Collective Attention Beyond Institutions: A Precision-Based Account of Digital Inference

Contemporary discussions of collective attention in digital environments often presuppose its existence without specifying the conditions under which it emerges, stabilizes, or fragments. This paper challenges that assumption by reconceptualizing collective attention not as a shared mental focus or an institutionally coordinated practice, but as an emergent socio-technical phenomenon grounded in the regulation of inference under uncertainty. Drawing on predictive processing and the Nyāya epistemological concept of saṃśaya (productive doubt), attention is defined as a structuring effort that governs how information from the environment is received so as to support action-guiding inference. On this basis, the paper develops a precision-based framework that distinguishes between two ideal-typical regimes of collective attention. Organic attention arises when inferential precision remains responsive to uncertainty, enabling sustained collective engagement without requiring shared intentions or virtue-based coordination. Mechanistic attention, by contrast, emerges when precision is decoupled from inferential difficulty and redirected by engagement-optimizing architectures, resulting in attention fragmentation despite high activity. This phenomenon is conceptualized as precision hijacking, a structural pathology of algorithmically amplified platforms. The framework is operationalized through two system-level indicators—Collective Free Energy (CFE) and Precision Alignment Index (PAI)—which capture unresolved inferential load and the coherence of inferential persistence across participants. Applying these measures to temporal interaction data from Wikipedia talk pages demonstrates that collective attention can stabilize in minimally institutionalized digital ecologies, exhibiting bounded inferential effort and structured alignment. These findings challenge institutional and virtue-based accounts of collective attention by showing that institutionalization enhances stability but is not a necessary condition for collective attentional emergence. By reframing attention as a precision-regulated socio-technical achievement, the paper contributes a diagnostic framework for analyzing digital attention economies, clarifies the philosophical stakes of algorithmic amplification, and offers new resources for the governance of collective sense-making in digital societies.