Method for Synthesizing Intellectualized Platforms of Cross-Referral Transition Between Cognitive Basis Systems for HCI Objects Perception Subjectivization

This study develops a specialized method for the synthesis of intelligent platforms for cross-referral transition between cognitive basis systems (CBSs) in the field of HCI object perception subjectivization, within the context of global scientific and applied efforts to increase the intelligence level of interaction between humans and software and/or hardware products (SHPs). The proposed method comprises a conceptual model, a mathematical model, and a specialized algorithm. Practical implementation was conducted using R (within an appropriate IDE) and Python. The method was approbated by solving a relevant applied problem: synthesizing a cross-referral transition platform between a specialized CBS for HCI object perception subjectivization and an existing personality classification system based on 16 Jungian sociotypes. The results indicated a 29.5% baseline correspondence rate for pairs of dominant perception impact factors regarding their exclusive alignment with specific Jungian sociotypes. Furthermore, the accuracy rate of the built-in multilayer perceptron (MLP) artificial neural network (ANN) for dominant factor pairs reached approximately 83.3% ± 2.95%, while introducing a third dominant factor raised the accuracy threshold to a potential 96.7% within the evaluated scenario. In addition, the paper outlines prospects for further research into the potential of the proposed method for identifying and documenting cross-referral relationships between fundamental constituents of various conventional and alternative CBSs.