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Related Concept Videos

Jung's Analytical Theory01:23

Jung's Analytical Theory

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Carl Jung, a Swiss psychiatrist and former follower of Freud, eventually broke away from Freud's ideas to create his framework, analytical psychology. This approach emphasizes achieving a balance between the conscious and unconscious aspects of the mind and reconciling various experiences within an individual's personality. Jung believed that this process, which typically unfolds in the latter part of life, involves an ongoing journey of recognizing and incorporating unconscious...
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Can an Algorithm Tell How Spiritual You Are? Using Generative Pretrained Transformers for Sophisticated Forms of Text

Michael Prinzing1, Elizabeth Bounds1, Karen Melton2

  • 1Department of Psychology & Neuroscience, Baylor University, Waco, Texas, USA.

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|December 12, 2024
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Summary
This summary is machine-generated.

Automated text analysis using Generative Pre-trained Transformers (GPTs) matches human raters for psychological assessment. GPTs provide a faster, more efficient method for analyzing nuanced constructs like spirituality.

Keywords:
artificial intelligenceautomated text analysiscontent analysiscontent codinglarge language models

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Area of Science:

  • Psychological Assessment
  • Computational Linguistics
  • Artificial Intelligence

Background:

  • Text analysis converts qualitative data into quantitative measures for psychological assessment.
  • Nuanced constructs like spirituality present unique challenges for automated analysis.
  • Generative Pre-trained Transformers (GPTs) are advanced AI models for natural language processing.

Purpose of the Study:

  • To evaluate if Generative Pre-trained Transformers (GPTs) can achieve the same accuracy as manual text analysis in psychological assessments.
  • To determine the efficacy of GPTs in analyzing complex and nuanced psychological constructs, specifically spirituality.
  • To compare the performance of GPTs against the established 'gold standard' of human raters in text-based psychological evaluation.

Main Methods:

  • Two studies were conducted involving US undergraduates and community adults who provided written texts about their goals and completed self-report measures.
  • Texts were analyzed for spirituality using both trained human research assistants and GPTs.
  • Data included self-reports on spirituality, religiousness, mental health, daily diaries, and behavioral measures.

Main Results:

  • GPTs demonstrated performance comparable to human raters in coding texts for spirituality.
  • Scores generated by GPTs and human raters showed high consistency.
  • Both human- and GPT-generated scores exhibited equivalent associations with other measures of spirituality and related constructs.

Conclusions:

  • Generative Pre-trained Transformers (GPTs) are capable of matching the accuracy of human raters in sophisticated text analysis tasks.
  • GPTs offer a significantly more time- and labor-efficient approach to psychological text analysis.
  • The findings support the use of GPTs as a reliable tool for quantitative analysis of qualitative data in psychological research.