Subjectifying the Personality State: Theoretical Underpinnings and an Empirical Example

Recent developments in personality research highlight the value of modelling dynamic state-like manifestations of personality. The present work integrates these developments with prominent clinical models addressing within-person multiplicity and promotes the exploration of models centred on state-like manifestations of personality that function as cohesive organizational units. Such units possess distinct subjective qualities and are characterized by specific affects, behaviours, cognitions, and desires that tend to be co-activated. As background, we review both theory and research from the fields of social cognition, psychotherapy, and psychopathology that serve as the foundation for such models. We then illustrate our ideas in greater detail with one well-supported clinical model—the schema therapy mode model, chosen because it provides a finite and definite set of modes (i.e. cohesive personality states). We assessed these modes using a newly developed experience-sampling measure administered to 52 individuals (four times daily for 15 days). We estimated intraindividual and group-level temporal and contemporaneous networks based on the within-person variance as well as between-person network. We discuss findings from exemplar participants and from group-level networks and address cross-model particularities and consistencies. In conclusion, we consider potential idiographic and nomothetic applications of subjective states dynamic personality research based on intensive longitudinal methods. © 2020 European Association of Personality Psychology     

‘Personality in Its Natural Habitat’ Revisited: A Pooled,Multi-sample Examination of the Relationships Between the Big Five Personality Traits and Daily Behaviour and Language Use

Past research using the Electronically Activated Recorder (EAR), an observational ambulatory assessment method for the real-world measurement of daily behaviour, has identified several behavioural manifestations of the Big Five domains in a small college sample (N = 96). With the use of a larger and more diverse sample of pooled data from N = 462 participants from a total of four community samples who wore the EAR from 2 to 6 days, the primary purpose of the present study was to obtain more precise and generalizable effect estimates of the Big Five–behaviour relationships and to re-examine the degree to which these relationships are gender specific. In an extension of the original article, the secondary purpose of the present study was to examine if the Big Five–behaviour relationships differed across two facets of each Big Five domain. Overall, while several of the behavioural manifestations of the Big Five were generally consistent with the trait definitions (replicating some findings from the original article), we found little evidence of gender differences (not replicating a basic finding from the original article). Unique to the present study, the Big Five–behaviour relationships were not always comparable across the two facets of each Big Five domain. © 2020 European Association of Personality Psychology     

New Directions in the Conceptualization and Assessment of Personality—A Dynamic Systems Approach

In this paper, we demonstrate how an integrative approach to personality—one that combines within-person and between-person differences—can be achieved by drawing on the principles of dynamic systems theory. The dynamic systems perspective has the potential to reconcile both the stable and dynamic aspect of personality, it allows including different levels of analysis (i.e. traits and states), and it can account for regulatory mechanisms, as well as dynamic interactions between the elements of the system, and changes over time. While all of these features are obviously appealing, implementing a dynamic systems approach to personality is challenging. It requires new conceptual models, specific longitudinal research designs, and complex data analytical methods. In response to these issues, the first part of our paper discusses the Personality Dynamics model, a model that integrates the dynamic systems principles in a relatively straightforward way. Second, we review associated methodological and statistical tools that allow empirically testing the PersDyn model. Finally, the model and associated methodological and statistical tools are illustrated using an experience sampling methodology data set measuring Big Five personality states in 59 participants (N = 1916 repeated measurements). © 2020 The Authors. European Journal of Personality published by John Wiley & Sons Ltd on behalf of European Association of Personality Psychology     

Personality,confirmation bias,and forensic interviewing performance

Confirmation bias is a universal characteristic of human cognition, with consequences for information processing and reasoning in everyday situations as well as in professional work such as forensic interviewing. Cognitive measures such as general intelligence are also related to personality traits, but there is a lack of research on personality and confirmation bias specifically. This study focuses, firstly, on the relationship between Big Five personality traits and confirmation bias as measured by the Wason selection task, and secondly, how these dispositions are related to observed performance in real forensic interviews of child victims. In a sample of police interviewers, Openness (i.e., the facets Ideas and Fantasy) and Neuroticism (i.e., the facets Anxiety and Vulnerability) were independently associated with confirmation bias (N = 72). Scores on the selection task, the Openness facet values, and the Extraversion facets Assertiveness and Activity were consistently related to interview performance (N = 46). Implications of these findings are discussed for the empirical and conceptual relations of cognitive ability to personality and for the selection and training of police interviewers and their professional development.     

