Matthias R. Mehl

1. Trauma-exposed adolescents are at risk for a host of deleterious outcomes, particularly stress-sensitive disorders such as acute stress disorder (ASD), post-traumatic stress disorder (PTSD), dep...

In psychology, mobile sensing is increasingly used to record behavior in real-life situations. However, little is known about the selectivity of samples participating in these new data collection approaches and thus about potential risks to the validity of research findings. We therefore investigated two potential sources of selection bias in smartphone-based data collections. Specifically, we examined whether smartphone system ownership (Android vs. iOS, i.e., platform-related differences) and willingness to participate (nonparticipation vs. intention to participate vs. actual participation, i.e., nonresponse error) are associated with sociodemographic, socioeconomic, and personality characteristics. Using two large-scale panel studies, we found replicable patterns for platform-related differences (N = 1,218 and N = 5,123) and nonresponse error (N = 1,673 and N = 2,337): The ownership of Android devices (in comparison to iOS devices) was associated with lower levels of education, income, and extraversion. The willingness to participate in mobile sensing studies was found to be higher among younger age groups, males, those with higher levels of openness to experience, and those with lower levels of neuroticism. Furthermore, different person characteristics played different roles at different stages of the recruitment process. Taken together, the results show that some selection bias in mobile sensing studies exists and that the effects were small to moderate in magnitude as well as comparable to selection bias for other, more common data collection approaches, such as online surveys. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

Brian Wilson of The Beach Boys is considered one of the most prolific artists of the 20th century. At the same time, he is also known for his persisting struggles with mental health. Considering that most of his 60+-year career consists of published songs and lyrics, he makes an interesting case study to understand the dynamics of language use in relation to psychological experiences over the course of an individual’s life and career. We identified song lyrics for which he is credited as (co-)writer and analyzed their lyrics ( N = 250) using the Linguistic Inquiry and Word Count (LIWC) program. Considering features previously associated with mental health (I-talk, Emotional Tone, Analytic language and Cognitive Processes words) over time, we found that I-talk and Cognitive Processes showed an initial increase, followed by a plateau (in 1967), while Emotional Tone showed a cubic relationship with time, being sensitive to changes in Brian Wilson’s life. Analytic language was highly variable and did not show any patterns. Our data suggest that even carefully crafted language, such as song lyrics, can carry information about the writer’s mental health and that considering language use over the course of an individual’s lifespan can provide crucial insights into psychological processes.

The COVID-19 pandemic posed a global threat to nearly every society around the world. Individuals turned to their political leaders to safely guide them through this crisis. The most direct way political leaders communicated with their citizens was through official speeches and press conferences. In this report, we compare psychological language markers of four different heads of state during the early stage of the pandemic. Specifically, we collected all pandemic-related speeches and press conferences delivered by political leaders in the USA (Trump), UK (Johnson), Germany (Merkel), and Switzerland (Swiss Federal Council) between February 27th and August 31st, 2020. We used natural language analysis to examine language markers of expressed positive and negative emotions, references to the community (we-talk), analytical thinking, and authenticity and compare these language markers across the four nations. Level differences in the language markers between the leaders can be detected: Trump’s language was characterized by a high expression of positive emotion, Merkel’s by a strong communal focus, and Johnson’s and the Swiss Federal Council by a high level of analytical thinking. Overall, these findings mirror different strategies used by political leaders to deal with the COVID-19 pandemic.

Social networks are the persons surrounding a patient who provide support, circulate information, and influence health behaviors. For patients seen by neurologists, social networks are one of the most proximate social determinants of health that are actually accessible to clinicians, compared with wider social forces such as structural inequalities. We can measure social networks and related phenomena of social connection using a growing set of scalable and quantitative tools increasing familiarity with social network effects and mechanisms. This scientific approach is built on decades of neurobiological and psychological research highlighting the impact of the social environment on physical and mental well-being, nervous system structure, and neuro-recovery. Here, we review the biology and psychology of social networks, assessment methods including novel social sensors, and the design of network interventions and social therapeutics.

Background: Language use and social interactions have demonstrated a close relationship with cognitive measures. It is important to improve the understanding of language use and behavioral indicators from social context to study the early prediction of cognitive decline among healthy populations of older adults. Objective: This study aimed at predicting an important cognitive ability, working memory, of 98 healthy older adults participating in a 4-day-long naturalistic observation study. We used linguistic measures, part-of-speech (POS) tags, and social context information extracted from 7450 real-life audio recordings of their everyday conversations. Methods: The methods in this study comprise (1) the generation of linguistic measures, representing idea density, vocabulary richness, and grammatical complexity, as well as POS tags with natural language processing (NLP) from the transcripts of real-life conversations and (2) the training of machine learning models to predict working memory using linguistic measures, POS tags, and social context information. We measured working memory using (1) the Keep Track test, (2) the Consonant Updating test, and (3) a composite score based on the Keep Track and Consonant Updating tests. We trained machine learning models using random forest, extreme gradient boosting, and light gradient boosting machine algorithms, implementing repeated cross-validation with different numbers of folds and repeats and recursive feature elimination to avoid overfitting. Results: For all three prediction routines, models comprising linguistic measures, POS tags, and social context information improved the baseline performance on the validation folds. The best model for the Keep Track prediction routine comprised linguistic measures, POS tags, and social context variables. The best models for prediction of the Consonant Updating score and the composite working memory score comprised POS tags only. Conclusions: The results suggest that machine learning and NLP may support the prediction of working memory using, in particular, linguistic measures and social context information extracted from the everyday conversations of healthy older adults. Our findings may support the design of an early warning system to be used in longitudinal studies that collects cognitive ability scores and records real-life conversations unobtrusively. This system may support the timely detection of early cognitive decline. In particular, the use of a privacy-sensitive passive monitoring technology would allow for the design of a program of interventions to enable strategies and treatments to decrease or avoid early cognitive decline.