Showing papers on "Personality published in 2018"

ASCERTAIN: Emotion and Personality Recognition Using Commercial Sensors

Abstract: We present ASCERTAIN—a multimodal databa AS e for impli C it p ER sonali T y and A ffect recognit I o N using commercial physiological sensors. To our knowledge, ASCERTAIN is the first database to connect personality traits and emotional states via physiological responses . ASCERTAIN contains big-five personality scales and emotional self-ratings of 58 users along with their Electroencephalogram (EEG), Electrocardiogram (ECG), Galvanic Skin Response (GSR) and facial activity data, recorded using off-the-shelf sensors while viewing affective movie clips. We first examine relationships between users’ affective ratings and personality scales in the context of prior observations, and then study linear and non-linear physiological correlates of emotion and personality. Our analysis suggests that the emotion-personality relationship is better captured by non-linear rather than linear statistics. We finally attempt binary emotion and personality trait recognition using physiological features. Experimental results cumulatively confirm that personality differences are better revealed while comparing user responses to emotionally homogeneous videos, and above-chance recognition is achieved for both affective and personality dimensions.

Association analysis in over 329,000 individuals identifies 116 independent variants influencing neuroticism

Abstract: Neuroticism is a relatively stable personality trait characterized by negative emotionality (for example, worry and guilt)1; heritability estimated from twin studies ranges from 30 to 50%2, and SNP-based heritability ranges from 6 to 15%3–6. Increased neuroticism is associated with poorer mental and physical health7,8, translating to high economic burden9. Genome-wide association studies (GWAS) of neuroticism have identified up to 11 associated genetic loci3,4. Here we report 116 significant independent loci from a GWAS of neuroticism in 329,821 UK Biobank participants; 15 of these loci replicated at P < 0.00045 in an unrelated cohort (N = 122,867). Genetic signals were enriched in neuronal genesis and differentiation pathways, and substantial genetic correlations were found between neuroticism and depressive symptoms (rg = 0.82, standard error (s.e.) = 0.03), major depressive disorder (MDD; rg = 0.69, s.e. = 0.07) and subjective well-being (rg = –0.68, s.e. = 0.03) alongside other mental health traits. These discoveries significantly advance understanding of neuroticism and its association with MDD. Analysis of 329,000 individuals in the UK Biobank identifies 116 loci associated with neuroticism. Genes implicated are enriched in neuronal differentiation pathways, and genetic correlations between neuroticism and other mental health traits are elucidated.

Life Events and Personality Trait Change.

Abstract: Objective Theory and research have emphasized the impact of life events on personality trait change. In this article, we review prospective research on personality trait change in response to nine major life events in the broader domains of love and work. Method We expected to find that life events lead to personality trait change to the extent that they have a lasting influence on individuals' thoughts, feelings, and behavior. Moreover, we predicted that love-related life events such as marriage or parenthood would be more strongly related to changes in traits that emphasize affective content, whereas work-related life events would be more likely to lead to change in traits that reflect behavioral or cognitive content. Results The current state of research provided some evidence that life events can lead to changes in personality traits and that different life events may be differently related to specific trait domains. Conclusions A more general conclusion emerging from this review is that the evidence for the nature, shape, and timing of personality trait change in response to life events is still preliminary. We discuss the implications of the results for theory and research and provide directions for future studies on life events and personality trait change.

Instagram addiction and the Big Five of personality: The mediating role of self-liking

Abstract: Background and aims Recent research has suggested that social networking site use can be addictive. Although extensive research has been carried out on potential addiction to social networking sites, such as Facebook, Twitter, YouTube, and Tinder, only one very small study has previously examined potential addiction to Instagram. Consequently, the objectives of this study were to examine the relationships between personality, self-liking, daily Internet use, and Instagram addiction, as well as exploring the mediating role of self-liking between personality and Instagram addiction using path analysis. Methods A total of 752 university students completed a self-report survey, including the Instagram Addiction Scale (IAS), the Big Five Inventory (BFI), and the Self-Liking Scale. Results Results indicated that agreeableness, conscientiousness, and self-liking were negatively associated with Instagram addiction, whereas daily Internet use was positively associated with Instagram addiction. The results also showed that self-liking partially mediated the relationship of Instagram addiction with agreeableness and fully mediated the relationship between Instagram addiction with conscientiousness. Discussion and conclusions This study contributes to the small body of literature that has examined the relationship between personality and social networking site addiction and is one of only two studies to examine the addictive use of Instagram and the underlying factors related to it.

