AI-assisted observation of human behavior highlights the paradox of perception: while subjective memory is vague and selective, it functions as the central guide for action. Generalization and parsimony allow for simplified yet effective models of social behavior, with evidence pointing to an optimal informational threshold—the Information Optimum. Beyond this threshold, excess input generates confusion rather

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The rise of digital and virtual environments adds further complexity. Online contexts transcend spatial boundaries and weaken the role of local community structures. Nevertheless, empirical observation suggests that humans remain collective beings, often converging on shared behavioral norms—even in cases where individuals explicitly seek to deviate [9]. Thus, collective imitation and alignment remain fundamental to

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The rise of digital and virtual environments adds further complexity. Online contexts transcend spatial boundaries and weaken the role of local community structures. Nevertheless, empirical observation suggests that humans remain collective beings, often converging on shared behavioral norms—even in cases where individuals explicitly seek to deviate [9]. Thus, collective imitation and alignment remain fundamental to

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From another perspective, the phenomenon can be understood in terms of attention. Human attention is sharp in the moment but narrow in scope [8]. Only a small fraction of stimuli reaches conscious processing and influences attitudes or choices. This implies that both the encoding of memory and the salience of information in decision-making are fundamentally

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The convergence of these findings leads to the formulation of the Information Optimum: there exists a threshold of informational input beyond which additional data no longer clarifies but instead obscures decision-making. This is consistent with theories of bounded rationality and cognitive overload [7]. Thus, more information is not always better; clarity emerges from selecting the

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Complementary findings from EX and CX studies highlight the principle of parsimony in predictive modeling. Models based on 8–12 dominant factors provide the strongest predictive power, while the inclusion of additional variables—often beginning with a thirteenth factor—tends to decrease accuracy by introducing noise [5,6]. This suggests that both perception and prediction benefit from focusing on

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Research consistently shows that humans share a significant portion of their behavioral repertoires, with estimates suggesting an overlap of approximately 85% [3]. This redundancy allows for generalization, reducing the complexity of behavioral modeling. Within this framework, AI-based analyses demonstrate that a set of roughly 80 anchor points is sufficient to explain approximately 85% of variance

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Human perception and memory are characterized by selective attention, incomplete encoding, and rapid decay [1,2]. Nevertheless, these subjective constructions shape individual behavior and intersubjective dynamics. With the advent of AI-based behavioral analysis, it has become possible to model these dynamics at scale, offering new insights into the structure and limits of human social perception. We

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