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.

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.

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.

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).

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.

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.

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: , which are extensively used in physics but not in the study of cognitive processes.

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).

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.

From physical to social interactions: The relative entropy model.

Complex social systems at various scales of analysis (e.g. dyads, families, tribes, etc.

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.

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.

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.

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.

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.

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.

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.

Metaphor identification in large texts corpora.

Identifying metaphorical language-use (e.g., sweet child) is one of the challenges facing natural language processing.

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.

A novel methodology for identifying emerging themes in small group dynamics.

An understanding of group dynamics, such as the dynamics in family psychotherapy, is of great importance to mental health practitioners and other experts who seek to understand the group as a working whole. In this context, identifying themes emerging from the dynamics is of prime importance. However, the lack of formal tools makes it difficult to validly identify emerging themes.

How language enables abstraction: a study in computational cultural psychology.

The idea that language mediates our thoughts and enables abstract cognition has been a key idea in socio-cultural psychology. However, it is not clear what mechanisms support this process of abstraction. Peirce argued that one mechanism by which language enables abstract thought is hypostatic abstraction, the process through which a predicate (e.

Penultimate interpretation.

Interpretation is at the center of psychoanalytic activity. However, interpretation is always challenged by that which is beyond our grasp, the 'dark matter' of our mind, what Bion describes as ' O'. O is one of the most central and difficult concepts in Bion's thought.

Psychosomatic symptoms as biomarkers: transcending the psyche-soma dichotomy.

Following the advancement in understanding dynamical systems, the author presents a novel metaphor of psychosomatic symptoms as low-dimensional biomarkers. This metaphor, which transcends the old binary of psyche-soma, resonates with classical psychoanalytic concepts and with Matte-Blanco's idea of repetition as indicative of dimensionality reduction. The relevance of this metaphor for explanation, diagnosis, and treatment is illustrated through a case study of a male patient suffering from hyperprolactinemia.