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

Area of Science:

• Cognitive psychology
• Human-computer interaction
• Decision science

Background:

• Evidence accumulation models quantify decision-making processes like evidence strength and caution.
• Standard models are typically applied to simple, rapid choices.
• Existing elaborations for complex scenarios are often difficult to implement in applied settings.

Purpose of the Study:

• To evaluate the applicability of two standard evidence accumulation models (diffusion and linear ballistic accumulation) to a complex, applied decision-making task.
• To assess model performance with realistic task features, including long response times, complex rules, multitasking, and time pressure.

Main Methods:

• Applied the diffusion model and the linear ballistic accumulation (LBA) model to data from a simulated unmanned aerial vehicle (UAV) operator task.
• The task involved detecting heterogeneous multiattribute targets under conditions of decision uncertainty and workload manipulations.

Main Results:

• Both the diffusion and LBA models descriptively fit the data well, despite the task's complexity and duration (over 2 seconds per response).
• The models successfully explained the psychological impact of decision uncertainty and workload variations.
• The models provided a coherent psychological account of operator performance in a realistic applied setting.

Conclusions:

• Standard evidence accumulation models are applicable and effective in complex, applied decision-making environments, such as UAV operation.
• These models can provide meaningful psychological insights even when dealing with realistic task complexities.
• The findings support the broader use of these established cognitive models in applied research and practice.