Automatically Coding Implicit Motives in Picture Story Exercises: The Automated Motive Coder

Researchers Max Brede, Felix Schönbrodt, Birk Hagemeyer, and Veronika Lerche introduced the Automated Motive Coder (AMC), a machine learning tool that automates coding of Picture Story Exercise (PSE) narratives with accuracy comparable to expert coders for original and translated texts. Presented at the First Workshop on Integrating NLP and Psychology to Study Social Interactions (NLPSI) @ICWSM '25 in Copenhagen, Denmark, the AMC supports multiple languages, improves speed, and successfully replicated the gender difference in the affiliation motive. The tool reduces workload and promotes efficiency in motive assessment.

Automatically Coding Implicit Motives in Picture Story Exercises: The Automated Motive Coder https://aclanthology.org/2025.nlpsi-1.3.pdf Max Brede /people/max-brede/ , Felix Schönbrodt /people/felix-schonbrodt/ , Birk Hagemeyer /people/birk-hagemeyer/unverified/ , Veronika Lerche /people/veronika-lerche/unverified/ 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.- Anthology ID: - 2025.nlpsi-1.3 - Volume: Proceedings of the First Workshop on Integrating NLP and Psychology to Study Social Interactions NLPSI @ICWSM ’25 /volumes/2025.nlpsi-1/ - Month: - June - Year: - 2025 - Address: - Copenhagen, Denmark - Editors: Aswathy Velutharambath /people/aswathy-velutharambath/unverified/ , Sofie Labat /people/sofie-labat/ , Neele Falk /people/neele-falk/ , Flor Miriam Plaza-del-Arco /people/flor-miriam-plaza-del-arco/ , Roman Klinger /people/roman-klinger/ , Véronique Hoste /people/veronique-hoste/unverified/ - Venues: NLPSI /venues/nlpsi/ | WS /venues/ws/ - SIG: - Publisher: - Association for the Advancement of Artificial Intelligence www.aaai.org - Note: - Pages: - 28–38 - Language: - URL: https://aclanthology.org/2025.nlpsi-1.3/ https://aclanthology.org/2025.nlpsi-1.3/ - DOI: 10.36190/2025.31 https://doi.org/10.36190/2025.31 - Cite ACL : - Max Brede, Felix Schönbrodt, Birk Hagemeyer, and Veronika Lerche. 2025. Automatically Coding Implicit Motives in Picture Story Exercises: The Automated Motive Coder https://aclanthology.org/2025.nlpsi-1.3/ . 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 . - Cite 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 - PDF: https://aclanthology.org/2025.nlpsi-1.3.pdf https://aclanthology.org/2025.nlpsi-1.3.pdf