A formal modeling framework for the dynamics of subjective well-being, including satisfaction with interpersonal relationships

AgencyJohn Templeton Foundation
PanelSocial Sciences
LocationUniversity of California, Irvine
Start dateAugust 2014
End dateAugust 2017
Budget$ 540,018.00
Agency code48192
LeadJoachim Vandekerckhove
OtherZita Oravecz
Project text pn-48192.pdf

Abstract

We will study the componential structure and mechanisms of subjective well-being (SWB) and its dynamic and interactive behavior over time using modern mathematical and computational models and techniques. As part of this, we will also study the role of cognitive evaluations of feeling loved.

We aim to develop an integrative formal framework for modeling SWB to contribute to our understanding of substantively interesting issues. The dynamical property of the framework will allow us to formalize various mechanisms of change, adaptation, and variation in SWB and predictors of inter-individual differences through intensive longitudinal designs (ILD).

We propose a three-pronged approach. First, we will use established psychometric methods to develop a tool to measure SWB as a multidimensional construct. The tool will be specifically designed to be used in ILDs. For a componential structure, we will adapt Martin Seligman's PERMA model, in which five dimensions of SWB (Positive emotions, Engagement, positive Relationships, Meaning, Accomplishment) are considered. For one dimension in particular, the "positive relationships" component, we will additionally apply an extended Cultural Consensus Model in order to quantify the cognitive aspect of feeling loved in daily life. Second, we will develop and apply a multidimensional stochastic differential equation model to capture the temporal dynamics of SWB, including the possible interactions between dimensions. Third, we will gather longitudinal data with the new measurement instrument and study the effects of positive psychology interventions using the new model.

Publications

Heshmati, S., Oravecz, Z., Pressman, S., Batchelder, W., Muth, C., & Vandekerckhove, J. (2019). What does it mean to feel loved? Cultural agreement and individual differences. Journal of Social and Personal Relationships, 36, 214-243.
Matzke, D., Boehm, U., & Vandekerckhove, J. (2018). Bayesian Inference in Psychology, Part III: Bayesian parameter estimation in nonstandard models. Psychonomic Bulletin & Review, 25, 77–101.
Etz, A., & Vandekerckhove, J. (2018). Introduction to Bayesian inference for psychology. Psychonomic Bulletin & Review, 25, 5–34.
Mistry, P., Pothos, E., Vandekerckhove, J., & Trueblood, J. (2018). A quantum probability account of individual differences in causal reasoning. Journal of Mathematical Psychology, 87, 76–97.
Nunez, M. D., Vandekerckhove, J., & Srinivasan, R. (2017). How attention influences perceptual decision making: Single-trial EEG correlates of drift-diffusion model parameters. Journal of Mathematical Psychology, 76, 117–130.
Oravecz, Z., Huentelman, M., & Vandekerckhove, J. (2016). Sequential Bayesian updating for Big Data. Big Data in Cognitive Science: From Methods to Insights, 13–33.
Guan, M., & Vandekerckhove, J. (2016). A Bayesian approach to mitigation of publication bias. Psychonomic Bulletin & Review, 23, 74–86.
Oravecz, Z., Tuerlinckx, F., & Vandekerckhove, J. (2016). Bayesian data analysis with the bivariate hierarchical Ornstein-Uhlenbeck process model. Multivariate Behavioral Research, 51, 106–119.
Etz, A., & Vandekerckhove, J. (2016). A Bayesian perspective on the Reproducibility Project: Psychology. PLoS ONE, 11, e0149794.
Oravecz, Z., Muth, C., & Vandekerckhove, J. (2016). Do people agree on what makes one feel loved? A cognitive psychometric approach to the consensus on felt love. PLoS ONE, 11, e0152803.
Nunez, M. D., Srinivasan, R., & Vandekerckhove, J. (2015). Individual differences in attention influence perceptual decision making. Frontiers in Psychology, 6, 18.
Guan, M., Lee, M. D., & Vandekerckhove, J. (2015). A hierarchical cognitive threshold model of human decision making on different length optimal stopping problems. Proceedings of the 37th Annual Conference of the Cognitive Science Society, 824–829.
Kupitz, C., Buschkuehl, M., Jaeggi, S., Jonides, J., Shah, P., & Vandekerckhove, J. (2015). A diffusion model account of the transfer-of-training effect. Proceedings of the 37th Annual Conference of the Cognitive Science Society.