Epistemic values and the Big Five: Personality characteristics of those who ascribe personal and moral value to epistemic rationality.
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SUBMITTER: Stahl T
PROVIDER: S-EPMC8491882 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Introduction Openness, conscientiousness, extroversion, agreeableness and neuroticism are known as the Big Five personality traits (BFPT). They are theoretical building blocks of the personality and comprise wide and interconnected spectra. Artificial intelligence (AI) could help to grasp their complexity. Objectives To investigate whether AI could predict the BFPT from themselves. Methods Data from 2,697 questionnaires were analysed using an AI. The short form of the International Personality Item Pool was used to assess the BFPT. Four of the BFPT scores were employed to predict the fifth one and the procedure was repeated for all of them alternatively. The AI was conservatively tuned to maximize the one-way random intraclass correlation coefficient (ICC) between predicted and real values. Their Pearson’s r was calculated too. The free and open source programming language R was used for all the analyses. Dataset source: Hansson, Isabelle; Berg, Anne Ingeborg; Thorvaldsson, Valgeir (2018), “Can personality predict longitudinal study attrition? Evidence from a population-based sample of older adults”, Mendeley Data, V1, doi: 10.17632/g3jx8zt2t9.1 Results Openness, conscientiousness, extroversion, agreeableness and neuroticism predictions obtained ICC of 0.219, 0.146, 0.306, 0.354, 0.121 and Pearson’s r of 0.254, 0.149, 0.393, 0.446, 0.122 respectively. The results for extroversion and agreeableness were indicative of fair performance. Conclusions AI might be useful to predict personality traits, mainly extroversion and agreeableness. This could be utile in many situations, such as dealing with missing data or deciding whether to formally test someone. Finally, the AI used in this study is freely available, allowing anyone to experiment.
| S-EPMC9475767 | biostudies-literature
Project description:Backgrounds Accelerated cellular ageing, which can be examined by telomere length (TL), may be an overarching mechanism underlying the association between personality and adverse health outcomes. This 6-year longitudinal study examined the relation between personality and leukocyte telomere length (LTL) across time among adults with a wide age-range.MethodsData from the Netherlands Study of Depression and Anxiety were used and included patients with a depression and/or anxiety disorder and healthy controls. Overall, 2936 persons (18-65 years, 66% female) had data on LTL at baseline and 1883 persons had LTL at 6-year follow-up. The Big Five personality traits (neuroticism, extraversion, openness to experience, agreeableness, and conscientiousness) and Type D personality were assessed.ResultsNeuroticism was negatively (B = -2.11, p = 0.03) and agreeableness was positively (B = 3.84, p = 0.03) related to LTL measured across two time points, which became just non-significant after adjusting for somatic health, lifestyle factors, and recent life stress (B = -1.99, p = 0.06; and B = 3.01, p = 0.10). Type D personality was negatively (B = -50.16, p < 0.01) related to LTL across two time points, which still remained statistically significant after full adjustment (B = -47.37, p = 0.01). Associations did not differ by age, gender, and current psychiatric status.ConclusionsThe Big Five traits high neuroticism and low agreeableness, and Type D personality were associated with shorter LTL measured across a 6-year period. Associations with the Big Five traits became non-significant after controlling for somatic health, lifestyle factors, and recent life stress, yet similar trends were observed. Type D personality remained independently associated with shorter LTL after full adjustment.
| S-EPMC5851042 | biostudies-literature
Project description:We used a new theory of the biological basis of the Big Five personality traits to generate hypotheses about the association of each trait with the volume of different brain regions. Controlling for age, sex, and whole-brain volume, results from structural magnetic resonance imaging of 116 healthy adults supported our hypotheses for four of the five traits: Extraversion, Neuroticism, Agreeableness, and Conscientiousness. Extraversion covaried with volume of medial orbitofrontal cortex, a brain region involved in processing reward information. Neuroticism covaried with volume of brain regions associated with threat, punishment, and negative affect. Agreeableness covaried with volume in regions that process information about the intentions and mental states of other individuals. Conscientiousness covaried with volume in lateral prefrontal cortex, a region involved in planning and the voluntary control of behavior. These findings support our biologically based, explanatory model of the Big Five and demonstrate the potential of personality neuroscience (i.e., the systematic study of individual differences in personality using neuroscience methods) as a discipline.
