Charlotte Vrijen is an assistant professor at the University of Groningen. Her main research interest is positive bias, i.e., the phenomenon that many people view the world more positively and brightly than it really is. Positive bias may protect us from developing mental health problems.
Charlotte’s current research focuses on investigating when and how individual differences in positive bias arise, and what role genetic vulnerabilities and parenting play. In 2022, she started her Veni project ‘The bright side of life - An intergenerational study into the origins of optimism’.
Charlotte is passionate about improving scientific practice. She is a member of her department’s Ethics Committee and of the Open Science Community Groningen, where she gives pre-registration workshops. Examples of open science practices in her work are pre-registrations, sharing syntax and data, and publishing open access.
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PhD in psychiatry, 2019
University of Groningen
PhD in philosophy, 2007
University of Groningen
Master in philosophy, 2001
University of Groningen
Multilevel meta-analysis, multilevel regression analysis, longitudinal data analysis, network analysis of ecological momentary assessments. Experience with R, Mplus, SPSS, Python (very basic)
I currently collect data with surveys, momentary assessment tools, and behavioural tasks. I also have experience with biomarker collection and lifestyle interventions.
I use polygenic scores (indexes of genetic predisposition to specific phenotypes) to study intergenerational transmission.
I am a member of my department’s Ethics Committee and of the Open Science Community Groningen, where I give regular pre-registration workshops. Examples of open science practices in my work are pre-registrations, sharing syntax and data, and publishing open access.
I am daily supervisor of a PhD student working on parent-child transmission of peer problems, and have supervised bachelor and master theses, as well as student interns and assistants. Last year, I taught a third-year bachelor course on how to perform a systematic literature review.
Reviewer (on occasional basis) for the Journal of Child Psychology and Psychiatry, the International Journal of Cognitive Therapy, Frontiers in Psychology, and Psychology Research and Behavior Management.
CONTEXT: Previous meta-analyses substantially contributed to our understanding of increased drug use risk in bullies but only included research up to 2014 and did not report on other types of substances. OBJECTIVE: To review and meta-analyze existing evidence regarding the prospective association between peer bullying perpetration in childhood and adolescence and later substance use. DATA SOURCES: Electronic databases were searched on March 14, 2019. STUDY SELECTION: We selected peer-reviewed articles and dissertations in English reporting original empirical studies on associations between bullying perpetration in childhood or adolescence and later use of drugs, alcohol, or tobacco. Records were assessed for eligibility independently by 2 authors. DATA EXTRACTION: Data extraction and quality assessment was performed by one author and checked by another author. RESULTS: In total, 215 effects were included from 28 publications, reporting on 22 samples, comprising 28,477 participants. Bullying perpetration was associated positively with all types of substance use (drugs, alcohol, tobacco, and general). The results for combined bullying-victimization were more mixed, with generally weaker effects. LIMITATIONS: Effects were based on a large variability in operationalizations and measures of bullying and substance use, impeding the interpretation of the pooled effect sizes. Although bullying appears to be a risk factor for substance use, no inferences can be made about so-called causal risk factors that can provide the basis for preventive interventions. CONCLUSIONS: There is evidence that adolescents and particularly children who bully their peers have a higher risk of substance use later in life than their nonbullying peers.
There is evidence that reward responsiveness and optimism are associated with mental and social functioning in adolescence and adulthood, but it is unknown if this is also the case for young children. Part of the reason for this gap in the literature is that the instruments that are used to assess reward responsiveness and optimism in adolescents and adults are usually not suitable for young children. Two behavioral tasks to assess reward learning, a questionnaire on reward responsiveness, and a questionnaire on optimism/pessimism will be tested on their feasibility and reliability in children aged 6-7. We adapted these instruments to the needs of 6-7-year-old children, by simplification of items, oral rather than written assessment, and reducing the number of conditions and items. Depending on their feasibility and reliability, these instruments will also be used to investigate if reward responsiveness and optimism are associated with mental and social functioning in young children. We aim to include 70 children aged 6-7. Data collection was originally planned in March and April 2020, but has been postponed due to Corona virus regulations. We expect to collect the data in the first half of 2021.
This study investigated whether low reward responsiveness marks vulnerability for developing depression in a large cohort of never-depressed 16-year-old adolescents who completed a reward task and were subsequently followed for 9 years, during which onset of depression was assessed. Participants who completed the reward task at 16 years, had no previous onset of depression, and were assessed on depression onset at 19 and/or 25 years were included in the present study (N = 531; 81 became depressed during follow-up). The findings of this study suggest that decreased reward responsiveness at 16 years marks vulnerability for depression. Prevention programs may aim at increasing at-risk adolescents’ responsiveness to cues for potential rewards, particularly in situations in which they are focused on negative experiences.
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 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.
Facial emotion identification bias has been suggested as trait marker for depression, but results have been inconclusive. To explore whether facial emotion identification biases may be trait markers for depression, we tested whether the speed with which young adolescents identified happy, sad, angry and fearful facial emotions predicted the onset of depression during an eight-year follow-up period. Data were collected as part of the TRacking Adolescents’ Individual Lives Survey (TRAILS), and involved 1840 adolescents who participated in a facial emotion identification test at age 11 and were subjected to the World Health Organization Composite International Diagnostic Interview (CIDI) at age 19. In a multi-emotion model, slow identification of happy facial emotions tentatively predicted onset of depressive disorder within the follow-up period. Slow identification of happy emotions and fast identification of sad emotions predicted symptoms of anhedonia, but not symptoms of sadness. Our results suggest that the relative speed of identification of happiness in relation to the identification of sadness is a better predictor of depression than the identification of either facial emotion alone. A possible mechanism underlying the predictive role of facial emotion identification may be a less reactive reward system.