Personality Data Research Articles

Multi-modal EEG NEO-FFI with Trained Attention Layer (MENTAL) for mental disorder prediction.

Early detection and accurate diagnosis of mental disorders is difficult due to the complexity of the diagnostic process, resulting in conditions being left undiagnosed or misdiagnosed. Previous studies have demonstrated that features of Electroencephalogram (EEG) data, such as Power Spectral Density (PSD), are useful biomarkers for indicating the onset of various mental disorders. Existing models using EEG data are typically trained to distinguish between healthy and afflicted individuals, and they are unable to distinguish between individuals with different disorders. We propose MENTAL (Multi-modal EEG NEO-FFI with Trained Attention Layer) to predict an individual's mental state using both EEG and Neo-Five Factor Inventory (NEO-FFI) personality data. We include an attention layer that captures the interactions between personality traits and PSD features, and emphasizes the important PSD features. MENTAL features a Recurrent Neural Network (RNN) to model the temporal nature of EEG data. We train our model with the Two Decades Brainclinics Research Archive for Insights in Neuroscience (TDBRAIN) dataset, which consists of 1274 healthy and psychiatric individuals including over 30 different diagnoses. MENTAL is able to achieve 93.3% accuracy when trained to classify between healthy individuals and those with ADHD. When trained to identify individuals with ADHD from among 33 disorder classes, MENTAL improves accuracy from 70.5 to 81.7%. MENTAL also demonstrates over 20% improvement in predictive accuracy when trained for MDD prediction. For the multi-class classification task of three classes, MENTAL improves accuracy by 4%, and for five classes, by nearly 8%. MENTAL is the first multi-modal model that utilizes both EEG and NEO-FFI data for the task of mental disorder prediction. We are one of the first groups to utilize TDBRAIN for automated disorder classification. MENTAL is accessible and cost-effective, as EEG is more affordable than other neuroimaging methods such as MRI, and the NEO-FFI is a self- reported survey. Our model can lead to acceptance and support for individuals living with mental health challenges and improve quality of life in our society.

Personality in Asian elephants (Elephas maximus): Temporal stability and methods of assessment

Personality is the essence of individuality in animals, affecting individual behaviours, perceptions and lived experiences. Being able to reliably assess personality in animals holds the key to understanding individual differences, and application of this knowledge is paramount in the provision of individual-level management of animals to optimise welfare. A key aspect of the definition of animal personality is ‘consistency over time’. Yet, despite the range of studies assessing elephant personality, there is a lack of consistency within methodologies and personality is usually assessed at a single point in time. Here, we examine personality data from adult members of the Asian elephant (Elephas maximus) herd at Chester Zoo at five separate time points, across a ten-year period (2013−2023). Data were analysed in terms of the instruments used to measure personality (differences in questions/items across assessments, presentation of the personality assessments, raters), and changes over time in elephant personality assessment scores. Select personality traits were consistent over multiple time points. Inter-rater reliability across personality adjectives is highest when keepers are involved in scale development, reinforcing the importance of collaboration between scientists and animal caregivers in building tools for evidence-based management decisions over the lifetime of animals.

The way we look at an image or a webpage can reveal personality traits

Personality is a central concept and a cross-domain explanatory factor in psychology to characterize and differentiate individuals. Surprisingly, among the many studies on oculomotor behavior, only a few have investigated how personality influences the exploration of a visual stimulus. Due to the limited number of existing studies, it is still uncertain if markers of personality in eye movements are always observable in eye movements across various exploration contexts. Here, introducing a novel concept of gaze-based signatures of personality, we used visual exploration metrics to detect personality signatures across various exploration contexts (visual search and free-viewing on images and webpages) in 91 participants. Personality data were collected as in the reference paper that validated the French version of the Big Five Inventory. Linear regression analyses demonstrated that while Extraversion and Openness to Experience did not correlate with any particular exploration metric, the other three traits–Conscientiousness, Agreeableness, and Neuroticism–correlated robustly with all exploration metrics in different visual exploration contexts. Our study provides evidence for the capture of the gaze-based signature of personality from very brief eye movement recordings.