Application of the ICD-11 classification of personality disorders

Abstract: The ICD-11 classification of Personality Disorders focuses on core personality dysfunction, while allowing the practitioner to classify three levels of severity (Mild Personality Disorder, Moderate Personality Disorder, and Severe Personality Disorder) and the option of specifying one or more prominent trait domain qualifiers (Negative Affectivity, Detachment, Disinhibition, Dissociality, and Anankastia). Additionally, the practitioner is also allowed to specify a Borderline Pattern qualifier. This article presents how the ICD-11 Personality Disorder classification may be applied in clinical practice using five brief cases. (1) a 29-year-old woman with Severe Personality Disorder, Borderline Pattern, and prominent traits of Negative Affectivity, Disinhibition, and Dissociality; (2) a 36-year-old man with Mild Personality Disorder, and prominent traits of Negative Affectivity and Detachment; (3) a 26-year-old man with Severe Personality Disorder, and prominent traits of Dissociality, Disinhibition, and Detachment; (4) a 19-year-old woman with Personality Difficulty, and prominent traits of Negative Affectivity and Anankastia; (5) a 53-year-old man with Moderate Personality Disorder, and prominent traits of Anankastia and Dissociality. The ICD-11 Personality Disorder classification was applicable to five clinical cases, which were classified according to Personaity Disorder severity and trait domain qualifiers. We propose that the classification of severity may help inform clinical prognosis and intensity of treatment, whereas the coding of trait qualifiers may help inform the focus and style of treatment. Empirical investigation of such important aspects of clinical utility are warranted.

Prevalence of personality disorders in the general adult population in Western countries: systematic review and meta-analysis.

Abstract: Background Personality disorder is a severe health issue. However, the epidemiology of personality disorders is insufficiently described and surveys report very heterogeneous rates.AimsWe aimed to conduct a meta-analysis on the prevalence of personality disorders in adult populations and examine potential moderators that affect heterogeneity. Method We searched PsycINFO, PSYNDEX and Medline for studies that used standardised diagnostics (DSM-IV/-5, ICD-10) to report prevalence rates of personality disorders in community populations in Western countries. Prevalence rates were extracted and aggregated by random-effects models. Meta-regression and sensitivity analyses were performed and publication bias was assessed. Results The final sample comprised ten studies, with a total of 113 998 individuals. Prevalence rates were fairly high for any personality disorder (12.16%; 95% CI, 8.01-17.02%) and similarly high for DSM Clusters A, B and C, between 5.53 (95% CI, 3.20-8.43%) and 7.23% (95% CI, 2.37-14.42%). Prevalence was highest for obsessive-compulsive personality disorder (4.32%; 95% CI, 2.16-7.16%) and lowest for dependent personality disorder (0.78%; 95% CI, 0.37-1.32%). A low prevalence was significantly associated with expert-rated assessment (versus self-rated) and reporting of descriptive statistics for antisocial personality disorder. Conclusions Epidemiological studies on personality disorders in community samples are rare, whereas prevalence rates are fairly high and vary substantially depending on samples and methods. Future studies investigating the epidemiology of personality disorders based on the DSM-5 and ICD-11 and models of personality functioning and traits are needed, and efficient treatment should be a priority for healthcare systems to reduce disease burden.Declaration of interestNone.

