Patterns representation in generative cognitive architecture

For exploring sequences of eye movements simulation and decoding approaches can be used, which intersect in cognitive architecture domain. The current research aims to create architectures for generating the internal representation of chaotic data learning. EyeLink 1000 Plus was used; 17 subjects participated in the research (mean age = 21 years, 3 males). The experiment includes three trials: the first is dimensionality reduction, the second is clustering, and the third is a classification problem. Three distinct cognitive styles were identified in addressing the proposed challenges. The proposed association and batch models enable the identification of extreme patterns, which can provide insights into the characteristics of the data. The foundational generative learning algorithm determines the appropriate timing and location for generating new nodes, selects a pattern X, and identifies a subpattern to be encoded by the newly created node, among other decisions. Findings are consistent with the anatomical perspective.