Personality Measurement of Students Using Item Response Theory Models: Stability Responses from Nigerian Institutions

Item Response Theory (IRT) is utilised to detect bias in assessment tools and address issues such as faked or manipulated responses, enhancing the reliability and stability of conclusions in personality assessment. This article examines the item parameter estimates of a scale and the effectiveness of one-, two-, and three-parameter logistic models in analysing response stability in personality measurement from repeated administration. Three hundred undergraduate students at three tertiary institutions in Nigeria were sampled using a multi-stage sampling procedure. Data was collected using an adapted version of the Big Five Inventory (BFI) with a reliability coefficient of 0.85. The results showed that the item parameter estimates (mean threshold) are within the recommended benchmarks. A comparison of the three IRT models based on the Likelihood ratio (InL), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC) values revealed that the two-parameter logistic model best fit the personality data among undergraduates from repeated administration. It is recommended that, rather than relying solely on a statistical decision-making process, IRT fit and model comparison should be applied to gain insight into the functioning of items and tests.

Decoding career success: A personality-based analysis of data science Professional based on ANFIS modeling

Career selection is one of the most important decisions every person faces in their life. Finding the right career path can be a complicated task, particularly in choosing careers with similarly required proficiencies. One of the critical factors affecting a person's career success is their personality, and taking account of this factor is of paramount importance. This study uses the NEO-FFI questionnaire to find personality patterns of software engineering and data science experts based on the Big Five personality traits: Neuroticism, extraversion, openness to experience, agreeableness, and conscientiousness. Afterward, an ANFIS (Adaptive Network-Based Inference System) is conducted using the experts' personality data to match the participants of these fields with their corresponding choices. This study demonstrated that data scientists and software engineers score higher in conscientiousness and agreeableness, respectively. Also, data experts have higher scores in all traits overall. In the end, the ANFIS is tested with another similar dataset and the prediction accuracy of the model is measured.

Smart device interest, perceived usefulness, and preferences in rural Alabama seniors

Limited data exist on the preferences for smart home devices in rural Americans. We examined the interest, usefulness, and pleasantness of various smart home interfaces and determined whether they differed by ethnicity, rurality, age, and gender. A total of 118 older adults living in the rural Deep South completed a survey instrument that assessed interest in various smart home applications and was queried about the perceived usefulness and perceived ease of use of screen, voice, and robot interfaces in 7 distinct scenarios. Personality data was collected via the Big Five Inventory. Technology readiness was measured using a technological readiness instrument. Participants were primarily female (81%), rural (76%), African American (69%), and aged 65-74 years old (51%). Participants were recruited from a total of 5 cities in West Alabama within the Black Belt. Data was collected before COVID-19 (July 2018 through July 2019).

Enhancing image sentiment analysis: A user-centered approach through user emotions and visual features

To bridge the gap between user understanding of emotions and image representation, this paper amalgamated a variety of user features to formulate an image sentiment analysis model. Through controlled user experiments, participants were engaged in discerning sentiment from abstract painting images. User visual features were derived from eye-tracking data collected during the experiment. This paper employed the FFM (Five Factor Model) and POMS (Profile of Mood States) methodologies to gather personality and mood data, which were utilized as user emotional features. Additionally, fundamental components of abstract painting were captured as basic image features. By synergizing features across these three dimensions, the study introduced the "User-Feature-Enhanced-Image-Sentiment-Model" (UFE-ISM) for image sentiment recognition. The research findings underscore the model's superior performance in sentiment classification on abstract painting. Notably, both basic image features and user visual features exhibited significant contributions to the image sentiment classification model.

Decoupling Shrinkage and Selection in Gaussian Linear Factor Analysis

Factor analysis is a popular method for modeling dependence in multivariate data. However, determining the number of factors and obtaining a sparse orientation of the loadings are still major challenges. In this paper, we propose a decision-theoretic approach that brings to light the relationship between model fit, factor dimension, and sparse loadings. This relation is done through a summary of the information contained in the multivariate posterior. A two-step strategy is used in our method. First, given the posterior samples from the Bayesian factor analysis model, a series of point estimates with a decreasing number of factors and different levels of sparsity are recovered by minimizing an expected penalized loss function. Second, the degradation in model fit between the posterior of the full model and the recovered estimates is displayed in a summary. In this step, a criterion is proposed for selecting the factor model with the best trade-off between fit, sparseness, and factor dimension. The findings are illustrated through a simulation study and an application to personality data. We used different prior choices to show the flexibility of the proposed method.

