This project bridges gaps between decision making, prospective memory, and applied cognition by presenting a comprehensive computational modeling framework to quantitatively measure latent cognitive control and attentional processes involved in performing a complex applied task with dynamic time pressure and memory demands. The empirical chapters find robust resource availability and reallocation effects associated with PM demand and time pressure using a cognitively demanding applied task, illustrating the utility of studying PM in representative environments. In such environments, PM load significantly affects the attentional system, whereas it does not in the simple laboratory tasks previously studied. This project extends previous work by modelling distinct attentional gain and focus mechanisms to give a finer picture of how the attentional system supports decision making depending on the reward and demand structure of the environment. This approach could be used to inform improvements in safety and efficiency in a range of applied settings.
|Qualification||Doctor of Philosophy|
|Award date||16 Aug 2019|
|Publication status||Unpublished - 2019|