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Project description:A model describing the wide variety of human behaviours called personality, is becoming increasingly popular among researchers due to the widespread availability of personal big data generated from the use of prevalent digital devices, e.g., smartphones and wearables. Such an approach can be used to model an individual and even digitally clone a person, e.g., a Cyber-I (cyber individual). This work is aimed at establishing a unique and comprehensive description for an individual to mesh with various personalized services and applications. An extensive research literature on or related to psychological modelling exists, i.e., into automatic personality computing. However, the integrity and accuracy of the results from current automatic personality computing is insufficient for the elaborate modeling in Cyber-I due to an insufficient number of data sources. To reach a comprehensive psychological description of a person, it is critical to bring in heterogeneous data sources that could provide plenty of personal data, i.e., the physiological data, and the Internet data. In addition, instead of calculating personality traits from personal data directly, an approach to a personality model derived from the theories of Carl Gustav Jung is used to measure a human subject's persona. Therefore, this research is focused on designing an archetype-based modeling of persona covering an individual's facets in different situations to approach a comprehensive personality model. Using personal big data to measure a specific persona in a certain scenario, our research is designed to ensure the accuracy and integrity of the generated personality model.
| S-EPMC5876706 | biostudies-literature
Project description:Can personality traits be measured and interpreted reliably across the world? While the use of Big Five personality measures is increasingly common across social sciences, their validity outside of western, educated, industrialized, rich, and democratic (WEIRD) populations is unclear. Adopting a comprehensive psychometric approach to analyze 29 face-to-face surveys from 94,751 respondents in 23 low- and middle-income countries, we show that commonly used personality questions generally fail to measure the intended personality traits and show low validity. These findings contrast with the much higher validity of these measures attained in internet surveys of 198,356 self-selected respondents from the same countries. We discuss how systematic response patterns, enumerator interactions, and low education levels can collectively distort personality measures when assessed in large-scale surveys. Our results highlight the risk of misinterpreting Big Five survey data and provide a warning against naïve interpretations of personality traits without evidence of their validity.
| S-EPMC6620089 | biostudies-other
Project description:There is ample evidence that morphological and social cues in a human face provide signals of human personality and behaviour. Previous studies have discovered associations between the features of artificial composite facial images and attributions of personality traits by human experts. We present new findings demonstrating the statistically significant prediction of a wider set of personality features (all the Big Five personality traits) for both men and women using real-life static facial images. Volunteer participants (N = 12,447) provided their face photographs (31,367 images) and completed a self-report measure of the Big Five traits. We trained a cascade of artificial neural networks (ANNs) on a large labelled dataset to predict self-reported Big Five scores. The highest correlations between observed and predicted personality scores were found for conscientiousness (0.360 for men and 0.335 for women) and the mean effect size was 0.243, exceeding the results obtained in prior studies using 'selfies'. The findings strongly support the possibility of predicting multidimensional personality profiles from static facial images using ANNs trained on large labelled datasets. Future research could investigate the relative contribution of morphological features of the face and other characteristics of facial images to predicting personality.
| S-EPMC7244587 | biostudies-literature
Project description:The Diagnostic and Statistical Manual of Mental Disorders (DSM; 5th ed.) reassignment of gambling disorder as an addictive disorder alongside the substance-related addictive disorders encourages research into their shared etiologies. The aims of this study were to examine: (a) the associations of Big Five personality dimensions with alcohol, nicotine, cannabis, and gambling disorders, (b) the comorbidity between these disorders, (c) the extent to which common personality underpinnings explain comorbidity, (d) whether results differed for men and women, and (e) the magnitude of personality differences corresponding to the 4 disorders. Participants were 3,785 twins and siblings (1,365 men, 2,420 women; Mage = 32 years, range = 21-46 years) from the Australian Twin Registry who completed psychiatric interviews and Big Five personality inventories. The personality profile of high neuroticism, low agreeableness, and low conscientiousness was associated with all 4 addictive disorders. All but 1 of the pairwise associations between the disorders were significant. After accounting for Big Five traits, the associations were attenuated to varying degrees but remained significant. The results were generally similar for men and women. The results suggest that the Big Five traits of neuroticism, agreeableness, and conscientiousness are associated with the general propensity to develop an addictive disorder and may in part explain their co-occurrence; however, they may be more broadly associated with the propensity for any psychiatric disorder. The effect sizes of the personality associations suggest that the diagnosis of gambling disorder as operationalized by the DSM may be more severe than the other addictive disorders. Calibration of the diagnosis of gambling disorder to the other addictive disorders may be warranted. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
| S-EPMC7269105 | biostudies-literature
Project description:Based on Eysenck's biopsychological trait theory, brain arousal has long been considered to explain individual differences in human personality. Yet, results from empirical studies remained inconclusive. However, most published results have been derived from small samples and, despite inherent limitations, EEG alpha power has usually served as an exclusive indicator for brain arousal. To overcome these problems, we here selected N = 468 individuals of the LIFE-Adult cohort and investigated the associations between the Big Five personality traits and brain arousal by using the validated EEG- and EOG-based analysis tool VIGALL. Our analyses revealed that participants who reported higher levels of extraversion and openness to experience, respectively, exhibited lower levels of brain arousal in the resting state. Bayesian and frequentist analysis results were especially convincing for openness to experience. Among the lower-order personality traits, we obtained the strongest evidence for neuroticism facet 'impulsivity' and reduced brain arousal. In line with this, both impulsivity and openness have previously been conceptualized as aspects of extraversion. We regard our findings as well in line with the postulations of Eysenck and consistent with the recently proposed 'arousal regulation model'. Our results also agree with meta-analytically derived effect sizes in the field of individual differences research, highlighting the need for large (collaborative) studies.