Improving Sensor-Free Affect Detection by Considering Students' Personality Traits

Emotion detection through sensors is intrusive and expensive, making it impractical for many educational settings. As an alternative, sensor-free affect detection, which relies solely on interaction log data for machine learning models, has been explored. However, sensor-free emotion detectors have not significantly improved performance when trained solely on students' interactions with their environment. In this study, we aimed to improve the performance of these models by incorporating students' personalities as additional information during model development. Personality is known to influence emotions, and the duration of negative emotions may depend on students' personalities. Therefore, we hypothesized that including personality data, based on the Big Five theory, in training machine learning models for sensor-free detection would enhance their performance. We collected data and videos from students using a step-based math ITS and used human coders to annotate emotions as target labels for our dataset. To test our hypothesis, we compared the performance of affect detectors trained with personality data to those trained solely on interaction data (baseline model) to detect confusion, engagement, frustration, and boredom. We found that incorporating personality data provided only a modest improvement in engagement detection. Our detector detected student engagement with a Cohen's Kappa of.633 and an AUC of.846 compared to Kappa = .630 and AUC = .846 for the same detector without personality data. Despite studies indicating that personality affects learning, this modest improvement leads us to conclude that adding personality information to sensor-free affect detectors is not cost-justifiable.

Midfrontal theta reactivity to conflict and error are linked to externalizing and internalizing respectively.

Dimensional psychopathology scores measure symptom severity; cutting across disorder categories. Their clinical utility is high given comorbidity, but their neural basis is unclear. We used scalp electroencephalography (EEG) to concurrently assess neural activity across internalizing and externalizing traits. "Theta rhythm" (4-7Hz) spectral power at the frontal midline site Fz in specific goal conflict and action error phases within a trial of a Stop-Signal Task was extracted using process-specific contrasts. A final sample of 146 community participants (63 males, 83 females; mean age = 36; SD = 9; range = 18 - 56), oversampled for externalizing disorder (49% diagnosed with a DSM-5 externalizing disorder), also supplied psychopathology and personality data. We used the Minnesota Multiphasic Personality Inventory-3 (MMPI-3) to measure symptoms and traits of psychopathology. An MMPI-3 measure of the higher-order internalizing psychopathology spectrum was positively correlated with action error theta. An MMPI-3 measure of the higher-order spectrum of externalizing psychopathology was negatively correlated with goal-conflict theta. We showed that goal-conflict and error theta activity are higher-order processes that index psychopathology severity. The associations extend into the nominally healthy range, and so reflect theta-related factors that apply to the general population as well as patients with sub-threshold diagnoses.

Supplemental Material for Leveraging Normative Personality Data and Machine Learning to Examine the Brain Structure Correlates of Obsessive-Compulsive Personality Disorder Traits

Supplemental Material for Leveraging Normative Personality Data and Machine Learning to Examine the Brain Structure Correlates of Obsessive-Compulsive Personality Disorder Traits

Personality and rock-climbing performance progression: a 12-months follow-up

ABSTRACT Although multiple investigations analysed the impact of personality on sport-related practice and outcomes, its contribution to explaining performance in high-risk or extreme sports was not thoroughly investigated. The present investigation pursues three different objectives: (1) identifying whether Five-Factor Model (FFM) personality traits are stable predictors of rock-climbing performance; (2) analysing the extent to which grit consistently explains various facets of rock-climbing performance, over and beyond the FFM traits; and (3) examining the extent to which grit is a consistent predictor of long-term rock-climbing progression. Data on rock-climbing performance was collected with a lag of 12 months after the collection of personality data. Participants (N = 113; 43 females and 70 males) with ages between 17 and 67 (M = 34.19, SD = 10.53) completed a comprehensive measure of rock-climbing performance (times 1 and 2). At time 1, personality was measured with the Big Five Inventory FFM –2 Short Form, and the 12-item Grit Scale, respectively. The International Rock Climbing Research Association (IRCRA) scale was used to measure various facets of rock-climbing performance at both times 1 and 2. Our previous findings were partly replicated, both openness to experience and grit emerging as consistent predictors of rock-climbing performance. However, no personality trait showed a significant association with climbing performance progression.

