Joy

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 di ff erent daily life a ff ect dynamics (i.e., e ff ects from one time interval of 6 hours to the next). We compared the daily life a ff ect dynamics of young adults (age 18 – 24) with a high bias toward happy facial emotions ( N = 25 ) to the a ff ect dynamics of young adults with a low bias toward happy emotions ( N = 25 ). A ff ect and related measures were assessed three times per day during 30 days. We used multilevel vector autoregressive (VAR) modelling to estimate lag 1 a ff ect 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 e ff ects 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 bene fi ts of a network approach for unraveling psychological mechanisms.