| S-EPMC8533901 | biostudies-literature
Project description:Selection methods are commonly used in talent acquisition to predict future job performance and to find the best candidates, but questionnaire-based assessments can be lengthy and lead to candidate fatigue and poor engagement, affecting completion rates and producing poor data. Gamification can mitigate some of these issues through greater engagement and shorter testing times. One avenue of gamification is image-based tests. Although such assessments are starting to gain traction in personnel selection, few studies describing their validity and psychometric properties exist. The current study explores the potential of a five-minute, forced-choice, image-based assessment of the Big Five personality traits to be used in selection. Study 1 describes the creation of the image pairs and the selection of the 150 best-performing items based on a sample of 300 respondents. Study 2 describes the creation of machine-learning-based scoring algorithms and tests of their convergent and discriminate validity and adverse impact based on a sample of 431 respondents. All models showed good levels of convergent validity with the IPIP-NEO-120 (openness r = 0.71, conscientiousness r = 0.70, extraversion r = 0.78, agreeableness r = 0.60, and emotional stability r = 0.70) and were largely free from potential adverse impact. The implications for recruitment policy and practice and the need for further validation are discussed.
| S-EPMC8883940 | biostudies-literature
Project description:BACKGROUND:Empathy promotes positive physician-patient communication and is associated with improved patient satisfaction, treatment adherence and clinical outcomes. It has been suggested that personality traits should be taken into consideration in programs designed to enhance empathy in medical education due to the association found between personality and empathy among medical students. However, the associations between empathy and big five personality traits in medical education are still underrepresented in the existing literature and relevant studies have not been conducted among medical students in China, where tensions in the physician-patient relationship have been reported as outstanding problems in the context of China's current medical reform. Thus, the main objective of this study was to examine the associations between empathy and big five personality traits among Chinese medical students. METHODS:A cross-sectional study was conducted in a medical university in Northeast China in June 2016. Self-reported questionnaires including the Interpersonal Reactivity Index (IRI) and Big Five Inventory (BFI) and demographic characteristics were distributed. A total of 530 clinical medical students became our final subjects. Hierarchical regression analysis was performed to explore the effects of big five personality traits on empathy. RESULTS:Results of this study showed that big five personality traits accounted for 19.4%, 18.1%, 30.2% of the variance in three dimensions of empathy, namely, perspective taking, empathic concern and personal distress, respectively. Specifically, agreeableness had a strong positive association with empathic concern (? = 0.477, P<0.01), and a moderate association with perspective taking (? = 0.349, P<0.01). Neuroticism was strongly associated with personal distress (? = 0.526, P<0.01) and modestly associated with perspective taking (? = 0.149, P<0.01). Openness to experience had modest associations with perspective taking (? = 0.150, P<0.01) and personal distress (? = -0.160, P<0.01). Conscientiousness had a modest association with perspective taking (? = 0.173, P<0.01). CONCLUSION:This study revealed that big five personality traits were important predictors of self-reported measures of both cognitive and affective empathy among Chinese medical students. Therefore, individualized intervention strategies based on personality traits could be integrated into programs to enhance empathy in medical education.
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