A Data Set of Synthetic Utterances for Computational Personality Analysis.

The computational analysis of human personality has mainly focused on the Big Five personality theory, and the psychodynamic approach is almost nonexistent despite its rich theoretical grounding and relevance to various tasks. Here, we provide a data set of 4972 synthetic utterances corresponding with five personality dimensions described by the psychodynamic approach: depressive, obsessive, paranoid, narcissistic, and anti-social psychopathic. The utterances have been generated through AI with a deep theoretical orientation that motivated the design of prompts for GPT-4. The dataset has been validated through 14 tests, and it may be relevant for the computational study of human personality and the design of authentic persona in digital domains, from gaming to the artistic generation of movie characters.

A Dataset of 10,000 Situations for Research in Computational Social Sciences Psychology and the Humanities.

It has been realized that situational dimensions, as represented by human beings, are crucial for understanding human behavior. The Riverside Situational Q (RSQ) is a tool that measures the psychological properties of situations. However, the RSQ-4 includes only 90 items and may have limited use for researchers interested in measuring situational dimensions using a computational approach. Here we present a corpus of 10,000 artificially generated situations corresponding mostly with the RSQ-4. The dataset was generated using GPT, the state-of-the-art large language model. The dataset validity is established through inter-judge reliability, and four experiments on large datasets support its quality. The dataset and the code used for generating 100 situational dimensions may be useful for researchers interested in measuring situational dimensions in textual data.

AI for identifying social norm violation.

Identifying social norms and their violation is a challenge facing several projects in computational science. This paper presents a novel approach to identifying social norm violations. We used GPT-3, zero-shot classification, and automatic rule discovery to develop simple predictive models grounded in psychological knowledge. Tested on two massive datasets, the models present significant predictive performance and show that even complex social situations can be functionally analyzed through modern computational tools.

Violation of expectations is correlated with satisfaction following hip arthroscopy.

PURPOSE: The mechanism by which preoperative expectations may be associated with patient satisfaction and procedural outcomes following hip preservation surgery (HPS) is far from simple or linear. The purpose of this study is to better understand patient expectations regarding HPS and their relationship with patient-reported outcomes (PROs) and satisfaction using machine learning (ML) algorithms. METHODS: Patients scheduled for hip arthroscopy completed the Hip Preservation Surgery Expectations Survey (HPSES) and the pre- and a minimum 2 year postoperative International Hip Outcome Tool (iHOT-33). Patient demographics, including age, gender, occupation, and body mass index (BMI), were also collected. At the latest follow-up, patients were evaluated for subjective satisfaction and postoperative complications. ML algorithms and standard statistics were used. RESULTS: A total of 69 patients were included in this study (mean age 33.7 ± 13.1 years, 62.3% males). The mean follow-up period was 27 months. The mean HPSES score, patient satisfaction, preoperative, and postoperative iHOT-33 were 83.8 ± 16.5, 75.9 ± 26.9, 31.6 ± 15.8, and 73 ± 25.9, respectively. Fifty-nine patients (86%) reported that they would undergo the surgery again, with no significant difference with regards to expectations. A significant difference was found with regards to expectation violation (p < 0.001). Expectation violation scores were also found to be significantly correlated with satisfaction. CONCLUSION: ML algorithms utilized in this study demonstrate that violation of expectations plays an important predictive role in postoperative outcomes and patient satisfaction and is associated with patients' willingness to undergo surgery again. LEVEL OF EVIDENCE: IV.

Improving pre-bariatric surgery diagnosis of hiatal hernia using machine learning models.

