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

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.

A sequential sampling account of response bias and speed-accuracy tradeoffs in a conflict detection task.

Signal Detection Theory (SDT; Green & Swets, 1966) is a popular tool for understanding decision making. However, it does not account for the time taken to make a decision, nor why response bias might change over time. Sequential sampling models provide a way of accounting for speed-accuracy trade-offs and response bias shifts. In this study, we test the validity of a sequential sampling model of conflict detection in a simulated air traffic control task by assessing whether two of its key parameters respond to experimental manipulations in a theoretically consistent way. Through experimental instructions, we manipulated participants' response bias and the relative speed or accuracy of their responses. The sequential sampling model was able to replicate the trends in the conflict responses as well as response time across all conditions. Consistent with our predictions, manipulating response bias was associated primarily with changes in the model's Criterion parameter, whereas manipulating speed-accuracy instructions was associated with changes in the Threshold parameter. The success of the model in replicating the human data suggests we can use the parameters of the model to gain an insight into the underlying response bias and speed-accuracy preferences common to dynamic decision-making tasks.

Relative position vectors: an alternative approach to conflict detection in air traffic control.

OBJECTIVE: We explore whether the visual presentation of relative position vectors (RPVs) improves conflict detection in conditions representing some aspects of future airspace concepts. BACKGROUND: To help air traffic controllers manage increasing traffic, new tools and systems can automate more cognitively demanding processes, such as conflict detection. However, some studies reveal adverse effects of such tools, such as reduced situation awareness and increased workload. New displays are needed that help air traffic controllers handle increasing traffic loads. METHOD: A new display tool based on the display of RPVs, the Multi-Conflict Display (MCD), is evaluated in a series of simulated conflict detection tasks. The conflict detection performance of air traffic controllers with the MCD plus a conventional plan-view radar display is compared with their performance with a conventional plan-view radar display alone. RESULTS: Performance with the MCD plus radar was better than with radar alone in complex scenarios requiring controllers to find all actual or potential conflicts, especially when the number of aircraft on the screen was large. However performance with radar alone was better for static scenarios in which conflicts for a target aircraft, or target pair of aircraft, were the focus. CONCLUSION: Complementing the conventional plan-view display with an RPV display may help controllers detect conflicts more accurately with extremely high aircraft counts. APPLICATIONS: We provide an initial proof of concept that RPVs may be useful for supporting conflict detection in situations that are partially representative of conditions in which controllers will be working in the future.

Adaptive decision making in a dynamic environment: a test of a sequential sampling model of relative judgment.

Research has identified a wide range of factors that influence performance in relative judgment tasks. However, the findings from this research have been inconsistent. Studies have varied with respect to the identification of causal variables and the perceptual and decision-making mechanisms underlying performance. Drawing on the ecological rationality approach, we present a theory of the judgment and decision-making processes involved in a relative judgment task that explains how people judge a stimulus and adapt their decision process to accommodate their own uncertainty associated with those judgments. Undergraduate participants performed a simulated air traffic control conflict detection task. Across two experiments, we systematically manipulated variables known to affect performance. In the first experiment, we manipulated the relative distances of aircraft to a common destination while holding aircraft speeds constant. In a follow-up experiment, we introduced a direct manipulation of relative speed. We then fit a sequential sampling model to the data, and used the best fitting parameters to infer the decision-making processes responsible for performance. Findings were consistent with the theory that people adapt to their own uncertainty by adjusting their criterion and the amount of time they take to collect evidence in order to make a more accurate decision. From a practical perspective, the paper demonstrates that one can use a sequential sampling model to understand performance in a dynamic environment, allowing one to make sense of and interpret complex patterns of empirical findings that would otherwise be difficult to interpret using standard statistical analyses.