Personality Traits of Entrepreneurs: A Review of Recent Literature

Abstract: We review the extensive literature since 2000 on the personality traits of entrepreneurs. We first consider baseline personality traits like the Big-5 model, self-efficacy and innovativeness, locus of control, and the need for achievement. We then consider risk attitudes and goals and aspirations of entrepreneurs. Within each area, we separate studies by the type of entrepreneurial behavior considered: entry into entrepreneurship, performance outcomes, and exit from entrepreneurship. This literature shows common results and many points of disagreement, reflective of the heterogeneous nature of entrepreneurship. We label studies by the type of entrepreneurial population studied (e.g., Main Street vs. those backed by venture capital) to identify interesting and irreducible parts of this heterogeneity, while also identifying places where we anticipate future large-scale research and the growing depth of the field are likely to clarify matters. There are many areas, like how firm performance connects to entrepreneurial personality, that are woefully understudied and ripe for major advances if the appropriate cross-disciplinary ingredients are assembled.

Resting-state functional connectivity predicts neuroticism and extraversion in novel individuals.

Abstract: The personality dimensions of neuroticism and extraversion are strongly associated with emotional experience and affective disorders. Previous studies reported functional magnetic resonance imaging (fMRI) activity correlates of these traits, but no study has used brain-based measures to predict them. Here, using a fully cross-validated approach, we predict novel individuals' neuroticism and extraversion from functional connectivity (FC) data observed as they simply rested during fMRI scanning. We applied a data-driven technique, connectome-based predictive modeling (CPM), to resting-state FC data and neuroticism and extraversion scores (self-reported NEO Five Factor Inventory) from 114 participants of the Nathan Kline Institute Rockland sample. After dividing the whole brain into 268 nodes using a predefined functional atlas, we defined each individual's FC matrix as the set of correlations between the activity timecourses of every pair of nodes. CPM identified networks consisting of functional connections correlated with neuroticism and extraversion scores, and used strength in these networks to predict a left-out individual's scores. CPM predicted neuroticism and extraversion in novel individuals, demonstrating that patterns in resting-state FC reveal trait-level measures of personality. CPM also revealed predictive networks that exhibit some anatomical patterns consistent with past studies and potential new brain areas of interest in personality.

Vulnerable Narcissism Is (Mostly) a Disorder of Neuroticism

Abstract: Objective Increasing attention has been paid to the distinction between the dimensions of narcissistic grandiosity and vulnerability. We examine the degree to which basic traits underlie vulnerable narcissism, with a particular emphasis on the importance of Neuroticism and Agreeableness. Method Across four samples (undergraduate, online community, clinical-community), we conduct dominance analyses to partition the variance predicted in vulnerable narcissism by the Five-Factor Model personality domains, as well as compare the empirical profiles generated by vulnerable narcissism and Neuroticism. Results These analyses demonstrate that the lion's share of variance is explained by Neuroticism (65%) and Agreeableness (19%). Similarity analyses were also conducted in which the extent to which vulnerable narcissism and Neuroticism share similar empirical networks was tested using an array of criteria, including self-, informant, and thin slice ratings of personality; interview-based ratings of personality disorder and pathological traits; and self-ratings of adverse events and functional outcomes. The empirical correlates of vulnerable narcissism and Neuroticism were nearly identical (MrICC = .94). Partial analyses demonstrated that the variance in vulnerable narcissism not shared with Neuroticism is largely specific to disagreeableness-related traits such as distrustfulness and grandiosity. Conclusions These findings demonstrate the parsimony of using basic personality to study personality pathology and have implications for how vulnerable narcissism might be approached clinically.

