In this work, two novel methodologies with the ability to address dependence and feedback among criteria and their interrelations are proposed for Cognitive Multi-Criteria Decision Making (CMCDM). Both methods, a modified Fuzzy Digraph approach and an FCM-based approach are intended for application in industrial robot selection. FCMs offer an alternative research direction in decision support that extends their traditional applications. Both methods are capable of handling crisp and fuzzy data; nevertheless, the FCM-based method offers a distinct advantage by enabling the evaluation of different scenarios based on the causal relationships between criteria. Moreover, a modification of Spearman’s rank correlation coefficient is presented, allowing for more meaningful comparisons between MCDM methods. Finally, an experimental analysis evaluates the effectiveness of the proposed methodologies, demonstrating their applicability in the field.