Coherence and the merging of relational classes in self-organizing networks: Extending Relational Density Theory
We extended prior work on Relational Density Theory (Belisle & Dixon, 2020a,b) by evaluating the role of pre-experimental coherence among relational classes on the development of merged classes. Distance was modelled geometrically using a multidimensional scaling procedure. Phases 1 and 2 were identical across the participants and Phase 3 differed based on group assignment. In phase 1, we examined the pre-experimental relatedness of 12 arbitrary symbols and 4 known textual words (SALT, PEPPER, KING, QUEEN). Non-coherence was observed between the arbitrary symbols and coherence between the known words (SALT=PEPPER, KING=QUEEN). In phase 2, we established 4, 4-member equivalence classes using a linear training arrangement, where each class included 3 arbitrary symbols and 1 known word. Separation of the classes within the geometric space was observed. In Phase 3, for half of the participants, we attempted to establish a class merger between 2 members of each coherent class (Coherence condition; salt = pepper and king = queen). For the other participants, we attempted to establish a class merger between 2 members of each non-coherent class (Non-coherence condition; king = pepper and queen = salt). Results support the successful merger of the merged coherence class but not the merged non-coherence class. Results have implications for relational self-organization in the establishment of complex combined networks.
Coherence, Multidimensional scaling, Relational density, Stimulus equivalence
Belisle, Jordan, and Michael Clayton. "Coherence and the merging of relational classes in self-organizing networks: Extending Relational Density Theory." Journal of Contextual Behavioral Science 20 (2021): 118-128.
Journal of Contextual Behavioral Science