ATP and spontaneous calcium oscillations control neural stem cell fate determination in Huntington's disease: a novel approach for cell clock research.

Calcium, the most versatile second messenger, regulates essential biology including crucial cellular events in embryogenesis. We investigated impacts of calcium channels and purinoceptors on neuronal differentiation of normal mouse embryonic stem cells (ESCs), with outcomes being compared to those of in vitro models of Huntington's disease (HD). Intracellular calcium oscillations tracked via real-time fluorescence and luminescence microscopy revealed a significant correlation between calcium transient activity and rhythmic proneuronal transcription factor expression in ESCs stably expressing ASCL-1 or neurogenin-2 promoters fused to luciferase reporter genes. We uncovered that pharmacological manipulation of L-type voltage-gated calcium channels (VGCCs) and purinoceptors induced a two-step process of neuronal differentiation. Specifically, L-type calcium channel-mediated augmentation of spike-like calcium oscillations first promoted stable expression of ASCL-1 in differentiating ESCs, which following P2Y2 purinoceptor activation matured into GABAergic neurons. By contrast, there was neither spike-like calcium oscillations nor responsive P2Y2 receptors in HD-modeling stem cells in vitro. The data shed new light on mechanisms underlying neurogenesis of inhibitory neurons. Moreover, our approach may be tailored to identify pathogenic triggers of other developmental neurological disorders for devising targeted therapies.

Regional personality assessment through social media language.

OBJECTIVE: We explore the personality of counties as assessed through linguistic patterns on social media. Such studies were previously limited by the cost and feasibility of large-scale surveys; however, language-based computational models applied to large social media datasets now allow for large-scale personality assessment. METHOD: We applied a language-based assessment of the five factor model of personality to 6,064,267 U.S. Twitter users. We aggregated the Twitter-based personality scores to 2,041 counties and compared to political, economic, social, and health outcomes measured through surveys and by government agencies. RESULTS: There was significant personality variation across counties. Openness to experience was higher on the coasts, conscientiousness was uniformly spread, extraversion was higher in southern states, agreeableness was higher in western states, and emotional stability was highest in the south. Across 13 outcomes, language-based personality estimates replicated patterns that have been observed in individual-level and geographic studies. This includes higher Republican vote share in less agreeable counties and increased life satisfaction in more conscientious counties. CONCLUSIONS: Results suggest that regions vary in their personality and that these differences can be studied through computational linguistic analysis of social media. Furthermore, these methods may be used to explore other psychological constructs across geographies.

Musical Preferences Predict Personality: Evidence From Active Listening and Facebook Likes.

Research over the past decade has shown that various personality traits are communicated through musical preferences. One limitation of that research is external validity, as most studies have assessed individual differences in musical preferences using self-reports of music-genre preferences. Are personality traits communicated through behavioral manifestations of musical preferences? We addressed this question in two large-scale online studies with demographically diverse populations. Study 1 ( N = 22,252) shows that reactions to unfamiliar musical excerpts predicted individual differences in personality-most notably, openness and extraversion-above and beyond demographic characteristics. Moreover, these personality traits were differentially associated with particular music-preference dimensions. The results from Study 2 ( N = 21,929) replicated and extended these findings by showing that an active measure of naturally occurring behavior, Facebook Likes for musical artists, also predicted individual differences in personality. In general, our findings establish the robustness and external validity of the links between musical preferences and personality.

Computer-based personality judgments are more accurate than those made by humans.

Judging others' personalities is an essential skill in successful social living, as personality is a key driver behind people's interactions, behaviors, and emotions. Although accurate personality judgments stem from social-cognitive skills, developments in machine learning show that computer models can also make valid judgments. This study compares the accuracy of human and computer-based personality judgments, using a sample of 86,220 volunteers who completed a 100-item personality questionnaire. We show that (i) computer predictions based on a generic digital footprint (Facebook Likes) are more accurate (r = 0.56) than those made by the participants' Facebook friends using a personality questionnaire (r = 0.49); (ii) computer models show higher interjudge agreement; and (iii) computer personality judgments have higher external validity when predicting life outcomes such as substance use, political attitudes, and physical health; for some outcomes, they even outperform the self-rated personality scores. Computers outpacing humans in personality judgment presents significant opportunities and challenges in the areas of psychological assessment, marketing, and privacy.

