Time Pressure in Real-Time Dynamic Decision Making

This research investigates the impact of time pressure and individual differences on performance and learning in a Real-Time Dynamic Decision Making (RTDDM) task. Our results indicate that individuals with high spatial WM capacity improved their performance in a high time pressure environment, while individuals with low spatial WM capacity did not. Our results also indicate that individuals with high linguistic WM capacity performed worse than individuals with low linguistic WM capacity. These results suggest that in high time pressure tasks, spatial thinking helps decision makers to build more efficient and effective cognitive models of the environment. We conclude by discussing the implications of these results for training and for the design of computer support in RTDDM tasks. Introduction Real-Time Dynamic Decision Making (RTDDM) tasks have three main characteristics: a) the decision maker has to make a series of interdependent decisions; b) the environment changes because of exogenous events and because of prior decisions; and c) the pacing of decisions is dictated by the task rather than by the decision maker (Brehmer, 1990). This research investigates the impact of time pressure on performance and learning in a RTDDM task. It also studies the impact of linguistic and spatial Working Memory (WM) capacity on performance and learning. Theory WM is the system for holding and manipulating information during the performance of cognitive tasks (Baddeley, 1990). Limitations in WM capacity have been recognized as a major bottleneck in human cognitive processing. We would expect that differences in WM capacity will have a great impact on how individuals perform and learn in RTDDM tasks because these environments impose high cognitive workloads. More specifically, we would expect that individuals with high WM resources should be better able to cope with rapidly changing environments. Also, since WM is used for both performance and learning, we would expect that decision makers would learn better decision strategies when they are first trained in a low time pressure environment, and then they are asked to make decisions in a higher time pressure environment. Conversely, individuals that are trained from the beginning in a high time pressure environment should find it harder to learn better decision strategies because all their cognitive resources are devoted to executing the task, and they have less spare resources devoted to learning. This prediction should be mediated by individual differences in WM capacity. WM is divided into two subsystem: 1) a linguistic sub-system, and 2) a spatial sub-system. In the linguistic sub-system, information is kept in linguistic code, and the processing of this information can be characterized as sequential and propositional. In the spatial sub-system, information is kept in visual code, and the processing can be characterized as more parallel and analogical. There is a strong evidence that language processing and spatial thinking are supported by separate pools of WM capacity (Shah and Miyake, 1996). Prior studies have shown that individuals with high linguistic WM capacity perform better than individuals with low linguistic WM capacity in a variety of real-time tasks such as reading comprehension (Just and Carpenter, 1992) and phone-based interaction (Huguenard, Lerch, Junker, Patz and Kass, 1977). Therefore, we would expect that individuals with high linguistic WM capacity will perform and learn better than individuals with low WM capacity in a RTDDM task. We did not make any predictions about the impact of individual differences in spatial WM capacity since our task is embedded in a scheduling decision making environment that requires the processing of numbers. Number processing is mainly handled by the linguistic sub-system in WM (Shah and Miyake, 1996). But it is possible that decision makers with high spatial WM capacity may decide to build a spatial model of the task, and use this model to make decisions. Since spatial thinking exhibits more parallel processing than linguistic processing, individuals with high spatial WM capacity may be better able to handle the high cognitive demands of RTDDM tasks if they can build a visual model of the task.