Estimating the Associations between Big Five Personality Traits, Testosterone, and Cortisol

OBJECTIVE: Hormones are often conceptualized as biological markers of individual differences and have been associated with a variety of behavioral indicators and characteristics, such as mating behavior or acquiring and maintaining dominance. However, before researchers create strong theoretical models for how hormones modulate individual and social behavior, information on how hormones are associated with dominant models of personality is needed. Although there have been some studies attempting to quantify the associations between personality traits, testosterone, and cortisol, there are many inconsistencies across these studies. METHODS: In this registered report, we examined associations between testosterone, cortisol, and Big Five personality traits. We aggregated 25 separate samples to yield a single sample of 3964 (50.3% women; 27.7% of women were on hormonal contraceptives). Participants completed measures of personality and provided saliva samples for testosterone and cortisol assays. RESULTS: The results from multi-level models and meta-analyses revealed mostly weak, non-significant associations between testosterone or cortisol and personality traits. The few significant effects were still very small in magnitude (e.g., testosterone and conscientiousness: r = −0.05). A series of moderation tests revealed that hormone-personality associations were mostly similar in men and women, those using hormonal contraceptives or not, and regardless of the interaction between testosterone and cortisol (i.e., a variant of the dual-hormone hypothesis). CONCLUSIONS: Altogether, we did not detect many robust associations between Big Five personality traits and testosterone or cortisol. The findings are discussed in the context of biological models of personality and the utility of examining heterogeneity in hormone-personality associations

Timescales of Massive Human Entrainment

The past two decades have seen an upsurge of interest in the collective behaviors of complex systems composed of many agents entrained to each other and to external events. In this paper, we extend concepts of entrainment to the dynamics of human collective attention. We conducted a detailed investigation of the unfolding of human entrainment - as expressed by the content and patterns of hundreds of thousands of messages on Twitter - during the 2012 US presidential debates. By time locking these data sources, we quantify the impact of the unfolding debate on human attention. We show that collective social behavior covaries second-by-second to the interactional dynamics of the debates: A candidate speaking induces rapid increases in mentions of his name on social media and decreases in mentions of the other candidate. Moreover, interruptions by an interlocutor increase the attention received. We also highlight a distinct time scale for the impact of salient moments in the debate: Mentions in social media start within 5-10 seconds after the moment; peak at approximately one minute; and slowly decay in a consistent fashion across well-known events during the debates. Finally, we show that public attention after an initial burst slowly decays through the course of the debates. Thus we demonstrate that large-scale human entrainment may hold across a number of distinct scales, in an exquisitely time-locked fashion. The methods and results pave the way for careful study of the dynamics and mechanisms of large-scale human entrainment.Comment: 20 pages, 7 figures, 6 tables, 4 supplementary figures. 2nd version revised according to peer reviewers' comments: more detailed explanation of the methods, and grounding of the hypothese

How do I love thee? Let me count the words: The social effects of expressive writing

ABSTRACT—Writing about emotional experiences is associated with a host of positive outcomes. This study extended the expressive-writing paradigm to the realm of romantic relationships to examine the social effects of writing. For 3 consecutive days, one person from each of 86 dating couples either wrote about his or her deepest thoughts and feelings about the relationship or wrote about his or her daily activities. In the days before and after writing, instant messages were collected from the couples. Participants who wrote about their relationship were significantly more likely to still be dating their romantic partners 3 months later. Linguistic analyses of the instant messages revealed that participants and their partners used significantly more positive and negative emotion words in the days following the expressive-writing manipulation if the participants had written about their relationship than if they had written about their daily activities. Increases in positive emotion words partially mediated the relation between expressive writing and relationship stability. Over the past two decades, multiple studies have demonstrated the positive benefits of expressive writing in domains as diverse as health, achievement, and well-being. Most of these studies have used a relatively straightforward procedure in which participants write about their deepest thoughts and feelings about a particular topic for 20 min a day, 3 or 4 days in a row. The findings indicate that expressive writing can result in fewer doctor visits, fewer depressive symptoms, enhanced immune system functioning, better grades, and a host of other positiv

Automated lexical analysis of interviews with individuals with schizophrenia

Schizophrenia is a chronic mental disorder that contributes to poor function and quality of life. We are aiming to design objective assessment tools of schizophrenia. In earlier work, we investigated non-verbal quantitative cues for this purpose. In this paper, we explore linguistic cues, extracted from interviews with patients with schizophrenia and healthy control subjects, conducted by trained psychologists. Specifically, we analyzed the interviews of 47 patients and 24 healthy age-matched control subjects. We applied automated speech recognition and linguistic tools to capture the linguistic categories of emotional and psychological states. Based on those linguistic categories, we applied a binary classifier to distinguish patients from matched control subjects, leading to a classification accuracy of about 86% (by leave-one-out cross-validation); this result seems to suggest that patients with schizophrenia tend to talk about different topics and use different words. We provided an in-depth discussion of the most salient lexical features, which may provide some insights into the linguistic alterations in patients.NRF (Natl Research Foundation, S’pore)NMRC (Natl Medical Research Council, S’pore)Accepted versio