Facial Width-to-Height Ratio Does Not Predict Self-Reported Behavioral Tendencies.

A growing number of studies have linked facial width-to-height ratio (fWHR) with various antisocial or violent behavioral tendencies. However, those studies have predominantly been laboratory based and low powered. This work reexamined the links between fWHR and behavioral tendencies in a large sample of 137,163 participants. Behavioral tendencies were measured using 55 well-established psychometric scales, including self-report scales measuring intelligence, domains and facets of the five-factor model of personality, impulsiveness, sense of fairness, sensational interests, self-monitoring, impression management, and satisfaction with life. The findings revealed that fWHR is not substantially linked with any of these self-reported measures of behavioral tendencies, calling into question whether the links between fWHR and behavior generalize beyond the small samples and specific experimental settings that have been used in past fWHR research.

Birds of a Feather Do Flock Together.

Friends and spouses tend to be similar in a broad range of characteristics, such as age, educational level, race, religion, attitudes, and general intelligence. Surprisingly, little evidence has been found for similarity in personality-one of the most fundamental psychological constructs. We argue that the lack of evidence for personality similarity stems from the tendency of individuals to make personality judgments relative to a salient comparison group, rather than in absolute terms (i.e., the reference-group effect), when responding to the self-report and peer-report questionnaires commonly used in personality research. We employed two behavior-based personality measures to circumvent the reference-group effect. The results based on large samples provide evidence for personality similarity between romantic partners ( n = 1,101; rs = .20-.47) and between friends ( n = 46,483; rs = .12-.31). We discuss the practical and methodological implications of the findings.

Living in the Past, Present, and Future: Measuring Temporal Orientation With Language.

Temporal orientation refers to individual differences in the relative emphasis one places on the past, present, or future, and it is related to academic, financial, and health outcomes. We propose and evaluate a method for automatically measuring temporal orientation through language expressed on social media. Judges rated the temporal orientation of 4,302 social media messages. We trained a classifier based on these ratings, which could accurately predict the temporal orientation of new messages in a separate validation set (accuracy/mean sensitivity = .72; mean specificity = .77). We used the classifier to automatically classify 1.3 million messages written by 5,372 participants (50% female; ages 13-48). Finally, we tested whether individual differences in past, present, and future orientation differentially related to gender, age, Big Five personality, satisfaction with life, and depressive symptoms. Temporal orientations exhibit several expected correlations with age, gender, and Big Five personality. More future-oriented people were older, more likely to be female, more conscientious, less impulsive, less depressed, and more satisfied with life; present orientation showed the opposite pattern. Language-based assessments can complement and extend existing measures of temporal orientation, providing an alternative approach and additional insights into language and personality relationships.

Self-Monitoring and the Metatraits.

Prior attempts at locating self-monitoring within general taxonomies of personality traits have largely proved unsuccessful. However, past research has typically neglected (a) the bidimensionality of the Self-Monitoring Scale and (b) the hierarchical nature of personality. The objective of this study was to test hypotheses that the two self-monitoring factors are located at the level of the metatraits. Using data from two large multi-informant samples, one community (Sample 1: N = 552, Mage = 51.26, 61% female; NPeers = 1,551, Mage = 48.61, 37% female) and one online (Sample 2: N = 3,726, Mage = 24.89, 59% female; NPeers = 17,868, Mage = 26.23, 64% female), confirmatory factor analysis was used to test the hypotheses. Results confirmed hypotheses that acquisitive self-monitoring would have a strong positive relation to metatrait Plasticity, whereas protective self-monitoring would have a moderate negative relation to metatrait Stability. In both samples, constraining the correlation between acquisitive self-monitoring and Plasticity to unity did not alter model fit indices, indicating that the two putatively distinct constructs are identical. Findings have wide-ranging implications, including integration of the construct of self-monitoring into the mainstream of personality research, as the latter moves toward the development of broad explanatory theories.

Private traits and attributes are predictable from digital records of human behavior.

