Towards a dynamic model of collective intelligence: Theoretical integration, nonverbal interaction and temporality

Most existing research on Collective Intelligence (CI) tends to emphasize final performance indicators or sums of individual cognitive traits, giving insufficient attention to how teams dynamically construct their collective capacity through ongoing interactions. In this paper, we propose an integrative perspective that draws on multiple existing approaches, ranging from conceptual frameworks (IMOI, TSM-CI) to measurement-oriented constructs (C-factor, synergy) and team cognition, while underscoring the essential behavioral
and temporal processes that drive collective intelligence. Rather than viewing collective intelligence as a static end-point, our model explores how nonverbal cues (e.g., posture, gaze, interpersonal distance), repeated feedback loops, and macro-level temporal dynamics converge to shape a team’s coordination, cohesion, and shared use of knowledge. By highlighting the continuous, real-time mechanisms through which teams adapt, self-regulate, and refine their performance, we address the need for a more process-oriented, longitudinal perspective. We also propose practical methodological pathways to more accurately capture how teams evolve from loosely connected individuals into high-performing collectives over time.