Investigating the association between social interactions and personality states dynamics

Dark triad personality traits are associated with decreased grey matter volumes in 'social brain' structures

Introduction

Personality traits and the degree of their prominence determine various aspects of social interactions. Some of the most socially relevant traits constitute the Dark Triad - narcissism, psychopathy, and Machiavellianism - associated with antisocial behaviour, disregard for moral norms, and a tendency to manipulation. Sufficient data point at the existence of Dark Triad 'profiles' distinguished by trait prominence. Currently, neuroimaging studies have mainly concentrated on the neuroanatomy of individual dark traits, while the Dark Triad profile structure has been mostly overlooked.

Methods

We performed a clustering analysis of the Dirty Dozen Dark Triad questionnaire scores of 129 healthy subjects using the k-means method. The variance ratio criterion (VRC) was used to determine the optimal number of clusters for the current data. The two-sample t-test within the framework of voxel-based morphometry (VBM) was performed to test the hypothesised differences in grey matter volume (GMV) for the obtained groups.

Results

Clustering analysis revealed 2 groups of subjects, both with low-to-mid and mid-to-high levels of Dark Triad traits prominence. A further VBM analysis of these groups showed that a higher level of Dark Triad traits may manifest itself in decreased grey matter volumes in the areas related to emotional regulation (the dorsolateral prefrontal cortex, the cingulate cortex), as well as those included in the reward system (the ventral striatum, the orbitofrontal cortex).

Discussion

The obtained results shed light on the neurobiological basis underlying social interactions associated with the Dark Triad and its profiles.

Using passive sensor data to probe associations of social structure with changes in personality: A synthesis of network analysis and machine learning

Social network analysis (SNA) is an increasingly popular and effective tool for modeling psychological phenomena. Through application to the personality literature, social networks, in conjunction with passive, non-invasive sensing technologies, have begun to offer powerful insight into personality state variability. Resultant constructions of social networks can be utilized alongside machine learning-based frameworks to uniquely model personality states. Accordingly, this work leverages data from a previously published study to combine passively collected wearable sensor information on face-to-face, workplace social interactions with ecological momentary assessments of personality state. Data from 54 individuals across six weeks was used to explore the relative importance of 26 unique structural and nodal social network features in predicting individual changes in each of the Big Five (5F) personality states. Changes in personality state were operationalized by calculating the weekly root mean square of successive differences (RMSSD) in 5F state scores measured daily via self-report. Using only SNA-derived features from wearable sensor data, boosted tree-based machine learning models explained, on average, approximately 28-30% of the variance in individual personality state change. Model introspection implicated egocentric features as the most influential predictors across 5F-specific models, with network efficiency, constraint, and effective size measures among the most important. Feature importance profiles for each 5F model partially echoed previous empirical findings. Results support future efforts focusing on egocentric components of SNA and suggest particular investment in exploring efficiency measures to model personality fluctuations within the workplace setting.

Use of Technology in the Study of Team-Interaction and Performance

Direct observation of groups is labor-intensive. As a result, current research on small groups often relies on retrospective ratings. Recent developments in sensor-technology have eased data gathering, leading to a renewed interest in direct observation of groups. Sensor technology has potential, but also limitations; research has been technology- and data-driven with less recognition of the large body, and long history, of research and theory building. We review the literature on technology in small group research, argue for more interdisciplinary research and propose combining sensor technology with methods of interaction analysis, and the theories that underlie them, developed prior to 1980.

Developing a Social Robot - A Case Study

Social robotics is currently challenging researchers to look at virtually every topic with relevance for human societies. The multidisciplinary nature of this new area is emerging at a fast pace and, at the same time, multiple challenges are also emerging.This chapter addresses the development of a social robot, including baseline work developed during the European FP7 Project Monarch on a social robot for edutainment activities for inpatient children in the Pediatrics ward of an oncological hospital, and the work developed in the post-project period. The long period (Monarch started in early 2013) allowed a diversity of experiments that resulted in valuable lessons and in the blossoming of new ideas and challenges.The overview of the Monarch project is the leitmotiv for a critical view of social robotics and the identification of key challenges, on the light of current technologies and social trends and expectations.

Pathways to conspiracy: The social and linguistic precursors of involvement in Reddit's conspiracy theory forum

Many individuals who engage with conspiracy theories come to do so through a combination of individual and social factors. The interaction between these factors is challenging to study using traditional experimental designs. Reddit.com is a large connected set of online discussion forums, including one (r/conspiracy) devoted to wide-ranging discussion of conspiracy theories. The availability of large datasets of user comments from Reddit give a unique opportunity to observe human behavior in social spaces and at scale. Using a retrospective case control study design, we analyzed how Reddit users who would go on to engage with a conspiracy-related forum differed from other users in the language they use, differences in the social environments where they posted, and potential interactions between the two factors. Together, the analyses provide evidence for self-selection into communities with a shared set of interests which can feed into a conspiratorial world-view, and that these differences are detectable relative to controls even before users begin to post in r/conspiracy. We also suggest that survey-based and experimental studies may benefit from differentiating between passive private endorsement by individuals and active engagement with conspiracy theories in social spaces.