Psychometric Models in Higher Dimensions: How Artificial Intelligence Can Expand the Space of Measurement

• DOI: 10.31234/osf.io/pe67q_v1
• Publication Date: 2025-03-30T07:27:11Z
• Authors: Agnieszka Szymańska

Some psychological models—especially complex, typological ones or those with a three-dimensional structure—cannot be properly verified within the classical 2D measurement space. Their projection onto plane geometrically distorts actual relationships between variables persons, leading to false simplifications and erroneous interpretations. In response this limitation, article proposes an expansion of space using tools from artificial intelligence—specifically, Support Vector Machines (SVM) Radial Basis Function (RBF) kernel—which enable transformation data into higher-dimensional spaces.The shifts focus individuals, treated as vectors traits. It demonstrates that kernel RBF not only classifies but transforms very in which operate—creating new geometric framework verification complex deep models becomes possible. logic, individuals become carriers model rather than mere observations. The concept culminates proposal for psychometric scale based on independent vectors, compatible structure Altogether, constitutes breakthrough shift: variable person space, projections structural depth models.