Spread the Joy: How High and Low Bias for Happy Facial Emotions Translate into Different Daily Life Affect Dynamics

Charlotte Vrijen*, Catharina A. Hartman, Eeske van Roekel, Peter de Jonge, Albertine J. Oldehinkel

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

3 Citations (Scopus)
278 Downloads (Pure)


There is evidence that people commonly show a bias toward happy facial emotions during laboratory tasks, that is, they identify other people's happy facial emotions faster than other people's negative facial emotions. However, not everybody shows this bias. Individuals with a vulnerability for depression, for example, show a low happy bias compared to healthy controls. The main aim of this study was to acquire a better understanding of laboratory measures of happy bias by studying how these translate to people's daily life. We investigated whether stable high and low happy bias during a laboratory task were associated with different daily life affect dynamics (i.e., effects from one time interval of 6 hours to the next). We compared the daily life affect dynamics of young adults (age 18-24) with a high bias toward happy facial emotions (N=25) to the affect dynamics of young adults with a low bias toward happy emotions (N=25). Affect and related measures were assessed three times per day during 30 days. We used multilevel vector autoregressive (VAR) modelling to estimate lag 1 affect networks for the high and low happy bias groups and used permutation tests to compare the two groups. Compared to their peers with a low happy bias, individuals with a high happy bias more strongly sustained the effects of daily life reward experiences over time. Individuals with a high happy bias may use their reward experiences more optimally in daily life to build resources that promote well-being and mental health. Low reward responsiveness in daily life may be key to why individuals who show a low happy bias during laboratory tasks are vulnerable for depression. This study illustrates the potential benefits of a network approach for unraveling psychological mechanisms.

Original languageEnglish
Article number2674523
Number of pages15
Publication statusPublished - 2-Dec-2018


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