a statistical model for estimation. In the current work, we illustrate how common components from these new dynamic personality theories may be implemented in a continuous-time modeling framework. As an empirical example, we reanalyze experience sampling data from Sherman et al. (2015) with N = 180 persons (with on average T = 40 [SD = 8] measurement occasions) to investigate four different effects between momentary happiness, momentary extraverted behavior, and the perception of a situation as social: (1) between-person effects, (2) contemporaneous effects, (3) autoregressive effects, and (4) cross-lagged effects. We highlight that these four effects must not necessarily point in the same direction, which is in line with assumptions from dynamic personality theories.

Intervening on psychopathology networks: Evaluating intervention targets through simulations

Identifying the different influences of symptoms in dynamic psychopathology models may hold promise for increasing treatment efficacy in clinical applications. Dynamic psychopathology models study the behavioral patterns of symptom networks, where symptoms mutually enforce each other. Interventions could be tailored to specific symptoms that are most effective at lowering symptom activity or that hinder the further development of psychopathology. Simulating interventions in psychopathology network models fits in a novel tradition where symptom-specific perturbations are used as in silico interventions. Here, we present the NodeIdentifyR algorithm (NIRA) to identify the projected most efficient, symptom-specific intervention target in a network model (i.e., the Ising model). We implemented NIRA in a freely available R package. The technique studies the projected effects of symptom-specific interventions by simulating data while symptom parameters (i.e., thresholds) are systematically altered. The projected effect of these interventions is defined in terms of the expected change in overall symptom activity across simulations. With this algorithm, it is possible to study (1) whether symptoms differ in their projected influence on the behavior of the symptom network and, if so, (2) which symptom has the largest projected effect in lowering or increasing overall symptom activation. As an illustration, we apply the algorithm to an empirical dataset containing Post-Traumatic Stress Disorder symptom assessments of participants who experienced the Wenchuan earthquake in 2008. The most important limitations of the method are discussed, as well as recommendations for future research, such as shifting towards modeling individual processes to validate these types of simulation-based intervention methods.

Detecting Idiographic Personality Change

Personality changes across the lifespan, but strong evidence regarding the mechanisms responsible for personality change remains elusive. Studies of personality change and life events, for example, suggest that personality is difficult to change. But there are two key issues with assessing personality change. First, most change models optimize population-level, not individual-level, effects, which ignores heterogeneity in patterns of change. Second, optimizing change as mean-levels of self-reports fails to incorporate methods for assessing personality dynamics, such as using changes in variances of and correlations in multivariate time series data that often proceed changes in mean-levels, making variance change detection a promising technique for the study of change. Using a sample of N = 388 participants (total N = 21,790) assessed weekly over 60 weeks, we test a permutation-based approach for detecting individual-level personality changes in multivariate time series and compare the results to event-based methods for assessing change. We find that a non-trivial number of participants show change over the course of the year but that there was little association between these change points and life events they experienced. We conclude by highlighting the importance in idiographic and dynamic investigations of change.

Conceptualizing and Studying Characteristics, Units, and Fits of Persons and Environments: A Coherent Synthesis

Based on a perspective on personality coherence as the extent to which personality-relevant characteristics are differentiated and integrated within a person in his or her environment, we propose a synthesis that builds on and harmonizes existing and partly conflicting theories, methodological approaches, and empirical findings. This understanding of personality coherence needs clear definitions of person and environment characteristics. We define traits as characteristics of the person, adaptations as characteristics of the person-in-contexts, and states as characteristics of the person-in-situations. Thus, our synthesis involves concepts of environments and person-environment units. Next, we provide testable criteria to differentiate characteristics of persons from characteristics of person-environment units and to identify dispositional traits for a narrow-sense perspective on personality coherence. We raise awareness of the importance of fit between (profiles of) person and environment characteristics for an understanding of the integrated uniqueness of persons in their environments. We outline implications of this broader perspective on personality coherence for personality development, self-regulation, social integration, well-being, and psychological interventions. Lastly, we conclude that the analysis of an individual’s uniqueness and personality differences requires information about how well-defined, well-differentiated, well-integrated, and well-operationalized person(ality) variables are actually expressed in, or interact and transact with, the individual environment.

Idiographic personality coherence: A quasi experimental longitudinal ESM study

Personality is a study of persons. However, persons exist within contexts, and personality coherence emerges from persons in contexts. But persons and environments bidirectionally influence each other, with persons selecting into and modifying their contexts, which also have lasting influences on personality. Thus, environmental change should produce changes in personality. Alternatively, environmental changes may produce few changes. This paradoxical viewpoint is based on the idea that novel environments have no predefined appropriate way to behave, which allows preexisting personality systems to stay coherent. We test these two perspectives by examining longitudinal consistency idiographic personality coherence using a quasi-experimental design (N = 50; total assessments = 5093). Personality coherence was assessed up to one year before the COVID-19 pandemic and again during lockdown. We also test antecedents and consequences of consistency, examining both what prospectively predicts consistency and what consistency prospectively predicts. Overall, consistency was modest but there were strong individual differences, indicating some people were quite consistent despite environmental upheaval. Moreover, there were relatively few antecedents and consequences of consistency, with the exception of some goals and domains of satisfaction predicting consistency, leaving open the question of why changes in coherence occur.

Understanding Personality through Patterns of Daily Socializing: Applying Recurrence Quantification Analysis to Naturalistically Observed Intensive Longitudinal Social Interaction Data

Ambulatory assessment methods provide a rich approach for studying daily behaviour. Too often, however, these data are analysed in terms of averages, neglecting patterning of this behaviour over time. This paper describes recurrence quantification analysis (RQA), a non–linear time series technique for analysing dynamic systems, as a method for analysing patterns of categorical, intensive longitudinal ambulatory assessment data. We apply RQA to objectively assessed social behaviour (e.g. talking to another person) coded from the Electronically Activated Recorder. Conceptual interpretations of RQA parameters, and an analysis of Electronically Activated Recorder data in adults going through a marital separation, are provided. Using machine learning techniques to avoid model overfitting, we find that adding RQA parameters to models that include just average amount of time spent talking (a static measure) improves prediction of four Big Five personality traits: extraversion, neuroticism, conscientiousness, and openness. Our strongest results suggest that a combination of average amount of time spent talking and four RQA parameters yield an R2 = .09 for neuroticism. Neuroticism is shown to be associated with shorter periods of extended conversation (periods of at least 12 minutes), demonstrating the utility of RQA to identify new relationships between personality and patterns of daily behaviour. Materials: https://osf.io/5nkr9/ . © 2020 European Association of Personality Psychology