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Introduction: With rapid advancements in natural language processing (NLP), predicting personality using this technology has become a significant research interest. In personality prediction, exploring appropriate questions that elicit natural language is particularly important because questions determine the context of responses. This study aimed to predict levels of neuroticism-a core psychological trait known to predict various psychological outcomes-using responses to a series of open-ended questions developed based on the five-factor model of personality. This study examined the model's accuracy and explored the influence of item content in predicting neuroticism. Methods: A total of 425 Korean adults were recruited and responded to 18 open-ended questions about their personalities, along with the measurement of the Five-Factor Model traits. In total, 30,576 Korean sentences were collected. To develop the prediction models, the pre-trained language model KoBERT was used. Accuracy, F1 Score, Precision, and Recall were calculated as evaluation metrics. Results: The results showed that items inquiring about social comparison, unintended harm, and negative feelings performed better in predicting neuroticism than other items. For predicting depressivity, items related to negative feelings, social comparison, and emotions showed superior performance. For dependency, items related to unintended harm, social dominance, and negative feelings were the most predictive. Discussion: We identified items that performed better at neuroticism prediction than others. Prediction models developed based on open-ended questions that theoretically aligned with neuroticism exhibited superior predictive performance.

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The policymaking process is largely opaque, especially regarding the actual writing of the policy. To attempt to better understand this complex process, we utilized mixed methods in our evaluation of an intervention. However, the process of mixing methods can be messy, and thus may require recalibration during the evaluation itself. Yet, in comparison to reporting results, relatively little attention is paid to the effects of mixing methods on the evaluation process. In this article, we take a reflexive approach to reporting a mixed methods evaluation of an intervention on the use of research evidence in U.S. federal policymaking. We focus on the research process in a qualitative coding team, and the effects of mixing methods on that process. Additionally, we report in general terms how to interpret multinomial logistic regressions, an underused analysis type applicable to many evaluations. Thus, this reflexive piece contributes (1) findings from evaluation of the intervention on the policymaking process, (2) an example of mixing methods leading to unexpected findings and future directions, (3) a report about the evaluation process itself, and (4) a tutorial for those new to multinomial logistic regressions.

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BACKGROUND: This research aimed to investigate the experience of Neuromyelitis Optica Spectrum Disorders (NMOSD) by integrating the perspectives of patients, caregivers and clinicians through narrative-based medicine to provide new insights to improve care relationships. METHODS: The research was conducted in the second half of 2022 and involved six Italian centres treating NMOSD and targeted adult patients, their caregivers and healthcare providers to collect the three points of view of living with or caring for this rare disease, still difficult to treat despite the pharmacological options. Narratives followed a structured outline according to the time: yesterday-today-tomorrow, to capture all disease phases. RESULTS: Twenty-five patients diagnosed with NMOSD, ten caregivers and 13 healthcare providers participated in the research. Patients reported symptoms limiting their daily activities and strongly impacting their social dimension. We noticed improvements across disease duration, whilst the persistence of limitations was recurrent in patients with longer diagnoses. Caregivers' narratives mainly share experiences of their daily life changes, the burden of the caregiving role and the solutions identified, if any. Healthcare providers defined their role as a guide. CONCLUSION: Limitations in activities are prominent in the lives of people with NMOSD, along with fatigue. Family members are the weakest link in the chain and need information and support. Healthcare professionals are attentive to the helping dimension.

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Recently, masked autoencoders have demonstrated their feasibility in extracting effective image and text features (e.g., BERT for natural language processing (NLP) and MAE in computer vision (CV)). This study investigates the potential of applying these techniques to vision-and-language representation learning in the medical domain. To this end, we introduce a self-supervised learning paradigm, multi-modal masked autoencoders (M3AE). It learns to map medical images and texts to a joint space by reconstructing pixels and tokens from randomly masked images and texts. Specifically, we design this approach from three aspects: First, taking into account the varying information densities of vision and language, we employ distinct masking ratios for input images and text, with a notably higher masking ratio for images; Second, we utilize visual and textual features from different layers for reconstruction to address varying levels of abstraction in vision and language; Third, we develop different designs for vision and language decoders. We establish a medical vision-and-language benchmark to conduct an extensive evaluation. Our experimental results exhibit the effectiveness of the proposed method, achieving state-of-the-art results on all downstream tasks. Further analyses validate the effectiveness of the various components and discuss the limitations of the proposed approach. The source code is available at https://github.com/zhjohnchan/M3AE.