BACKGROUND: Bariatric patients have a high prevalence of hiatal hernia (HH). HH imposes various difficulties in performing laparoscopic bariatric surgery. Preoperative evaluation is generally inaccurate, establishing the need for better preoperative assessment. OBJECTIVE: To utilize machine learning ability to improve preoperative diagnosis of HH. METHODS: Machine learning (ML) prediction models were utilized to predict preoperative HH diagnosis using data from a prospectively maintained database of bariatric procedures performed in a high-volume bariatric surgical center between 2012 and 2015. We utilized three optional ML models to improve preoperative contrast swallow study (SS) prediction, automatic feature selection was performed using patients' features. The prediction efficacy of the models was compared to SS. RESULTS: During the study period, 2482 patients underwent bariatric surgery. All underwent preoperative SS, considered the baseline diagnostic modality, which identified 236 (9.5%) patients with presumed HH. Achieving 38.5% sensitivity and 92.9% specificity. ML models increased sensitivity up to 60.2%, creating three optional models utilizing data and patient selection process for this purpose. CONCLUSION: Implementing machine learning derived prediction models enabled an increase of up to 1.5 times of the baseline diagnostic sensitivity. By harnessing this ability, we can improve traditional medical diagnosis, increasing the sensitivity of preoperative diagnostic workout.

Extreme Rare Events Identification Through Jaynes Inferential Approach.

The identification of extreme rare events is a challenge that appears in several real-world contexts, from screening for solo perpetrators to the prediction of failures in industrial production. In this article, we explain the challenge and present a new methodology for addressing it, a methodology that may be considered in terms of features engineering. This methodology, which is based on Jaynes inferential approach, is tested on a dataset dealing with failures in production in the pulp-and-paper industry. The results are discussed in the context of the benefits of using the approach for features engineering in practical contexts involving measurable risks.

Short-term prediction through ordinal patterns.

Prediction in natural environments is a challenging task, and there is a lack of clarity around how a myopic organism can make short-term predictions given limited data availability and cognitive resources. In this context, we may ask what kind of resources are available to the organism to help it address the challenge of short-term prediction within its own cognitive limits. We point to one potentially important resource: ordinal patterns, which are extensively used in physics but not in the study of cognitive processes. We explain the potential importance of ordinal patterns for short-term prediction, and how natural constraints imposed through (i) ordinal pattern types, (ii) their transition probabilities and (iii) their irreversibility signature may support short-term prediction. Having tested these ideas on a massive dataset of Bitcoin prices representing a highly fluctuating environment, we provide preliminary empirical support showing how organisms characterized by bounded rationality may generate short-term predictions by relying on ordinal patterns.

Screening for potential school shooters through the weight of evidence.

The challenge of automatically screening for potential school shooters involves several difficulties. In this paper, we present a simple and interpretable methodology for screening for potential school shooters through (1) the psychological textual signature of the shooter and (2) Jaynes approach for measuring the weight of evidence. We have tested our proposed approach on a dataset of texts written by shooters and non-shooters alike (N = 5047). Our major finding is that the methodology can successfully support the screening for potential shooters in interpretable way. The major implication for stakeholders is that there is great potential in developing screening systems for improving the safely of schools. However, developing such a system is a project that must be actualized within an integrated system of "command and control".

Utilization of machine-learning models to accurately predict the risk for critical COVID-19.

Among patients with Coronavirus disease (COVID-19), the ability to identify patients at risk for deterioration during their hospital stay is essential for effective patient allocation and management. To predict patient risk for critical COVID-19 based on status at admission using machine-learning models. Retrospective study based on a database of tertiary medical center with designated departments for patients with COVID-19. Patients with severe COVID-19 at admission, based on low oxygen saturation, low partial arterial oxygen pressure, were excluded. The primary outcome was risk for critical disease, defined as mechanical ventilation, multi-organ failure, admission to the ICU, and/or death. Three different machine-learning models were used to predict patient deterioration and compared to currently suggested predictors and to the APACHEII risk-prediction score. Among 6995 patients evaluated, 162 were hospitalized with non-severe COVID-19, of them, 25 (15.4%) patients deteriorated to critical COVID-19. Machine-learning models outperformed the all other parameters, including the APACHE II score (ROC AUC of 0.92 vs. 0.79, respectively), reaching 88.0% sensitivity, 92.7% specificity and 92.0% accuracy in predicting critical COVID-19. The most contributory variables to the models were APACHE II score, white blood cell count, time from symptoms to admission, oxygen saturation and blood lymphocytes count. Machine-learning models demonstrated high efficacy in predicting critical COVID-19 compared to the most efficacious tools available. Hence, artificial intelligence may be applied for accurate risk prediction of patients with COVID-19, to optimize patients triage and in-hospital allocation, better prioritization of medical resources and improved overall management of the COVID-19 pandemic.