We show that easily accessible digital records of behavior, Facebook Likes, can be used to automatically and accurately predict a range of highly sensitive personal attributes including: sexual orientation, ethnicity, religious and political views, personality traits, intelligence, happiness, use of addictive substances, parental separation, age, and gender. The analysis presented is based on a dataset of over 58,000 volunteers who provided their Facebook Likes, detailed demographic profiles, and the results of several psychometric tests. The proposed model uses dimensionality reduction for preprocessing the Likes data, which are then entered into logistic/linear regression to predict individual psychodemographic profiles from Likes. The model correctly discriminates between homosexual and heterosexual men in 88% of cases, African Americans and Caucasian Americans in 95% of cases, and between Democrat and Republican in 85% of cases. For the personality trait "Openness," prediction accuracy is close to the test-retest accuracy of a standard personality test. We give examples of associations between attributes and Likes and discuss implications for online personalization and privacy.

Large language models know how the personality of public figures is perceived by the general public.

We show that people's perceptions of public figures' personalities can be accurately predicted from their names' location in GPT-3's semantic space. We collected Big Five personality perceptions of 226 public figures from 600 human raters. Cross-validated linear regression was used to predict human perceptions from public figures' name embeddings extracted from GPT-3. The models' accuracy ranged from r = .78 to .88 without controls and from r = .53 to .70 when controlling for public figures' likability and demographics, after correcting for attenuation. Prediction models showed high face validity as revealed by the personality-descriptive adjectives occupying their extremes. Our findings reveal that GPT-3 word embeddings capture signals pertaining to individual differences and intimate traits.

Facial recognition technology and human raters can predict political orientation from images of expressionless faces even when controlling for demographics and self-presentation.

Carefully standardized facial images of 591 participants were taken in the laboratory while controlling for self-presentation, facial expression, head orientation, and image properties. They were presented to human raters and a facial recognition algorithm: both humans (r = .21) and the algorithm (r = .22) could predict participants' scores on a political orientation scale (Cronbach's α = .94) decorrelated with age, gender, and ethnicity. These effects are on par with how well job interviews predict job success, or alcohol drives aggressiveness. The algorithm's predictive accuracy was even higher (r = .31) when it leveraged information on participants' age, gender, and ethnicity. Moreover, the associations between facial appearance and political orientation seem to generalize beyond our sample: The predictive model derived from standardized images (while controlling for age, gender, and ethnicity) could predict political orientation (r ≈ .13) from naturalistic images of 3,401 politicians from the United States, the United Kingdom, and Canada. The analysis of facial features associated with political orientation revealed that conservatives tended to have larger lower faces. The predictability of political orientation from standardized images has critical implications for privacy, the regulation of facial recognition technology, and understanding the origins and consequences of political orientation. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Overlap in meaning is a stronger predictor of semantic activation in GPT-3 than in humans.

Modern large language models generate texts that are virtually indistinguishable from those written by humans and achieve near-human performance in comprehension and reasoning tests. Yet, their complexity makes it difficult to explain and predict their functioning. We examined a state-of-the-art language model (GPT-3) using lexical decision tasks widely used to study the structure of semantic memory in humans. The results of four analyses showed that GPT-3's patterns of semantic activation are broadly similar to those observed in humans, showing significantly higher semantic activation in related (e.g., "lime-lemon") word pairs than in other-related (e.g., "sour-lemon") or unrelated (e.g., "tourist-lemon") word pairs. However, there are also significant differences between GPT-3 and humans. GPT-3's semantic activation is better predicted by similarity in words' meaning (i.e., semantic similarity) rather than their co-occurrence in the language (i.e., associative similarity). This suggests that GPT-3's semantic network is organized around word meaning rather than their co-occurrence in text.

Human-like intuitive behavior and reasoning biases emerged in large language models but disappeared in ChatGPT.