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Analyzing online retrospective experience reports of psychedelic use can provide valuable insight into their acute subjective effects. Such reports are unexplored in relation to mystical states, which are thought to be a therapeutic mechanism within psychedelic-assisted psychotherapy. We created a set of words that, when encountered in an experience report, indicate the occurrence of mystical elements within the experience. We used the Shroomery.org website to retrieve 7317 publicly available retrospective psychedelic experience reports of psychedelic use, primarily of psilocybin, and have a designated experience intensity level self-assessed by the text authors during submission of the report. We counted the mystical language words using Linguistic Inquiry and Word Count (LIWC) software and additionally performed sentiment analysis of all reports. We found that the occurrence of mystical language grew with increased self-reported experience intensity. We also found that negative sentiment increased, and positive sentiment decreased as self-reported psychedelic experience intensity increased. These two findings raise the question of whether mystical experiences can co-exist with challenging elements within the psychedelic experience, a consideration for future qualitative studies. We present a new mystical language dictionary measure for further use and expansion, with some suggestions on how it can be used in future studies.

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Borderline personality disorder (BPD) is characterized by severe interpersonal dysfunction, yet the underlying nature of such dysfunction remains poorly understood. The present study adopted a behavioral approach to more objectively describe the social-cognitive contributors to interpersonal dysfunction in BPD. Participants (N = 530) completed an online survey comprising validated measures of BPD features and other problematic interpersonal traits (e.g., narcissism), as well as a writing prompt where they were asked to share their personal thoughts about relationships. Computerized language analysis methods were used to quantify various psychosocial dimensions of participants' writing, which were incorporated into a principal component analysis. Analyses revealed four core social dimensions of thought: (1) Connectedness/Intimacy; (2) Immediacy; (3) Social Rumination; (4) Negative Affect. All four dimensions correlated with BPD features in intuitive ways, some of which were specific to BPD. This study highlights the value of natural language analysis to explore fundamental dimensions of personality disorder.

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Significant advances in sensor technology and virtual reality (VR) offer new possibilities for early and effective detection of mild cognitive impairment (MCI), and this wealth of data can improve the early detection and monitoring of patients. In this study, we proposed a non-invasive and effective MCI detection protocol based on electroencephalogram (EEG), speech, and digitized cognitive parameters. The EEG data, speech data, and digitized cognitive parameters of 86 participants (44 MCI patients and 42 healthy individuals) were monitored using a wearable EEG device and a VR device during the resting state and task (the VR-based language task we designed). Regarding the features selected under different modality combinations for all language tasks, we performed leave-one-out cross-validation for them using four different classifiers. We then compared the classification performance under multimodal data fusion using features from a single language task, features from all tasks, and using a weighted voting strategy, respectively. The experimental results showed that the collaborative screening of multimodal data yielded the highest classification performance compared to single-modal features. Among them, the SVM classifier using the RBF kernel obtained the best classification results with an accuracy of 87%. The overall classification performance was further improved using a weighted voting strategy with an accuracy of 89.8%, indicating that our proposed method can tap into the cognitive changes of MCI patients. The MCI detection scheme based on EEG, speech, and digital cognitive parameters proposed in this study provides a new direction and support for effective MCI detection, and suggests that VR and wearable devices will be a promising direction for easy-to-perform and effective MCI detection, offering new possibilities for the exploration of VR technology in the field of language cognition.

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Introduction: Smartphone technology can provide an effective means to bring real-life and (near-)real-time feedback from hearing aid wearers into the clinic. Ecological Momentary Assessment (EMA) encourages listeners to report on their experiences during or shortly after they take place in order to minimize recall bias, e.g., guided by surveys in a mobile application. Allowing listeners to describe experiences in their own words, further, ensures that answers are independent of predefined jargon or of how survey questions are formulated. Through these means, one can obtain ecologically valid sets of data, for instance during a hearing aid trial, which can support clinicians to assess the needs of their clients, provide directions for fine-tuning, and counselling. At a larger scale, such datasets would facilitate training of machine learning algorithms that could help hearing technology to anticipate user needs. Methods: In this retrospective, exploratory analysis of a clinical data set, we performed a cluster analysis on 8,793 open-text statements, which were collected through self-initiated EMAs, provided by 2,301 hearing aid wearers as part of their hearing care. Our aim was to explore how listeners describe their daily life experiences with hearing technology in (near-)real-time, in their own words, by identifying emerging themes in the reports. We also explored whether identified themes correlated with the nature of the experiences, i.e., self-reported satisfaction ratings indicating a positive or negative experience. Results: Results showed that close to 60% of listeners' reports related to speech intelligibility in challenging situations and sound quality dimensions, and tended to be valued as positive experiences. In comparison, close to 40% of reports related to hearing aid management, and tended to be valued as negative experiences. Discussion: This first report of open-text statements, collected through self-initiated EMAs as part of clinical practice, shows that, while EMA can come with a participant burden, at least a subsample of motivated hearing aid wearers could use these novel tools to provide feedback to inform more responsive, personalized, and family-centered hearing care.