Linking personality and cognition: a meta-analysis

Abstract: In the past decade, several conceptual papers have linked variation in animal personality to variation in cognition, and recent years have seen a flood of empirical studies testing this link. However, these results have not been synthesized in a quantitative way. Here, we systematically search the literature and conduct a phylogenetically controlled meta-analysis of empirical papers that have tested the relationship between animal personality (exploration, boldness, activity, aggression and sociability) and cognition (initial learning/reversal speed, number of correct choices/errors after standard training). We find evidence for a small but significant relationship between variation in personality and variation in learning across species in the absolute scale; however, the direction of this relationship is highly variable and when both positive and negative effect sizes are considered, the average effect size does not differ significantly from zero. Importantly, this variation among studies is not explained by differences in personality or learning measure, or taxonomic grouping. Further, these results do not support current hypotheses suggesting that that fast-explorers are fast-learners or that slow-explorers perform better on tests of reversal learning. Rather, we find evidence that bold animals are faster learners, but only when boldness is measured in response to a predator (or simulated predator) and not when boldness is measured by exposure to a novel object (or novel food). Further, although only a small sub-sample of papers reported results separately for males and females, sex explained a significant amount of variation in effect size. These results, therefore, suggest that, while personality and learning are indeed related across a range of species, the direction of this relationship is highly variable. Thus further empirical work is needed to determine whether there are important moderators of this relationship.This article is part of the theme issue 'Causes and consequences of individual differences in cognitive abilities'.

Resting-state functional brain connectivity best predicts the personality dimension of openness to experience.

Abstract: Personality neuroscience aims to find associations between brain measures and personality traits. Findings to date have been severely limited by a number of factors, including small sample size and omission of out-of-sample prediction. We capitalized on the recent availability of a large database, together with the emergence of specific criteria for best practices in neuroimaging studies of individual differences. We analyzed resting-state functional magnetic resonance imaging (fMRI) data from 884 young healthy adults in the Human Connectome Project database. We attempted to predict personality traits from the “Big Five,” as assessed with the Neuroticism/Extraversion/Openness Five-Factor Inventory test, using individual functional connectivity matrices. After regressing out potential confounds (such as age, sex, handedness, and fluid intelligence), we used a cross-validated framework, together with test-retest replication (across two sessions of resting-state fMRI for each subject), to quantify how well the neuroimaging data could predict each of the five personality factors. We tested three different (published) denoising strategies for the fMRI data, two intersubject alignment and brain parcellation schemes, and three different linear models for prediction. As measurement noise is known to moderate statistical relationships, we performed final prediction analyses using average connectivity across both imaging sessions (1 hr of data), with the analysis pipeline that yielded the highest predictability overall. Across all results (test/retest; three denoising strategies; two alignment schemes; three models), Openness to experience emerged as the only reliably predicted personality factor. Using the full hour of resting-state data and the best pipeline, we could predict Openness to experience (NEOFAC_O: r=.24, R^2=.024) almost as well as we could predict the score on a 24-item intelligence test (PMAT24_A_CR: r=.26, R^2=.044). Other factors (Extraversion, Neuroticism, Agreeableness, and Conscientiousness) yielded weaker predictions across results that were not statistically significant under permutation testing. We also derived two superordinate personality factors (“α” and “β”) from a principal components analysis of the Neuroticism/Extraversion/Openness Five-Factor Inventory factor scores, thereby reducing noise and enhancing the precision of these measures of personality. We could account for 5% of the variance in the β superordinate factor (r=.27, R^2=.050), which loads highly on Openness to experience. We conclude with a discussion of the potential for predicting personality from neuroimaging data and make specific recommendations for the field.

The perniciousness of perfectionism: A meta-analytic review of the perfectionism-suicide relationship.

Abstract: Objective Over 50 years of research implicates perfectionism in suicide. Yet the role of perfectionism in suicide needs clarification due to notable between-study inconsistencies in findings, underpowered studies, and uncertainty about whether perfectionism confers risk for suicide. We addressed this by meta-analyzing perfectionism's relationship with suicide ideation and attempts. We also tested whether self-oriented, other-oriented, and socially prescribed perfectionism predicted increased suicide ideation, beyond baseline ideation. Method Our literature search yielded 45 studies (N = 11,747) composed of undergraduates, medical students, community adults, and psychiatric patients. Results Meta-analysis using random effects models revealed perfectionistic concerns (socially prescribed perfectionism, concern over mistakes, doubts about actions, discrepancy, perfectionistic attitudes), perfectionistic strivings (self-oriented perfectionism, personal standards), parental criticism, and parental expectations displayed small-to-moderate positive associations with suicide ideation. Socially prescribed perfectionism also predicted longitudinal increases in suicide ideation. Additionally, perfectionistic concerns, parental criticism, and parental expectations displayed small, positive associations with suicide attempts. Conclusions Results lend credence to theoretical accounts suggesting self-generated and socially based pressures to be perfect are part of the premorbid personality of people prone to suicide ideation and attempts. Perfectionistic strivings' association with suicide ideation also draws into question the notion that such strivings are healthy, adaptive, or advisable.