From physical to social interactions: The relative entropy model.

Complex social systems at various scales of analysis (e.g. dyads, families, tribes, etc.) are formed and maintained through verbal interactions. Therefore, the ability to (1) model these interactions and (2) to use models of interaction for identifying significant relations may be of interest to the social sciences. Adopting the perspective of social physics, we present a general approach for modeling interactions through relative entropy. For illustrating the benefits of the approach, we derive measures of "perspective-taking" and use them for identifying significant-romantic relations in a data set composed of the verbal interactions taken place at the famous TV series "Sex and the City". Using these measures, we show that significant-romantic relations can be identified with success. These results provide preliminary support for the benefits of using the proposed approach.

The Adaptive Behavior of a Soccer Team: An Entropy-Based Analysis.

To optimize its performance, a competitive team, such as a soccer team, must maintain a delicate balance between organization and disorganization. On the one hand, the team should maintain organized patterns of behavior to maximize the cooperation between its members. On the other hand, the team's behavior should be disordered enough to mislead its opponent and to maintain enough degrees of freedom. In this paper, we have analyzed this dynamic in the context of soccer games and examined whether it is correlated with the team's performance. We measured the organization associated with the behavior of a soccer team through the Tsallis entropy of ball passes between the players. Analyzing data taken from the English Premier League (2015/2016), we show that the team's position at the end of the season is correlated with the team's entropy as measured with a super-additive entropy index. Moreover, the entropy score of a team significantly contributes to the prediction of the team's position at the end of the season beyond the prediction gained by the team's position at the end of the previous season.

Dynamic Patterns of Expertise: The Case of Orthopedic Medical Diagnosis.

The aim of this study was to analyze dynamic patterns for scanning femoroacetabular impingement (FAI) radiographs in orthopedics, in order to better understand the nature of expertise in radiography. Seven orthopedics residents with at least two years of expertise and seven board-certified orthopedists participated in the study. The participants were asked to diagnose 15 anteroposterior (AP) pelvis radiographs of 15 surgical patients, diagnosed with FAI syndrome. Eye tracking data were recorded using the SMI desk-mounted tracker and were analyzed using advanced measures and methodologies, mainly recurrence quantification analysis. The expert orthopedists presented a less predictable pattern of scanning the radiographs although there was no difference between experts and non-experts in the deterministic nature of their scan path. In addition, the experts presented a higher percentage of correct areas of focus and more quickly made their first comparison between symmetric regions of the pelvis. We contribute to the understanding of experts' process of diagnosis by showing that experts are qualitatively different from residents in their scanning patterns. The dynamic pattern of scanning that characterizes the experts was found to have a more complex and less predictable signature, meaning that experts' scanning is simultaneously both structured (i.e. deterministic) and unpredictable.

Profiling School Shooters: Automatic Text-Based Analysis.

School shooters present a challenge to both forensic psychiatry and law enforcement agencies. The relatively small number of school shooters, their various characteristics, and the lack of in-depth analysis of all of the shooters prior to the shooting add complexity to our understanding of this problem. In this short paper, we introduce a new methodology for automatically profiling school shooters. The methodology involves automatic analysis of texts and the production of several measures relevant for the identification of the shooters. Comparing texts written by 6 school shooters to 6056 texts written by a comparison group of male subjects, we found that the shooters' texts scored significantly higher on the Narcissistic Personality dimension as well as on the Humilated and Revengeful dimensions. Using a ranking/prioritization procedure, similar to the one used for the automatic identification of sexual predators, we provide support for the validity and relevance of the proposed methodology.

Identifying the location of a concealed object through unintentional eye movements.