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Rumination is closely related to mental disorders and can thus be used as a marker of their presence or a predictor of their development. The presence of masking and fabrication in psychological selection can lead to inaccurate detection of psychological disorders. Human language is considered crucial in eliciting specific conscious activities, and the use of natural language processing (NLP) in the development of questionnaires for psychological tests has the potential to elicit immersive ruminative thinking, leading to changes in neural activity. Electroencephalography (EEG) is commonly used to detect and record neural activity in the human brain and is sensitive to changes in brain activity. In this study, we used NLP to develop a questionnaire to induce ruminative thinking and then recorded the EEG signals in response to the questionnaire. The behavioral results revealed that ruminators exhibited higher arousal rates and longer reaction times, specifically in response to the ruminative items of the questionnaire. The EEG results showed no significant difference between the ruminators and the control group during the resting state; however, a significant alteration in the coherence of the entire brain of the ruminators existed while they were answering the ruminative items. No differences were found in the control participants while answering the two items. These behavioral and EEG results indicate that the questionnaire elicited immersive ruminative thinking, specifically in the ruminators. Therefore, the questionnaire designed using NLP is capable of eliciting ruminative thinking in ruminators, offering a promising approach for the early detection of mental disorders in psychological selection.

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Incoherent speech in schizophrenia has long been described as the mind making "leaps" of large distances between thoughts and ideas. Such a view seems intuitive, and for almost two decades, attempts to operationalize these conceptual "leaps" in spoken word meanings have used language-based embedding spaces. An embedding space represents meaning of words as numerical vectors where a greater proximity between word vectors represents more shared meaning. However, there are limitations with word vector-based operationalizations of coherence which can limit their appeal and utility in clinical practice. First, the use of esoteric word embeddings can be conceptually hard to grasp, and this is complicated by several different operationalizations of incoherent speech. This problem can be overcome by a better visualization of methods. Second, temporal information from the act of speaking has been largely neglected since models have been built using written text, yet speech is spoken in real time. This issue can be resolved by leveraging time stamped transcripts of speech. Third, contextual information - namely the situation of where something is spoken - has often only been inferred and never explicitly modeled. Addressing this situational issue opens up new possibilities for models with increased temporal resolution and contextual relevance. In this paper, direct visualizations of semantic distances are used to enable the inspection of examples of incoherent speech. Some common operationalizations of incoherence are illustrated, and suggestions are made for how temporal and spatial contextual information can be integrated in future implementations of measures of incoherence.

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The Semantic Coherence Dataset has been designed to experiment with semantic coherence metrics. More specifically, the dataset has been built to the ends of testing whether probabilistic measures, such as perplexity, provide stable scores to analyze spoken language. Perplexity, which was originally conceived as an information-theoretic measure to assess the probabilistic inference properties of language models, has recently been proven to be an appropriate tool to categorize speech transcripts based on semantic coherence accounts. More specifically, perplexity has been successfully employed to discriminate subjects suffering from Alzheimer Disease and healthy controls. Collected data include speech transcripts, intended to investigate semantic coherence at different levels: data are thus arranged into two classes, to investigate intra-subject semantic coherence, and inter-subject semantic coherence. In the former case transcripts from a single speaker can be employed to train and test language models and to explore whether the perplexity metric provides stable scores in assessing talks from that speaker, while allowing to distinguish between two different forms of speech, political rallies and interviews. In the latter case, models can be trained by employing transcripts from a given speaker, and then used to measure how stable the perplexity metric is when computed using the model from that user and transcripts from different users. Transcripts were extracted from talks lasting almost 13 hours (overall 12:45:17 and 120,326 tokens) for the former class; and almost 30 hours (29:47:34 and 252,270 tokens) for the latter one. Data herein can be reused to perform analyses on measures built on top of language models, and more in general on measures that are aimed at exploring the linguistic features of text documents.

