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    <abstract>The Picture Story Exercise (PSE) is a projective measure in personality psychology where individuals create narratives based on ambiguous images. Traditionally, the coding of these narratives has been labor-intensive. We introduce the Automated Motive Coder (AMC), which employs recent advances in natural language processing and machine learning to automate the coding of PSE narratives. Trained on an extensive dataset, the AMC demonstrates accuracy comparable to expert coders for both original and translated texts. The model offers support for multiple languages that were absent in prior methods while improving in accuracy and speed. To illustrate its effectiveness, we tested and successfully replicated the established psychological effect of gender difference in the affiliation motive. The AMC can be utilized through established machine learning tools, offering a pragmatic and reliable method for coding across several languages. This tool provides an option to reduce the workload involved in PSE coding, promoting efficiency and consistency in motive assessment.</abstract>
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%0 Conference Proceedings
%T Automatically Coding Implicit Motives in Picture Story Exercises: The Automated Motive Coder
%A Brede, Max
%A Schönbrodt, Felix
%A Hagemeyer, Birk
%A Lerche, Veronika
%Y Velutharambath, Aswathy
%Y Labat, Sofie
%Y Falk, Neele
%Y Plaza-del-Arco, Flor Miriam
%Y Klinger, Roman
%Y Hoste, Véronique
%S Proceedings of the First Workshop on Integrating NLP and Psychology to Study Social Interactions (NLPSI) @ICWSM ‘25
%D 2025
%8 June
%I Association for the Advancement of Artificial Intelligence (www.aaai.org)
%C Copenhagen, Denmark
%F brede-etal-2025-automatically
%X The Picture Story Exercise (PSE) is a projective measure in personality psychology where individuals create narratives based on ambiguous images. Traditionally, the coding of these narratives has been labor-intensive. We introduce the Automated Motive Coder (AMC), which employs recent advances in natural language processing and machine learning to automate the coding of PSE narratives. Trained on an extensive dataset, the AMC demonstrates accuracy comparable to expert coders for both original and translated texts. The model offers support for multiple languages that were absent in prior methods while improving in accuracy and speed. To illustrate its effectiveness, we tested and successfully replicated the established psychological effect of gender difference in the affiliation motive. The AMC can be utilized through established machine learning tools, offering a pragmatic and reliable method for coding across several languages. This tool provides an option to reduce the workload involved in PSE coding, promoting efficiency and consistency in motive assessment.
%R 10.36190/2025.31
%U https://aclanthology.org/2025.nlpsi-1.3/
%U https://doi.org/10.36190/2025.31
%P 28-38

Markdown (Informal)

[Automatically Coding Implicit Motives in Picture Story Exercises: The Automated Motive Coder](https://aclanthology.org/2025.nlpsi-1.3/) (Brede et al., NLPSI 2025)

• Automatically Coding Implicit Motives in Picture Story Exercises: The Automated Motive Coder (Brede et al., NLPSI 2025)

ACL

• Max Brede, Felix Schönbrodt, Birk Hagemeyer, and Veronika Lerche. 2025. Automatically Coding Implicit Motives in Picture Story Exercises: The Automated Motive Coder. In Proceedings of the First Workshop on Integrating NLP and Psychology to Study Social Interactions (NLPSI) @ICWSM ’25, pages 28–38, Copenhagen, Denmark. Association for the Advancement of Artificial Intelligence (www.aaai.org).