In some investigative and interrogative contexts, the investigator is seeking to identify the location of an object (e.g., implanted bomb) which is known to a given subject (e.g., a terrorist). In this paper, we present a non-intrusive methodology for uncovering the loci of a concealed object by analyzing the subject's eye movements. Using a combination of eye tracking, psychological manipulation and a search algorithm, we have performed two experiments. In the first experiment, we have gained 58% hit rate in identifying the location of the concealed object and in the second experiment 56% hit rate. The pros and cons of the methodology for forensic investigation are discussed.

Personality from a cognitive-biological perspective.

The term "personality" is used to describe a distinctive and relatively stable set of mental traits that aim to explain the organism's behavior. The concept of personality that emerged in human psychology has been also applied to the study of non-human organisms from birds to horses. In this paper, I critically review the concept of personality from an interdisciplinary perspective, and point to some ideas that may be used for developing a cognitive-biological theory of personality. Integrating theories and research findings from various fields such as cognitive ethnology, clinical psychology, and neuroscience, I argue that the common denominator of various personality theories are neural systems of threat/trust management and their emotional, cognitive, and behavioral dimensions. In this context, personality may be also conceived as a meta-heuristics both human and non-human organisms apply to model and predict the behavior of others. The paper concludes by suggesting a minimal computational model of personality that may guide future research.

Change in the embedding dimension as an indicator of an approaching transition.

Predicting a transition point in behavioral data should take into account the complexity of the signal being influenced by contextual factors. In this paper, we propose to analyze changes in the embedding dimension as contextual information indicating a proceeding transitive point, called OPtimal Embedding tRANsition Detection (OPERAND). Three texts were processed and translated to time-series of emotional polarity. It was found that changes in the embedding dimension proceeded transition points in the data. These preliminary results encourage further research into changes in the embedding dimension as generic markers of an approaching transition point.

A vectorial semantics approach to personality assessment.

Personality assessment and, specifically, the assessment of personality disorders have traditionally been indifferent to computational models. Computational personality is a new field that involves the automatic classification of individuals' personality traits that can be compared against gold-standard labels. In this context, we introduce a new vectorial semantics approach to personality assessment, which involves the construction of vectors representing personality dimensions and disorders, and the automatic measurements of the similarity between these vectors and texts written by human subjects. We evaluated our approach by using a corpus of 2468 essays written by students who were also assessed through the five-factor personality model. To validate our approach, we measured the similarity between the essays and the personality vectors to produce personality disorder scores. These scores and their correspondence with the subjects' classification of the five personality factors reproduce patterns well-documented in the psychological literature. In addition, we show that, based on the personality vectors, we can predict each of the five personality factors with high accuracy.

Metaphor identification in large texts corpora.

Identifying metaphorical language-use (e.g., sweet child) is one of the challenges facing natural language processing. This paper describes three novel algorithms for automatic metaphor identification. The algorithms are variations of the same core algorithm. We evaluate the algorithms on two corpora of Reuters and the New York Times articles. The paper presents the most comprehensive study of metaphor identification in terms of scope of metaphorical phrases and annotated corpora size. Algorithms' performance in identifying linguistic phrases as metaphorical or literal has been compared to human judgment. Overall, the algorithms outperform the state-of-the-art algorithm with 71% precision and 27% averaged improvement in prediction over the base-rate of metaphors in the corpus.

Proactive screening for depression through metaphorical and automatic text analysis.

OBJECTIVE: Proactive and automatic screening for depression is a challenge facing the public health system. This paper describes a system for addressing the above challenge. MATERIALS AND METHOD: The system implementing the methodology--Pedesis--harvests the Web for metaphorical relations in which depression is embedded and extracts the relevant conceptual domains describing it. This information is used by human experts for the construction of a "depression lexicon". The lexicon is used to automatically evaluate the level of depression in texts or whether the text is dealing with depression as a topic. RESULTS: Tested on three corpora of questions addressed to a mental health site the system provides 9% improvement in prediction whether the question is dealing with depression. Tested on a corpus of Blogs, the system provides 84.2% correct classification rate (p<.001) whether a post includes signs of depression. By comparing the system's prediction to the judgment of human experts we achieved an average 78% precision and 76% recall. CONCLUSION: Depression can be automatically screened in texts and the mental health system may benefit from this screening ability.