We design a battery of semantic illusions and cognitive reflection tests, aimed to elicit intuitive yet erroneous responses. We administer these tasks, traditionally used to study reasoning and decision-making in humans, to OpenAI's generative pre-trained transformer model family. The results show that as the models expand in size and linguistic proficiency they increasingly display human-like intuitive system 1 thinking and associated cognitive errors. This pattern shifts notably with the introduction of ChatGPT models, which tend to respond correctly, avoiding the traps embedded in the tasks. Both ChatGPT-3.5 and 4 utilize the input-output context window to engage in chain-of-thought reasoning, reminiscent of how people use notepads to support their system 2 thinking. Yet, they remain accurate even when prevented from engaging in chain-of-thought reasoning, indicating that their system-1-like next-word generation processes are more accurate than those of older models. Our findings highlight the value of applying psychological methodologies to study large language models, as this can uncover previously undetected emergent characteristics.

Political Attitudes and Disease Threat: Regional Pathogen Stress Is Associated With Conservative Ideology Only for Older Individuals.

What environmental factors are associated with individual differences in political ideology, and do such associations change over time? We examine whether reductions in pathogen prevalence in U.S. states over the past 60 years are associated with reduced associations between parasite stress and conservatism. We report a positive association between infection levels and conservative ideology in the United States during the 1960s and 1970s. However, this correlation reduces from the 1980s onwards. These results suggest that the ecological influence of infectious diseases may be larger for older people who grew up (or whose parents grew up) during earlier time periods. We test this hypothesis by analyzing the political affiliation of 45,000 Facebook users, and find a positive association between self-reported political affiliation and regional pathogen stress for older (>40 years) but not younger individuals. It is concluded that the influence of environmental pathogen stress on ideology may have reduced over time.

The Song Remains the Same: A Replication and Extension of the MUSIC Model.

There is overwhelming anecdotal and empirical evidence for individual differences in musical preferences. However, little is known about what drives those preferences. Are people drawn to particular musical genres (e.g., rap, jazz) or to certain musical properties (e.g., lively, loud)? Recent findings suggest that musical preferences can be conceptualized in terms of five orthogonal dimensions: Mellow, Unpretentious, Sophisticated, Intense, and Contemporary (conveniently, MUSIC). The aim of the present research is to replicate and extend that work by empirically examining the hypothesis that musical preferences are based on preferences for particular musical properties and psychological attributes as opposed to musical genres. Findings from Study 1 replicated the five-factor MUSIC structure using musical excerpts from a variety of genres and subgenres and revealed musical attributes that differentiate each factor. Results from Studies 2 and 3 show that the MUSIC structure is recoverable using musical pieces from only the jazz and rock genres, respectively. Taken together, the current work provides strong evidence that preferences for music are determined by specific musical attributes and that the MUSIC model is a robust framework for conceptualizing and measuring such preferences.

Facial recognition technology can expose political orientation from naturalistic facial images.

Ubiquitous facial recognition technology can expose individuals' political orientation, as faces of liberals and conservatives consistently differ. A facial recognition algorithm was applied to naturalistic images of 1,085,795 individuals to predict their political orientation by comparing their similarity to faces of liberal and conservative others. Political orientation was correctly classified in 72% of liberal-conservative face pairs, remarkably better than chance (50%), human accuracy (55%), or one afforded by a 100-item personality questionnaire (66%). Accuracy was similar across countries (the U.S., Canada, and the UK), environments (Facebook and dating websites), and when comparing faces across samples. Accuracy remained high (69%) even when controlling for age, gender, and ethnicity. Given the widespread use of facial recognition, our findings have critical implications for the protection of privacy and civil liberties.

Spouses' faces are similar but do not become more similar with time.

The widely disseminated convergence in physical appearance hypothesis posits that long-term partners' facial appearance converges with time due to their shared environment, emotional mimicry, and synchronized activities. Although plausible, this hypothesis is incompatible with empirical findings pertaining to a wide range of other traits-such as personality, intelligence, attitudes, values, and well-being-in which partners show initial similarity but do not converge over time. We solve this conundrum by reexamining this hypothesis using the facial images of 517 couples taken at the beginning of their marriages and 20 to 69 years later. Using two independent methods of estimating their facial similarity (human judgment and a facial recognition algorithm), we show that while spouses' faces tend to be similar at the beginning of marriage, they do not converge over time, bringing facial appearance in line with other personal characteristics.

Privacy in the age of psychological targeting.

