Emergence of Language Related to Self-experience and Agency in Autobiographical Narratives of Individuals With Schizophrenia

BACKGROUND AND HYPOTHESIS

Disturbances in self-experience are a central feature of schizophrenia and its study can enhance phenomenological understanding and inform mechanisms underlying clinical symptoms. Self-experience involves the sense of self-presence, of being the subject of one's own experiences and agent of one's own actions, and of being distinct from others. Self-experience is traditionally assessed by manual rating of interviews; however, natural language processing (NLP) offers automated approach that can augment manual ratings by rapid and reliable analysis of text.

STUDY DESIGN

We elicited autobiographical narratives from 167 patients with schizophrenia or schizoaffective disorder (SZ) and 90 healthy controls (HC), amounting to 490 000 words and 26 000 sentences. We used NLP techniques to examine transcripts for language related to self-experience, machine learning to validate group differences in language, and canonical correlation analysis to examine the relationship between language and symptoms.

STUDY RESULTS

Topics related to self-experience and agency emerged as significantly more expressed in SZ than HC (P < 10-13) and were decoupled from similarly emerging features such as emotional tone, semantic coherence, and concepts related to burden. Further validation on hold-out data showed that a classifier trained on these features achieved patient-control discrimination with AUC = 0.80 (P < 10-5). Canonical correlation analysis revealed significant relationships between self-experience and agency language features and clinical symptoms.

CONCLUSIONS

Notably, the self-experience and agency topics emerged without any explicit probing by the interviewer and can be algorithmically detected even though they involve higher-order metacognitive processes. These findings illustrate the utility of NLP methods to examine phenomenological aspects of schizophrenia.

Changes in Intimacy and Sexuality During the COVID-19 Pandemic: A Qualitative Analysis of Data from a Survey on Partnered Individuals in Eight European Countries

This qualitative study explores experiences of intimacy and sexuality during the first phase of the COVID-19 pandemic of 3357 participants from Croatia, the Czech Republic, the Netherlands, France, Germany, Portugal, Sweden, and Turkey. Data were collected through open-ended questions within a survey on sexuality during the pandemic. Based on a reflexive thematic analysis three themes occurred. The first theme No change was described by 41% when summarizing their sex life during the pandemic. The second theme Positive change was experienced by 25%, and the third theme Negative change by 21%. An overarching theme then emerged as: "Still the same trajectories, but the pandemic could be a catalyst for improved or worsened sex- life." For those intimate partnered individuals who already had problems with intimacy and sexuality before the COVID-19 pandemic it seemed to remain the same or deteriorated throughout the pandemic. For those with positive experiences of intimacy and sexuality before the COVID-19 pandemic it remained the same or improved throughout the pandemic. These findings are relevant for researchers and clinicians in developing preventive and supportive interventions in the context of crisis and social isolation.

Searching for Prosociality in Qualitative Data: Comparing Manual, Closed–Vocabulary, and Open–Vocabulary Methods

Although most people present themselves as possessing prosocial traits, people differ in the extent to which they actually act prosocially in everyday life. Qualitative data that were not ostensibly collected to measure prosociality might contain information about prosocial dispositions that is not distorted by self–presentation concerns. This paper seeks to characterise charitable donors from qualitative data. We compared a manual approach of extracting predictors from participants’ self–described personal strivings to two automated approaches: A summation of words predefined as prosocial and a support vector machine classifier. Although variables extracted by the support vector machine predicted donation behaviour well in the training sample ( N = 984), virtually, no variables from any method significantly predicted donations in a holdout sample ( N = 496). Raters’ attempts to predict donations to charity based on reading participants’ personal strivings were also unsuccessful. However, raters’ predictions were associated with past charitable involvement. In sum, predictors derived from personal strivings did not robustly explain variation in charitable behaviour, but personal strivings may nevertheless contain some information about trait prosociality. The sparseness of personal strivings data, rather than the irrelevance of open–ended text or individual differences in goal pursuit, likely explains their limited value in predicting prosocial behaviour. © 2020 European Association of Personality Psychology