Evidence accumulation in a complex task: Making choices about concurrent multiattribute stimuli under time pressure.

Abstract

Evidence accumulation models transform observed choices and associated response times into psychologically meaningful constructs such as the strength of evidence and the degree of caution. Standard versions of these models were developed for rapid (∼1 s) choices about simple stimuli, and have recently been elaborated to some degree to address more complex stimuli and response methods. However, these elaborations can be difficult to use with designs and measurements typically encountered in complex applied settings. We test the applicability of 2 standard accumulation models-the diffusion (Ratcliff & McKoon, 2008) and the linear ballistic accumulation (LBA) (Brown & Heathcote, 2008)-to data from a task representative of many applied situations: the detection of heterogeneous multiattribute targets in a simulated unmanned aerial vehicle (UAV) operator task. Despite responses taking more than 2 s and complications added by realistic features, such as a complex target classification rule, interruptions from a simultaneous UAV navigation task, and time pressured choices about several concurrently present potential targets, these models performed well descriptively. They also provided a coherent psychological explanation of the effects of decision uncertainty and workload manipulations. Our results support the wider application of standard evidence accumulation models to applied decision-making settings.