Natural language analyzed with AI-based transformers predict traditional subjective well-being measures approaching the theoretical upper limits in accuracy

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...

Full description

Saved in:
Bibliographic Details
Published in:Scientific reports 2022-03, Vol.12 (1), p.3918-9, Article 3918
Main Authors: Kjell, Oscar N. E., Sikström, Sverker, Kjell, Katarina, Schwartz, H. Andrew
Format: Article
Language:English
Subjects:
631/477
631/477/2811
Accuracy
Annan samhällsvetenskap
Artificial Intelligence
Communication
Humanities and Social Sciences
Humans
Language
multidisciplinary
Natural language
Other Social Sciences
Psychological assessment
Questionnaires
Reproducibility of Results
Samhällsvetenskap
Science
Science (multidisciplinary)
Social Sciences
Social Sciences Interdisciplinary
Surveys and Questionnaires
Tvärvetenskapliga studier inom samhällsvetenskap
Well being
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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  
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-022-07520-w