Coordination studies reveal that groups can achieve performance exceeding the sum of individual contributions (Bahrami et al., 2010). Further evidence suggests that weak coupling maximizes the benefits of coordinated problem-solving (Abney et al., 2015; Schloesser et al., 2021). This work develops a computational framework to study coordination in coupled systems. We trained two echo state networks (ESNs) to classify cepstrum-coded speech signals from nine native Japanese speakers (Kudo et al., 1999). Coupling ESN feedback during testing reveals a nonlinear relationship between joint performance and coupling: moderate coupling (feedback integrates readout states from both networks) enhances performance, whereas full coupling (feedback is swapped between networks) returns performance to that of independent networks. These results suggest that while interaction between networks can enhance performance, excessive integration may diminish the benefits of independent contributions (cf. Fusaroli et al., 2012). Our model provides a novel, formal framework for explaining interaction dynamics in collective intelligences.