Why do owls have it worse? Mediating role of self-perceptions in the links between diurnal preference and features of mental health

Recent research provides evidence for the negative social perceptions of evening chronotypes and their consequences on mental health. However, there is a lack of studies indicating whether these negative, socially shared beliefs may become internalized in negative self-perceptions of evening-types (E-types). The present article provides a seminal empirical analysis of the role of self-liking and self-competence in the associations between chronotype and both depressiveness and well-being. In the first part of the study, the participants completed the Composite Scale of Morningness. On the basis of the chronotype cut-off criteria for Composite Scale of Morningness distribution, 100 individuals were classified as morning-types (M-types) and 66 individuals as E-types. Therefore, 166 participants (80 women and 86 men) aged 18–36 years (M ± SD: 29.27 ± 4.81 years) took part in the second part of the study, and completed questionnaires measuring self-liking, self-competence, life satisfaction, positive and negative affect, and depressiveness. Results show that E-types scored lower in self-liking, self-competence and subjective well-being, and higher in depressive symptoms than M-types. Controlling for age and gender, we obtained significant mediation effects, showing that the relationship between chronotype and subjective well-being might stem from the lower levels of self-liking and self-competence among E-types, and that the relationship between chronotype and depressive symptoms might stem from the lower level of self-liking among E-types. Our results suggest that self-liking and self-competence are important antecedents of lower well-being and higher depressiveness reported by E-types. Socially shared stereotypes of M-types and E-types can be internalized by the extreme chronotypes, which may significantly affect their psychological health

Spatiotemporal complexity patterns of resting‐state bioelectrical activity explain fluid intelligence : sex matters

Neural complexity is thought to be associated with efficient information processing but the exact nature of this relation remains unclear. Here, the relationship of fluid intelligence (gf) with the resting‐state EEG (rsEEG) complexity over different timescales and different electrodes was investigated. A 6‐min rsEEG blocks of eyes open were analyzed. The results of 119 subjects (57 men, mean age = 22.85 ± 2.84 years) were examined using multivariate multiscale sample entropy (mMSE) that quantifies changes in information richness of rsEEG in multiple data channels at fine and coarse timescales. gf factor was extracted from six intelligence tests. Partial least square regression analysis revealed that mainly predictors of the rsEEG complexity at coarse timescales in the frontoparietal network (FPN) and the temporo‐parietal complexities at fine timescales were relevant to higher gf. Sex differently affected the relationship between fluid intelligence and EEG complexity at rest. In men, gf was mainly positively related to the complexity at coarse timescales in the FPN. Furthermore, at fine and coarse timescales positive relations in the parietal region were revealed. In women, positive relations with gf were mostly observed for the overall and the coarse complexity in the FPN, whereas negative associations with gf were found for the complexity at fine timescales in the parietal and centro‐temporal region. These outcomes indicate that two separate time pathways (corresponding to fine and coarse timescales) used to characterize rsEEG complexity (expressed by mMSE features) are beneficial for effective information processing

Mobilize is a Verb

In three studies, we investigated the role of linguistic features characterizing texts aiming to mobilize others. In Study 1 (N = 728), participants produced a leaflet either mobilizing others to engage in an action or expressing their thoughts about that action, and evaluated how action-oriented their text was. Mobilizing texts included more verbs and concrete words, and the presence of these linguistic characteristics was positively linked to participants’ evaluations of their messages as action-oriented. In Studies 2 and 3 (N = 557 and N = 556), independent groups of participants evaluated texts produced in Study 1. Readers’ perceptions of texts as action-oriented were associated with the same linguistic features as in Study 1 and further positively linked to perceived message effectiveness (Study 2) and behavioral intention (Study 3). The studies reveal how encoding and decoding of verbs and concrete words serve as distinct persuasive tools in calls to action

BERTAgent: The Development of a Novel Tool to Quantify Agency in Textual Data

Agency, pertaining to goal-orientation and achievement, is a fundamental aspect of human cognition and behavior. Accordingly, detecting and quantifying linguistic representations of agency is critical for the analysis of human actions, interactions, and social dynamics. Available agency-quantifying computational tools rely on word-counting methods, which are insensitive to the semantic context in which the words are used and consequently are inaccurate in case of polysemy and negation. Additionally, some currently available tools fail to account for differences in the intensity and directionality (valence) of agency. In order to overcome these shortcomings, we present BERTAgent, a novel tool to quantify semantic agency in text. BERTAgent is a computational language model that utilizes the transformers architecture, a popular deep learning approach to natural language processing. BERTAgent was fine-tuned using carefully selected textual data that were evaluated by human coders with respect to the level of conveyed agency. In five validation studies, we demonstrate that BERTAgent outperforms previous solutions in terms of convergent and discriminant validity. Additionally, the detailed description of BERTAgent’s development procedure serves as a tutorial for the advancement of similar tools, providing a blueprint for leveraging the existing lexicographical datasets in conjunction with the deep learning techniques in order to detect and quantify other psychological constructs in textual data. https://pypi.org/project/bertagent/ https://bertagent.readthedocs.io/ https://github.com/cogsys-io/BERTAgent-SOM/ https://github.com/cogsys-io/bertagent