ArticleScientific reports2022
Natural language analyzed with AI-based transformers predict traditional subjective well-being measures approaching the theoretical upper limits in accuracy.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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36 citing papers in PubMed.
- Assessing the Need for Mental Health Support From Free-Text Responses: Development and Validation of Language-Based Assessments in Adults With Internalizing Symptoms.JMIR mental health · 2026Article
- Natural Language Response Formats for Assessing Depression and Worry With Large Language Models: A Sequential Evaluation With Model Pre-Registration.Assessment · 2026Article
- Estimating Longitudinal Changes in Workday Happiness From Daily Report Texts Among Employees: Retrospective Feasibility Study.JMIR formative research · 2026Article
- Deep Semantics Analysis for Scale Development: Replicating Psychometrics Validation Process with Large Language Models.Behavioral sciences (Basel, Switzerland) · 2026Article
- Explaining GPTs' schema of depression: A machine behavior analysis.Journal of psychopathology and clinical science · 2026Article
- Measuring complex constructs in large-scale text with computational social mixed methods.Behavior research methods · 2026Article
- Replicability and Validity of a New Artificial-Intelligence Assessment of Posttraumatic Stress Disorder From Patient Language: A Sequential Evaluation With Model Preregistration.Clinical psychological science : a journal of the Association for Psychological Science · 2026Article
- Language-based assessments can predict psychological and subjective well-being.Communications psychology · 2026Article
- Human Expertise and Large Language Model Embeddings in the Content Validity Assessment of Personality Tests.Educational and psychological measurement · 2026Article
- Evaluating the Efficacy of AI-Based Interactive Assessments Using Large Language Models for Depression Screening: Development and Usability Study.JMIR formative research · 2026Article
- Language-based personality assessment from life narratives: a focus on model interpretability and efficiency.Frontiers in artificial intelligence · 2026Article
- Can an Algorithm Tell How Spiritual You Are? Using Generative Pretrained Transformers for Sophisticated Forms of Text Analysis.Journal of personality · 2025Article
- Large language model performance versus human expert ratings in automated suicide risk assessment.Scientific reports · 2025Article
- Decoding the mind of self-compassion through a topic modeling analysis of 9000+ free-text narratives.Scientific reports · 2025Article
- Advancing psychological assessment: quantifying self-compassion through free-text responses and language model BERT.Scientific reports · 2025Article
- Systematic Evaluation of Auto-Encoding and Large Language Model Representations for Capturing Author States and Traits.Findings of ACL. ACL · 2025Article
- Automatic implicit motive codings are at least as accurate as humans' and 99% faster.Journal of personality and social psychology · 2025Article
- Talk to your data: Introducing text embedding similarity analysis (TESA) in psychological research.Behavior research methods · 2025Article
- Towards a latent space cartography of subjective experience in mental health.Psychiatry and clinical neurosciences · 2025Article
- Exploring Biases of Large Language Models in the Field of Mental Health: Comparative Questionnaire Study of the Effect of Gender and Sexual Orientation in Anorexia Nervosa and Bulimia Nervosa Case Vignettes.JMIR mental health · 2025Article
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Abstract
We show that using a recent break-through in artificial intelligence -transformers-, psychological assessments from text-responses can approach theoretical upper limits in accuracy, converging with standard psychological rating scales. Text-responses use people's primary form of communication -natural language- and have been suggested as a more ecologically-valid response format than closed-ended rating scales that dominate social science. However, previous language analysis techniques left a gap between how accurately they converged with standard rating scales and how well ratings scales converge with themselves - a theoretical upper-limit in accuracy. Most recently, AI-based language analysis has gone through a transformation as nearly all of its applications, from Web search to personalized assistants (e.g., Alexa and Siri), have shown unprecedented improvement by using transformers. We evaluate transformers for estimating psychological well-being from questionnaire text- and descriptive word-responses, and find accuracies converging with rating scales that approach the theoretical upper limits (Pearson r = 0.85, p < 0.001, N = 608; in line with most metrics of rating scale reliability). These findings suggest an avenue for modernizing the ubiquitous questionnaire and ultimately opening doors to a greater understanding of the human condition.
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