Disciplinary barriers need communication: a behavioral and fNIRS study under group decision-making paradigm shift based on cabin design

In the field of interdisciplinary engineering design, the terminology used by decision-makers from different disciplinary backgrounds often exhibits significant disciplinary heterogeneity, resulting in misunderstandings or communication barriers for decision-making teams. Due to the ambiguity of cognitive structures, the impact of interdisciplinary knowledge on decision-making quality and cognitive load was poorly answered. This study, grounded in utility theory and multi-criteria decision theory, introduced an enhanced multi-attribute decision-making task (MADM-LGD) to research the behavioral characteristics of decision-making groups and the cognitive shifts that occur during interdisciplinary decision-making paradigm transitions. An experiment utilizing Functional Near-Infrared Spectroscopy (fNIRS) was conducted based on a ship cabin design task, aiming to explore the neural mechanisms underlying interdisciplinary group decision-making. The analysis of experiment revealed several key findings: (1) Prior cognitive level does not significantly affect decision quality during the individual decision-making phase, but it positively influences decision quality during the group decision-making phase. (2) Interdisciplinary communication ability positively impacts decision quality. Hence, teams which exhibit stronger interdisciplinary communication achieve superior decision performance; (3) The task-oriented phase imposes a higher cognitive load compared to the non-task-oriented phase, while interdisciplinary communication helps alleviate this cognitive load, reducing the cognitive pressures associated with heterogeneous engineering semantics, promoting mutual understanding across disciplines, and ultimately enhancing decision quality. This study offers valuable guidance for advancing the empirical theories and practices of interdisciplinary group decision-making in artificial intelligence (AI) and human intelligence (HI).