Reputational self-awareness: an innovative career development tool

PurposeThe authors introduce a new construct, reputational self-awareness (RSA). RSA represents the congruence between how individuals think they are viewed by others (i.e. metaperceptions) versus how they are actually viewed (i.e. other ratings). The authors sought to demonstrate that RSA is a superior predictor of performance indices.Design/methodology/approachPersonality self-ratings from 381 business students and their ratings by 966 others were collected via online surveys. Other raters rated self-raters' personalities as well as their task performance, organizational citizenship behaviors (OCBs) and counterproductive work behaviors (CWBs).FindingsResults indicate that RSA predicts variance in performance above and beyond self-report ratings, and performance is highest when metaperceptions and other ratings of performance are aligned. These results support the use of a multi-perspective approach to personality assessment as a useful tool for coaching and career development.Research limitations/implicationsThe authors' results support the use of a multi-perspective approach to personality assessment as a useful tool for coaching and career development. A cross-sectional design was used in which personality and performance data were gathered from respondents, and the P 720 is a relatively new personality instrument.Practical implicationsRSA is a valuable tool for employee development, coaching and counseling because, as extant research and the authors' findings demonstrate, awareness of how others view and judge one, one's reputation is essential information to guide work behaviors and career success. Therefore, a key career-development goal for trainers and counselors should be to use a multi-perspective approach to maximize clients' RSA.Social implicationsUse of other ratings as opposed to traditional self-rating of personality provides superior prediction of behavior and is more useful for career development.Originality/valueThis is the first study to demonstrate utility of RSA, i.e. that individuals who more accurately assess their personality are rated as performing better by others. The authors' results offer new insights for personality research and career development and support the use of personality assessment from multiple perspectives, thus enabling the exploration of potentially insightful research questions that cannot be examined by assessing personality from a single perspective.

Assessing the measurement invariance of the Personality Inventory for DSM-5 across Black and White americans.

The Personality Inventory for DSM-5 (PID-5) is the primary tool for assessing maladaptive personality traits within the DSM-5 alternative model for personality disorders. Evidence has begun to accumulate on the replicability and measurement invariance of its five-domain factor structure across countries, clinical and community populations, and sex, but its equivalency across racial groups within a given country is largely unstudied. Attempting to replicate the evidence of noninvariance demonstrated by Bagby et al. (2022), we examined the factor structure of the PID-5 across White Americans (n = 612) and Black Americans (n = 613) within the United States. The five-domain structure emerged across both samples with reasonably congruent factor loadings. Therefore, we tested for measurement invariance using the 13-step framework advocated by Marsh et al. (2009) for personality data. We found support for the PID-5's comparability across racial groups, offering some preliminary backing for its use with Black Americans, though additional evidence is needed to clarify the conflicting results and further validate the instrument. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

A BOM model transformation method for hierarchical production planning management process of complex products

