A cognitive prosthesis for complex decision-making

While simple heuristics can be ecologically rational and effective in naturalistic decision making contexts, complex situations require analytical decision making strategies, hypothesis-testing and learning. Sub-optimal decision strategies – using simplified as opposed to analytic decision rules – have been reported in domains such as healthcare, military operational planning, and government policy making. We investigate the potential of a computational toolkit called “IMAGE” to improve decision-making by developing structural knowledge and increasing understanding of complex situations. IMAGE is tested within the context of a complex military convoy management task through (a) interactive simulations, and (b) visualization and knowledge representation capabilities. We assess the usefulness of two versions of IMAGE (desktop and immersive) compared to a baseline. Results suggest that the prosthesis helped analysts in making better decisions, but failed to increase their structural knowledge about the situation once the cognitive prosthesis is removed

Determinants of the control of dynamic systems: The role of structural knowledge

In educational and organisational settings it has become common practice to use computer-based complex problems that represent dynamic systems for assessment and training purposes. In the interpretation of performance scores and the design of training programs, it is often assumed that the capacity to effectively control the outcomes of a dynamic system depends on the acquisition of structural knowledge. Control performance scores are generally interpreted as evidence of individual differences in the capacity to acquire and utilise structural knowledge and training programs typically try to improve learners‘ mental models of the system of interest. However, a causal relationship between the acquisition of structural knowledge and successful system control has not been established, and some findings suggest that it may be possible to control dynamic systems in the absence of structural knowledge. Therefore, the goals of this project were to determine the conditions that are required to learn how to control dynamic systems and the psychological processes that separate successful from less successful problem solvers in the performance of this task. The main emphasis of this investigation was to clarify the role of structural knowledge in the control of dynamic systems and to identify sources of individual differences in problem solvers‘ capacity to acquire such knowledge and apply it in a goal-orientated application. In a series of studies, a combined experimental and differential approach was adopted to address these goals. This consisted of the experimental manipulation of the task and structural characteristics of complex problems combined with the use of process indicators and external psychometric tests. Study 1 examined whether problem solvers need to directly interact with a dynamic system in order to acquire structural knowledge that is useful for system control. Study 2 examined whether increments in structural knowledge lead to improvements in control performance and whether dynamic systems can be successfully controlled without structural knowledge. Study 3 examined whether the relationship between structural knowledge and control performance is moderated by system complexity. Each of these studies also investigated the role of fluid intelligence in the acquisition and application of knowledge. Additional methodological contributions include the application of Cognitive Load Theory to the design of the instructions used to manipulate structural knowledge, the use of randomly generated control performance scores to evaluate the success of performance and the development of a theoretically driven operationalisation of system complexity. Across the studies, it was found that structural knowledge was a necessary condition of better than random performance and that there was a causal relationship between structural knowledge and control performance. However, the likelihood that structural knowledge would be acquired and utilised was found to be dependent on the complexity of the system. Small increments in system complexity resulted in floor effects on performance. Fluid intelligence was found to play a crucial role in the acquisition and subsequent application of knowledge. Overall, the results indicate that the complexity of the system determines the amount of knowledge that is acquired by the problem solver, which in turn, combined with their intelligence, determines the quality of their control performance