Effective adaptation in multi-agent systems relies on information exchange, which can follow simple (pairwise) or complex (reinforced social influence) contagion dynamics. So far, complex contagion has been mainly studied in nonlinear threshold-based binary decision models, valued for their simplicity and empirical relevance. However, many real-world collective processes lack thresholds and instead involve continuous variables. In this talk, we generalize the notion of complex contagion to consensus-based decision-making. We show that transitions from simple to complex contagion also arise in this broader class of dynamics. Using network science concepts, we introduce a new characterization of complex contagion and demonstrate its presence in consensus systems. These findings extend complex contagion beyond threshold models and reveal that the nature of contagion is intrinsically linked to the type and timescale of the spreading behavior, from slow perturbations to rapid collective responses.