Mind the mindset! The interaction of proactive personality, transformational leadership and growth mindset for engagement at work

Abstract: Purpose The purpose of this paper is to analyze whether and how employees’ proactive personality is related to work engagement. Drawing on job demands-resources theory, the study proposes that this relationship is moderated by a three-way interaction between proactive personality × transformational leadership × growth mindset. Design/methodology/approach The study is based on survey data from 259 employees of an internationally operating high-tech organization in the Netherlands. Findings In line with prior studies, support is found for positive significant relationships of proactive personality and transformational leadership with engagement. Additionally, transformational leadership is found to moderate the relationship between proactive personality and work engagement, but only when employees have a growth mindset. Originality/value The study advances the literature that investigates the proactive personality-engagement relationship. Specifically, this study is the first to examine a possible three-way interaction that may deepen the insights for how proactive personality, transformational leadership and growth mindset interact in their contribution to work engagement.

Effects of psilocybin therapy on personality structure

Abstract: Objective To explore whether psilocybin with psychological support modulates personality parameters in patients suffering from treatment-resistant depression (TRD). Method Twenty patients with moderate or severe, unipolar, TRD received oral psilocybin (10 and 25 mg, one week apart) in a supportive setting. Personality was assessed at baseline and at 3-month follow-up using the Revised NEO Personality Inventory (NEO-PI-R), the subjective psilocybin experience with Altered State of Consciousness (ASC) scale, and depressive symptoms with QIDS-SR16. Results Neuroticism scores significantly decreased while Extraversion increased following psilocybin therapy. These changes were in the direction of the normative NEO-PI-R data and were both predicted, in an exploratory analysis, by the degree of insightfulness experienced during the psilocybin session. Openness scores also significantly increased following psilocybin, whereas Conscientiousness showed trend-level increases, and Agreeableness did not change. Conclusion Our observation of changes in personality measures after psilocybin therapy was mostly consistent with reports of personality change in relation to conventional antidepressant treatment, although the pronounced increases in Extraversion and Openness might constitute an effect more specific to psychedelic therapy. This needs further exploration in future controlled studies, as do the brain mechanisms of postpsychedelic personality change.

Emotional Labor and Burnout: A Review of the Literature

Abstract: This literature review was conducted to investigate the association between emotional labor and burnout and to explore the role of personality in this relationship The results of this review indicate that emotional labor is a job stressor that leads to burnout Further examination of personality traits, such as self-efficacy and type A behavior pattern, is needed to understand the relationships between emotional labor and health outcomes, such as burnout, psychological distress, and depression The results also emphasized the importance of stress management programs to reduce the adverse outcomes of emotional labor, as well as coping repertories to strengthen the personal potential suitable to organizational goals Moreover, enhancing employees' capacities and competence and encouraging a positive personality through behavior modification are also necessary

Eye Movements During Everyday Behavior Predict Personality Traits.

