Merge – a machine learning approach to artistic research
2020–
As part of my doctoral thesis (Okulov, 2024), I developed a research approach in interdisciplinary collaboration with Asutosh Hota and Yu Tian that merges quantitative methods with artistic research. The appraoch includes a digital platform where multimodal stimuli can be annotated nonverbally. Digital drawing is used to represent how individuals attend to and interpret the stimuli. From the pen expression data, motor expressions are analyzed using machine learning to understand how they reflect affective responses (see Sievers et al., 2019).
This method will be applied in interdisciplinary contexts of psychological research and social sciences to study how individuals and larger groups perceive and resolve conflicted situations to achieve transformative experiences. In this context, the method will be referred to as Nonverbal Stimulated Recall Interviews (nSRI), linking it to the SRI method developed for psychotherapy research (Kykyri et al., 2023).
References
Kykyri, V. L., Wahlström, J., & Seikkula, J. (2023). Inner and outer dialogue in couple therapy: the potential of stimulated recall interviews. In The Routledge International Handbook of Innovative Qualitative Psychological Research (pp. 229-242). Routledge.
Okulov, J. (2024). Quantifying Qualia – Aesthetic Machine Attention in Resisting the Objectifying Tendency of Thought. [Doctoral Thesis, Aalto University]. Aalto University. http://urn.fi/URN:ISBN:978-952-64-1746-2
Sievers, B., Lee, C., Haslett, W. & Wheatley, T. (2019). A multi-sensory code for emotional arousal. Proceedings of the Royal Society B, 286(1906), 20190513.