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Neurooncology in the 21st century is a complex discipline integrating achievements of fundamental and applied neurosciences. Complex processes and data in clinical neurooncology determine the necessity for advanced methods of mathematical modeling and predictive analytics to obtain new scientific knowledge. Such methods are currently being developed in computer science (artificial intelligence). This review is devoted to potential and range of possible applications of artificial intelligence technologies in neurooncology with a special emphasis on glial tumors. Our conclusions may be valid for other areas of clinical medicine.

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In recent decades, epidemic and pandemic illnesses have grown prevalent and are a regular source of concern throughout the world. The extent to which the globe has been affected by the COVID-19 epidemic is well documented. Smart technology is now widely used in medical applications, with the automated detection of status and feelings becoming a significant study area. As a result, a variety of studies have begun to focus on the automated detection of symptoms in individuals infected with a pandemic or epidemic disease by studying their body language. The recognition and interpretation of arm and leg motions, facial recognition, and body postures is still a developing field, and there is a dearth of comprehensive studies that might aid in illness diagnosis utilizing artificial intelligence techniques and technologies. This literature review is a meta review of past papers that utilized AI for body language classification through full-body tracking or facial expressions detection for various tasks such as fall detection and COVID-19 detection, it looks at different methods proposed by each paper, their significance and their results.

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Devising automatic tools to assist specialists in the early detection of mental disturbances and psychotic disorders is to date a challenging scientific problem and a practically relevant activity. In this work we explore how language models (that are probability distributions over text sequences) can be employed to analyze language and discriminate between mentally impaired and healthy subjects. We have preliminarily explored whether perplexity can be considered a reliable metrics to characterize an individual's language. Perplexity was originally conceived as an information-theoretic measure to assess how much a given language model is suited to predict a text sequence or, equivalently, how much a word sequence fits into a specific language model. We carried out an extensive experimentation with healthy subjects, and employed language models as diverse as N-grams - from 2-grams to 5-grams - and GPT-2, a transformer-based language model. Our experiments show that irrespective of the complexity of the employed language model, perplexity scores are stable and sufficiently consistent for analyzing the language of individual subjects, and at the same time sensitive enough to capture differences due to linguistic registers adopted by the same speaker, e.g., in interviews and political rallies. A second array of experiments was designed to investigate whether perplexity scores may be used to discriminate between the transcripts of healthy subjects and subjects suffering from Alzheimer Disease (AD). Our best performing models achieved full accuracy and F-score (1.00 in both precision/specificity and recall/sensitivity) in categorizing subjects from both the AD class, and control subjects. These results suggest that perplexity can be a valuable analytical metrics with potential application to supporting early diagnosis of symptoms of mental disorders.

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Given the current COVID-19 pandemic, medical research today focuses on epidemic diseases. Innovative technology is incorporated in most medical applications, emphasizing the automatic recognition of physical and emotional states. Most research is concerned with the automatic identification of symptoms displayed by patients through analyzing their body language. The development of technologies for recognizing and interpreting arm and leg gestures, facial features, and body postures is still in its early stage. More extensive research is needed using artificial intelligence (AI) techniques in disease detection. This paper presents a comprehensive survey of the research performed on body language processing. Upon defining and explaining the different types of body language, we justify the use of automatic recognition and its application in healthcare. We briefly describe the automatic recognition framework using AI to recognize various body language elements and discuss automatic gesture recognition approaches that help better identify the external symptoms of epidemic and pandemic diseases. From this study, we found that since there are studies that have proven that the body has a language called body language, it has proven that language can be analyzed and understood by machine learning (ML). Since diseases also show clear and different symptoms in the body, the body language here will be affected and have special features related to a particular disease. From this examination, we discovered that it is possible to specialize the features and language changes of each disease in the body. Hence, ML can understand and detect diseases such as pandemic and epidemic diseases and others.