To Challenge the Morning Lark and the Night Owl: Using Smartphone Sensing Data to Investigate Day–Night Behaviour Patterns

For decades, day–night patterns in behaviour have been investigated by asking people about their sleep–wake timing, their diurnal activity patterns, and their sleep duration. We demonstrate that the increasing digitalization of lifestyle offers new possibilities for research to investigate day–night patterns and related traits with the help of behavioural data. Using smartphone sensing, we collected in vivo data from 597 participants across several weeks and extracted behavioural day–night pattern indicators. Using this data, we explored three popular research topics. First, we focused on individual differences in day–night patterns by investigating whether ‘morning larks’ and ‘night owls’ manifest in smartphone-sensed behavioural indicators. Second, we examined whether personality traits are related to day–night patterns. Finally, exploring social jetlag, we investigated whether traits and work weekly day–night behaviours influence day–night patterns on weekends. Our findings highlight that behavioural data play an essential role in understanding daily routines and their relations to personality traits. We discuss how psychological research can integrate new behavioural approaches to study personality.     

“自爱”指标与维度的初探——《四库全书》中“自爱”的心理学研究

从《四库全书》中检索到1700部涉及“自爱”的古籍,形成包含2370条句段的语料库并逐条进行内容分析。结果发现,自爱包含自我珍重、自我接纳与自我约束三个主要指标以及个我自爱、小我自爱与大我自爱三个主要维度。未来研究将依据我国社会文化和历史的脉络,结合新时代人们对自爱的理解,编制相应的测量工具,从而为健全人格养成服务。     

Facilitating Adolescent Well‐Being: A Review of the Challenges and Opportunities and the Beneficial Roles of Parents,Schools, Neighborhoods,and Policymakers

Adolescents face exceptional challenges and opportunities that may have a lifelong impact on their consumption and personal and societal well‐being. Parents, community members (schools and neighborhoods), and policymakers play major roles in shaping adolescents and influencing their engagement in consumption behaviors that are either developmentally problematic (e.g., drug use and unhealthy eating) or developmentally constructive (e.g., academic pursuits and extracurricular activities). In this article, we discuss two main topics: (a) the challenges and opportunities that characterize adolescence, based primarily on research in epidemiology and neuroscience, and (b) the ways that parents, community members, and policymakers can facilitate positive adolescent development, based on research from many disciplines including marketing, psychology, sociology, communications, public health, and education. Our goal is to summarize the latest scientific findings that can be used by various stakeholders to help adolescents navigate this turbulent period and become well‐adjusted, thriving adults.     

Mapping differential responses to cognitive training using machine learning

We used two simple unsupervised machine learning techniques to identify differential trajectories of change in children who undergo intensive working memory (WM) training. We used self‐organizing maps (SOMs)—a type of simple artificial neural network—to represent multivariate cognitive training data, and then tested whether the way tasks are represented changed as a result of training. The patterns of change we observed in the SOM weight matrices implied that the processes drawn upon to perform WM tasks changed following training. This was then combined with K‐means clustering to identify distinct groups of children who respond to the training in different ways. Firstly, the K‐means clustering was applied to an independent large sample (N = 616, M_age = 9.16 years, range = 5.16–17.91 years) to identify subgroups. We then allocated children who had been through cognitive training (N = 179, M_age = 9.00 years, range = 7.08–11.50 years) to these same four subgroups, both before and after their training. In doing so, we were able to map their improvement trajectories. Scores on a separate measure of fluid intelligence were predictive of a child's improvement trajectory. This paper provides an alternative approach to analysing cognitive training data that go beyond considering changes in individual tasks. This proof‐of‐principle demonstrates a potentially powerful way of distinguishing task‐specific from domain‐general changes following training and of establishing different profiles of response to training.