The design of joint cognitive systems: the effect of cognitive coupling on performance

Abstract

In recent years, there has been a growing interdisciplinary interest in designing intelligent human-computer systems for problem-solving. Although progress has been made, we are far from building intelligent human-computer systems that fully exploit the natural synergies of the combination of human and intelligent machine. One of the significant paradigms of intelligent decision support is the cognitive systems engineering approach. This approach considers the human and the intelligent machine as components of a joint cognitive system and focuses on the need to maximize the overall performance of the joint system. Factors influencing the performance of the joint cognitive system include the cognitive characteristics of the human, the computer system, and the task. An important relationship between the cognitive characteristics of the human and those of the system is cognitive coupling, which has a number of dimensions.The study described in this paper explores the style dimension of cognitive coupling by presenting a laboratory experiment that examines the interactions among human cognitive style (analytic vs. heuristic), problem type (analysis-inducing vs. heuristic-inducing), and nature of decision aid (analytic vs. heuristic). The study demonstrates that, depending on the characteristics of the human, the computerized aid, and the problem to be solved, the joint human-computer system performance can be better or worse than the performance of the individual human or computer system working alone. Furthermore, the results suggest that the impact of cognitive style on decision-making performance may depend upon the characteristics of the problem, the nature of the decision-aid, and the measures used to evaluate performance. Inadequate recognition of these factors and their interactions may have led to conflicting results in prior decision-making studies using cognitive style.