The Bill of Materials (BOM) is the core data in the hierarchical production planning management of complex products. In the current competitive market environment and increasing demand for individual customization, complex product manufacturing enterprises are exploring methods that fully use common data and efficiently organize the BOM according to order requirements. However, due to the lack of a unified data modeling method for hierarchical production planning, BOM data at different levels cannot be interpenetrated. Moreover, the single-tree BOM lacks flexibility in describing the dynamic change process of data in different planning management stages (i.e., order receiving, variant design, and manufacturing execution).This paper proposes a BOM model transformation method for hierarchical production planning management to efficiently and accurately organize the data in different planning management stages of complex products. First, the Hierarchical Bill of Materials (HBOMs) model is constructed to integrate BOM data of different levels of planning. Subsequently, to describe the data requirements in HBOMs for different plan management stages, a composite HBOMs containing three types was designed. The Generic-HBOM (G-HBOM) aims to manage the common data of the same product type. The Instance-HBOM before-manufacturing (I-HBOM (BM)) and Instance-HBOM after-manufacturing (I-HBOM (AM)) manage the personality data of single product variant design and manufacturing execution stages, respectively. The transformation process models of HBOMs are constructed by analyzing the transformation logic between three types of HBOMs. To support the transformation process of HBOMs, five transformation operators are summarized, and the node traversal algorithm logic is proposed. Finally, a steam turbine generator product is taken as an example to verify the feasibility and effectiveness of this method. This method can effectively improve the efficiency of BOM construction and reduce data redundancy while helping to ensure the integrity, consistency, and accuracy of BOM data.

Corruption revisited: the influence of national personality, culture, and wealth

Corruption is often seen as one of the root causes of pressing national and global challenges. The persisting stark national differences in corruption levels and their potential causes have thus attracted growing interest from international business scholars. The objective of this study was to re-examine key factors that predict levels of national corruption. Drawing on comprehensive personality data from over 5 million respondents across 87 nations, and numerous dimensions of national culture, the study examines the relative importance of national personality versus national culture and wealth as predictors of national corruption. Regression analysis found that collectivism (particularly societal practices pertaining to collectivism) and wealth were robust predictors of corruption. In contrast, there was no consistent support for the effects of the Big Five personality traits aggregated to the national level, above and beyond the effects of national culture and wealth. These findings highlight and specify the important role played by national culture, and call into question previous research on national personality and corruption. More broadly, our study further highlights the need to exert caution when examining the influence of national-level personality, and the need for cross-national personality researchers to improve the validity, interpretability, and replicability of their work.

Top-Down Influence? Predicting CEO Personality and Risk Impact from Speech Transcripts

How much does a CEO’s personality impact the performanceof their company? Management theory posits a great influence, but it is difficult to show empirically—there is a lack of publicly available self-reported personality data of top managers. Instead, we propose a text-based personality regressor based on crowd-sourced Myers–Briggs Type Indicator (MBTI) assessments. The ratings have a high internal and external validity and can be predicted with moderate to strong correlations for three out of four dimensions. Providing evidence for the upper echelons theory, we demonstrate that the predicted CEO personalities have explanatory power of financial risk.

Regional personality differences predict variation in early COVID-19 infections and mobility patterns indicative of social distancing.

The early stages of the COVID-19 pandemic revealed stark regional variation in the spread of the virus. While previous research has highlighted the impact of regional differences in sociodemographic and economic factors, we argue that regional differences in social and compliance behaviors-the very behaviors through which the virus is transmitted-are critical drivers of the spread of COVID-19, particularly in the early stages of the pandemic. Combining self-reported personality data that capture individual differences in these behaviors (3.5 million people) with COVID-19 prevalence and mortality rates as well as behavioral mobility observations (29 million people) in the United States and Germany, we show that regional personality differences can help explain the early transmission of COVID-19; this is true even after controlling for a wide array of important sociodemographic, economic, and pandemic-related factors. We use specification curve analyses to test the effects of regional personality in a robust and unbiased way. The results indicate that in the early stages of COVID-19, Openness to experience acted as a risk factor, while Neuroticism acted as a protective factor. The findings also highlight the complexity of the pandemic by showing that the effects of regional personality can differ (a) across countries (Extraversion), (b) over time (Openness), and (c) from those previously observed at the individual level (Agreeableness and Conscientiousness). Taken together, our findings support the importance of regional personality differences in the early spread of COVID-19, but they also caution against oversimplified answers to phenomena as complex as a global pandemic. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Persuasive communication systems: a machine learning approach to predict the effect of linguistic styles and persuasion techniques