Abstract: Besides allowing us to perceive our surroundings, eye movements are also a window into our mind and a rich source of information on who we are, how we feel, and what we do. Here we show that eye movements during an everyday task predict aspects of our personality. We tracked eye movements of 42 participants while they ran an errand on a university campus and subsequently assessed their personality traits using well-established questionnaires. Using a state-of-the-art machine learning method and a rich set of features encoding different eye movement characteristics, we were able to reliably predict four of the Big Five personality traits (neuroticism, extraversion, agreeableness, conscientiousness) as well as perceptual curiosity only from eye movements. Further analysis revealed new relations between previously neglected eye movement characteristics and personality. Our findings demonstrate a considerable influence of personality on everyday eye movement control, thereby complementing earlier studies in laboratory settings. Improving automatic recognition and interpretation of human social signals is an important endeavor, enabling innovative design of human–computer systems capable of sensing spontaneous natural user behavior to facilitate efficient interaction and personalization.

Sibling relationships across the life-span

Abstract: The relationship between siblings is the longest lasting that people have in their lives - it is longer than their relationships with their children, with their parents, or with their partners. It is a relationship that can be of great power that is watched and worried over by parents, and explored and illuminated by novelists. Sleeping problems, feeding, and toilet problems increased, and withdrawal, anxiety, and dependency all have been found to intensify. Parent-child relationships can differ markedly within the same family. And children from a remarkably early age monitor what happens between their parents and their siblings. The differences in personality, interests, well-being, and adjustment of siblings growing up in the same family are marked, and this presents those who study families with a challenge. By far the most common cause to which the teenagers attributed negative changes in their relations with their siblings involved friendships outside the family.

Models of Schizotypy: The Importance of Conceptual Clarity.

Abstract: The observation of psychosis-like traits that resemble symptoms of schizophrenia and bipolar disorder, both among healthy relatives of psychotic patients and among the general population, can be traced to the early 20th century.1,2 These traits have since been described within various models of illness and health (ie, normal/abnormal personality, abnormal psychotic continua), each giving rise to concepts such as "schizotypy," "psychoticism," and "psychosis-proneness" that are not necessarily interchangeable, although their subtle distinctions are often overlooked. Historically, there have been 3 major models of schizophrenia-/psychosis-proneness, one of which is referred to as "taxonic" or "quasi-dimensional,"3,4 and 2 models that can be regarded as "fully dimensional,"5,6 as distinguished by the relationship that is proposed to exist between psychosis-proneness and the risk of clinical schizophrenia or other psychotic disorder. In this review, we outline the key assumptions of each model and its implications for research of psychosis in relation to mental illness and health and for the alternative models. We integrate historical concept development with current findings from various fields of research (eg, personality, neurobiology, and behavioral genetics) and highlight the remaining questions each model poses in relation to understanding the development of psychotic illness and the distribution of psychotic-like traits in the general population.

Interpersonal Dynamics in Personality and Personality Disorders

Abstract: Clinical and basic personality psychologists interact less than they should, given their similar interests. In clinical personality psychology, available evidence supports a transition from the cur...

Habit formation and change.

Abstract: This review highlights emerging findings and new directions in research on habit formation and change. We first identify the cognitive, attentional mechanisms that contribute to habit formation. Then we show how habit is transforming the way researchers think about self-control, and how changing habits involves environmental pressures as much as intrapsychic forces. Finally, we describe big data and new technologies that offer novel methods to study habits outside the lab by capturing repeated actions in the natural environments in which they occur.

Personality Predictions Based on User Behavior on the Facebook Social Media Platform

Abstract: With the development of social networks, a large variety of approaches have been developed to define users’ personalities based on their social activities and language use habits. Particular approaches differ with regard to different machine learning algorithms, data sources, and feature sets. The goal of this paper is to investigate the predictability of the personality traits of Facebook users based on different features and measures of the Big 5 model. We examine the presence of structures of social networks and linguistic features relative to personality interactions using the myPersonality project data set. We analyze and compare four machine learning models and perform the correlation between each of the feature sets and personality traits. The results for the prediction accuracy show that even if tested under the same data set, the personality prediction system built on the XGBoost classifier outperforms the average baseline for all the feature sets, with a highest prediction accuracy of 74.2%. The best prediction performance was reached for the extraversion trait by using the individual social network analysis features set, which achieved a higher personality prediction accuracy of 78.6%.