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To what degree can we determine people's connections with groups through the language they use? In recent years, large archives of behavioral data from social media communities have become available to social scientists, opening the possibility of tracking naturally occurring group identity processes. A feature of most digital groups is that they rely exclusively on the written word. Across 3 studies, we developed and validated a language-based metric of group identity strength and demonstrated its potential in tracking identity processes in online communities. In Studies 1a-1c, 873 people wrote about their connections to various groups (country, college, or religion). A total of 2 language markers of group identity strength were found: high affiliation (more words like we, togetherness) and low cognitive processing or questioning (fewer words like think, unsure). Using these markers, a language-based unquestioning affiliation index was developed and applied to in-class stream-of-consciousness essays of 2,161 college students (Study 2). Greater levels of unquestioning affiliation expressed in language predicted not only self-reported university identity but also students' likelihood of remaining enrolled in college a year later. In Study 3, the index was applied to naturalistic Reddit conversations of 270,784 people in 2 online communities of supporters of the 2016 presidential candidates-Hillary Clinton and Donald Trump. The index predicted how long people would remain in the group (3a) and revealed temporal shifts mirroring members' joining and leaving of groups (3b). Together, the studies highlight the promise of a language-based approach for tracking and studying group identity processes in online groups.

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The practice of nurse health coaching (NHC) draws from the art and science of nursing, behavioral sciences, and evidence-based health-coaching methods. This secondary analysis of the audio-recorded natural language of participants during NHC sessions of our recent 8-week RCT evaluates improvement over time in cognitive−behavioral outcomes: change talk, resiliency, self-efficacy/independent agency, insight and pattern recognition, and building towards sustainability. We developed a measurement tool for coding, Indicators of Health Behavior Change (IHBC), that was designed to allow trained health-coach experts to assess the presence and frequency of the indicators in the natural language content of participants. We used a two-step method for randomly selecting the 20 min audio-recorded session that was analyzed at each time point. Fifty-six participants had high-quality audio recordings of the NHC sessions. Twelve participants were placed in the social determinants of health (SDH) group based on the following: low income (

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Interpersonal relationships are vital to our well-being. In recent years, it has become increasingly common to seek relationship help through anonymous online platforms. Accordingly, we conducted a large-scale analysis of real-world relationship help-seeking to create a descriptive overview of the nature and substance of online relationship help-seeking. By analyzing the demographic characteristics and language of relationship help-seekers on Reddit (N = 184,631), we establish the first-ever big data analysis of relationship help-seeking and relationship problems in situ among the general population. Our analyses highlight real-world relationship struggles found in the general population, extending beyond past work that is typically limited to counseling/intervention settings. We find that relationship problem estimates from our sample are closer to those found in the general population, providing a more generalized insight into the distribution and prevalence of relationship problems as compared with past work. Further, we find several meaningful associations between relationship help-seeking behavior, gender, and attachment. Notably, numerous gender differences in help-seeking and romantic attachment emerged. Our findings suggest that, contrary to more traditional contexts, men are more likely to seek help with their relationships online, are more expressive of their emotions (e.g., discussing the topic of "heartache"), and show language patterns generally consistent with more secure attachment. Our analyses highlight pathways for further exploration, providing even deeper insights into the timing, lifecycle, and moderating factors that influence who, what, why, and how people seek help for their interpersonal relationships.

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Throughout history, scholars and laypeople alike have believed that our words contain subtle clues about what we are like as people, psychologically speaking. However, the ways in which language has been used to infer psychological processes has seen dramatic shifts over time and, with modern computational technologies and digital data sources, we are on the verge of a massive revolution in language analysis research. In this article, we discuss the past and current states of research at the intersection of language analysis and psychology, summarizing the central successes and shortcomings of psychological text analysis to date. We additionally outline and discuss a critical need for language analysis practitioners in the social sciences to expand their view of verbal behavior. Lastly, we discuss the trajectory of interdisciplinary research on language and the challenges of integrating analysis methods across paradigms, recommending promising future directions for the field along the way.

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Individuals with depression are prone to maladaptive patterns of thinking, known as cognitive distortions, whereby they think about themselves, the world, and the future in overly negative and inaccurate ways. These distortions are associated with marked changes in an individual's mood, behavior, and language. We hypothesize that societies can undergo similar changes in their collective psychology that are reflected in historical records of language use. Here, we investigate the prevalence of textual markers of cognitive distortions in over 14 million books for the past 125 y and observe a surge of their prevalence since the 1980s, to levels exceeding those of the Great Depression and both World Wars. This pattern does not seem to be driven by changes in word meaning, publishing and writing standards, or the Google Books sample. Our results suggest a recent societal shift toward language associated with cognitive distortions and internalizing disorders.

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