Psychological targeting describes the practice of extracting people's psychological profiles from their digital footprints (e.g. their Facebook Likes, Tweets or credit card records) in order to influence their attitudes, emotions or behaviors through psychologically informed interventions at scale. We discuss how the increasingly blurred lines between public and private information, and the continuation of the outdated practices of notice and consent, challenge traditional conceptualizations of privacy in the context of psychological targeting. Drawing on the theory of contextual integrity, we argue that it is time to rethink privacy and move beyond the questions of who collects what data to how the data are being used. Finally, we suggest that regulations of psychological targeting should be accompanied by a mindset that fosters (1) privacy by design to make it easy for individuals to act in line with their privacy goals, as well as (2) disclosure by choice, to allow individuals to freely decide whether and when they might be willing to forsake their privacy for better service.

Individual-Level Analyses of the Impact of Parasite Stress on Personality: Reduced Openness Only for Older Individuals.

The parasite stress hypothesis predicts that individuals living in regions with higher infectious disease rates will show lower openness, agreeableness, and extraversion, but higher conscientiousness. This article, using data from more than 250,000 U.S. Facebook users, reports tests of these predictions at the level of both U.S. states and individuals and evaluates criticisms of previous findings. State-level results for agreeableness and conscientiousness are consistent with previously reported cross-national findings, but others (a significant positive correlation with extraversion and no correlation with openness) are not. However, effects of parasite stress on conscientiousness and agreeableness are not found when analyses account for the data's hierarchical structure and include controls. We find that only openness is robustly related to parasite stress in these analyses, and we also find a significant interaction with age: Older, but not younger, inhabitants of areas of high parasite stress show lower openness. Interpretations of the findings are discussed.

Bone Defect Repair Using a Bone Substitute Supported by Mesenchymal Stem Cells Derived from the Umbilical Cord.

OBJECTIVE: Bone defects or atrophy may arise as a consequence of injury, inflammation of various etiologies, and neoplastic or traumatic processes or as a result of surgical procedures. Sometimes the regeneration process of bone loss is impaired, significantly slowed down, or does not occur, e.g., in congenital defects. For the bone defect reconstruction, a piece of the removed bone from ala of ilium or bone transplantation from a decedent is used. Replacement of the autologous or allogenic source of the bone-by-bone substitute could reduce the number of surgeries and time in the pharmacological coma during the reconstruction of the bone defect. Application of mesenchymal stem cells in the reconstruction surgery may have positive influence on tissue regeneration by secretion of angiogenic factors, recruitment of other MSCs, or differentiation into osteoblasts. Materials and Methods. Mesenchymal stem cells derived from the umbilical cord (Wharton's jelly (WJ-MSC)) were cultured in GMP-grade DMEM low glucose supplemented with heparin, 10% platelet lysate, glucose, and antibiotics. In vitro WJ-MSCs were seeded on the bone substitute Bio-Oss Collagen® and cultured in the StemPro® Osteogenesis Differentiation Kit. During the culture on the 1st, 7th, 14th, and 21st day (day in vitro (DIV)), we analyzed viability (confocal microscopy) and adhesion capability (electron microscopy) of WJ-MSC on Bio-Oss scaffolds, gene expression (qPCR), and secretion of proteins (Luminex). In vivo Bio-Oss® scaffolds with WJ-MSC were transplanted to trepanation holes in the cranium to obtain their overgrowth. The computed tomography was performed 7, 14, and 21 days after surgery to assess the regeneration. RESULTS: The Bio-Oss® scaffold provides a favourable environment for WJ-MSC survival. WJ-MSCs in osteodifferentiation medium are able to attach and proliferate on Bio-Oss® scaffolds. Results obtained from qPCR and Luminex® indicate that WJ-MSCs possess the ability to differentiate into osteoblast-like cells and may induce osteoclastogenesis, angiogenesis, and mobilization of host MSCs. In animal studies, WJ-MSCs seeded on Bio-Oss® increased the scaffold integration with host bone and changed their morphology to osteoblast-like cells. CONCLUSIONS: The presented construct consisted of Bio-Oss®, the scaffold with high flexibility and plasticity, approved for clinical use with seeded immunologically privileged WJ-MSC which may be considered reconstructive therapy in bone defects.