Collective memory — the persistence of shared representations across a population of observers — has been extensively studied through the lens of collaborative recall, social contagion of memory, and conversational mnemonic influence (Hirst Rajaram Roediger et al. , 2001). These lines of research have established the empirical reality of shared memory formation but have not produced a unified formal account of the threshold dynamics by which individually unstable representations become collectively stable attractors. The present paper proposes such an account within the Kiawe Epistemological Framework (KEF), extending the field-theoretic model of individual memory crystallization to systems of N coupled observer fields. When N observers share torsional coupling through the operator X — the structural consequence of the N→Z oscillation governing the G6–X–Q9–N cycle — their individual fields can undergo collective crystallization into a shared density ρc above a critical threshold λc (N). The resulting stable attractor constitutes a collective memory structure that persists independently of any individual field and resists dissolution proportionally to the number of coupled observers. This formal account provides the mechanistic foundation for the threshold dynamics documented in studies of social belief propagation (Centola et al. , 2018; Granovetter, 1978) and extends the theory of collective learning proposed by Shteynberg et al. (2020) by specifying the formal conditions under which collective attention produces stable shared representations rather than transient alignment. Three canonical instantiations are analyzed: the social construction of reality (Berger & Luckmann, 1966), the Mandela Effect as collective crystallization without factual basis, and restorative stasis as the biological mechanism of individual decoupling. Four verifiable predictions are formulated for threshold dynamics, coupling strength as a function of temporal simultaneity, sleep quality and crystallization susceptibility, and dissolution energy scaling with network age.
Succi et al. (Tue,) studied this question.