PurposePrediction is a critical task in targeted online advertising, where predictions better than random guessing can translate to real economic return. This study aims to use machine learning (ML) methods to identify individuals who respond well to certain linguistic styles/persuasion techniques based on Aristotle’s means of persuasion, rhetorical devices, cognitive theories and Cialdini’s principles, given their psychometric profile.Design/methodology/approachA total of 1,022 individuals took part in the survey; participants were asked to fill out the ten item personality measure questionnaire to capture personality traits and the dysfunctional attitude scale (DAS) to measure dysfunctional beliefs and cognitive vulnerabilities. ML classification models using participant profiling information as input were developed to predict the extent to which an individual was influenced by statements that contained different linguistic styles/persuasion techniques. Several ML algorithms were used including support vector machine, LightGBM and Auto-Sklearn to predict the effect of each technique given each individual’s profile (personality, belief system and demographic data).FindingsThe findings highlight the importance of incorporating emotion-based variables as model input in predicting the influence of textual statements with embedded persuasion techniques. Across all investigated models, the influence effect could be predicted with an accuracy ranging 53%–70%, indicating the importance of testing multiple ML algorithms in the development of a persuasive communication (PC) system. The classification ability of models was highest when predicting the response to statements using rhetorical devices and flattery persuasion techniques. Contrastingly, techniques such as authority or social proof were less predictable. Adding DAS scale features improved model performance, suggesting they may be important in modelling persuasion.Research limitations/implicationsIn this study, the survey was limited to English-speaking countries and largely Western society values. More work is needed to ascertain the efficacy of models for other populations, cultures and languages. Most PC efforts are targeted at groups such as users, clients, shoppers and voters with this study in the communication context of education – further research is required to explore the capability of predictive ML models in other contexts. Finally, long self-reported psychological questionnaires may not be suitable for real-world deployment and could be subject to bias, thus a simpler method needs to be devised to gather user profile data such as using a subset of the most predictive features.Practical implicationsThe findings of this study indicate that leveraging richer profiling data in conjunction with ML approaches may assist in the development of enhanced persuasive systems. There are many applications such as online apps, digital advertising, recommendation systems, chatbots and e-commerce platforms which can benefit from integrating persuasion communication systems that tailor messaging to the individual – potentially translating into higher economic returns.Originality/valueThis study integrates sets of features that have heretofore not been used together in developing ML-based predictive models of PC. DAS scale data, which relate to dysfunctional beliefs and cognitive vulnerabilities, were assessed for their importance in identifying effective persuasion techniques. Additionally, the work compares a range of persuasion techniques that thus far have only been studied separately. This study also demonstrates the application of various ML methods in predicting the influence of linguistic styles/persuasion techniques within textual statements and show that a robust methodology comparing a range of ML algorithms is important in the discovery of a performant model.

Psychological distress and decision-making factors for prophylactic bilateral mastectomy in cancer-unaffected BRCA1/2 pathogenic variant carriers.

Women carrying a BRCA1/2 pathogenic variant have an increased risk for breast cancer and may opt for risk-reducing bilateral mastectomy. In this study, we examine which demographic, psychosocial, and personality factors are associated with their decision to undergo risk-reducing bilateral mastectomy. Cancer-unaffected women with a pathogenic variant in BRCA1 or BRCA2 were recruited before receiving their genetic test result and completed follow-up including decision to undergo mastectomy over 6-8months after genetic test result disclosure. Anxiety, depression, breast cancer worry, personality and sociodemographic data were assessed. A total of 125 cancer-unaffected women were included in the analysis. Participants were found to have higher anxiety levels than the general female population regardless of mastectomy decision. Breast cancer worry was higher among women who opted for risk-reducing mastectomy and did not decrease over time. By contrast, women who did not opt for surgery experienced decreasing levels of breast cancer worry. Regression analysis found that women with a pathogenic variant in BRCA1, younger women and women with higher breast cancer worry were more likely to opt for surgery. Our study provides valuable insights into the factors that influence women with a BRCA1/2 pathogenic variant to undergo risk-reducing mastectomy. These findings may be helpful in understanding individual differences in decision-making concerning preventive options and show the need to address negative anticipatory feelings associated with carrying a pathogenic variant in a high breast cancer risk gene in clinical care.