2026-10-06T11:33:05Z https://api.figshare.com/v2/oai

oai:figshare.com:article/34072131 2026-10-05T17:33:07Z category_173 portal_316 item_type_3 month_year_10_2026

Supplementary file 1_ISIT: a frame-clustering implicit measure of situated experience.docx Aldo Gangemi (2618008) Chiara Lucifora (12002381) Applied Psychology computational grounded theory frame semantics implicit knowledge extraction knowledge graphs situated cognition verbal reports Introduction<p>The Implicit Situation Intensity Test (ISIT) is a method for converting participants’ verbal reports about an experience into a quantitative, per-participant implicit measure of how strongly cognitive, emotional, behavioral, and value-laden frames have been activated by that experience.</p>Method<p>ISIT operates by clustering frames extracted from the verbal report’s extended knowledge graph (XKG)—the structured representation produced by the POLANYI++ neurosymbolic pipeline—into a Frame Ontology (FO) and scoring each frame’s salience for each participant on a normalized scale. The implicit measure produced by ISIT is designed to be combined with explicit measures from standardized questionnaires, implicit measures from physiological sensors, and participant profile information, in a single multivariate analysis of situated experience, producing participant-level scores, suitable for psychometric integration.</p>Results<p>We illustrate the method through the Lucifora et al. study on virtual male embodiment in a catcalling scenario (n = 36), in which a 37-indicator Frame Ontology spanning four domains (emotional states, behavioral responses, violence perception, and semantic markers), whose frame clusters entered the study’s multivariate correlation structure alongside the standardized instruments and emotion ratings; safety-related frames showed differential correlation patterns with anger and sadness versus with fear that simpler instruments do not capture. To place the extraction itself on an auditable footing, every frame the pipeline produced was adjudicated by a tacit-aware judge (DEEPJUDGE) and human-verified: frame-level precision on a warrant criterion was high (0.95–0.96 across two foundation-model backends, with near-zero over-projection), and the two backends produced markedly more similar profiles for the same report than for different ones.</p>Discussion<p>ISIT shares with grounded theory (GT) and computational grounded theory (CGT) the inductive commitment that situational structure should emerge from data, but it differs in three operational respects: it operates on structured extended knowledge graphs (XKGs) rather than raw text; it produces participant-level numerical scores rather than corpus-level qualitative themes; and it integrates by design with other measurement channels rather than standing as a free-standing qualitative analysis. ISIT fills the verbal-channel gap in the cognitive-science measurement ecosystem: it is the implicit-measure complement to autonomic, behavioral, and questionnaire-based instruments, not their replacement. Convergent and divergent validity against standard implicit measures, predictive validity against behavioral outcomes, and multi-modal integration validation are set out as a defined roadmap.</p> 2026-10-05T17:33:07Z Dataset Dataset 10.3389/fpsyg.2026.1883850.s001 https://figshare.com/articles/dataset/Supplementary_file_1_ISIT_a_frame-clustering_implicit_measure_of_situated_experience_docx/34072131 CC BY 4.0