Assessing Intuitive Decision Making with Cognitive Neuroscience-based Methods
(Room S320F)
27 Nov 18
4:30 PM
-
5:00 PM
Many military jobs pose complex perceptual-cognitive challenges, such as detecting potential collisions among vehicles that are operating in close physical proximity. These situations require rapid and intuitive decision making. Unlike deliberate decision making, intuitive “hunches” are largely automatic and do not require working memory (Evans, 2003; Evans & Stanovich, 2013; Kahneman, 2011). As a result, cognitive neuroscience-based methods such as Diffusion Modeling (DM; Wagenmakers, 2009) and electroencephalography (EEG) are well-suited to the study of intuitive decision making. Previous research by Lucia and colleagues (2017) provided support for a generalized neurological “signature” of intuitive decision making (Luu et al, 2010). This signature held for two different decision tasks, both of which used briefly-presented static imagery as the decision stimulus. In the current study, we examine the generalizability of this neurological signature with a new sample of 22 submariners who performed a similar decision task, but which used brief motion pictures as the decision stimulus. We also analyze the response time (RT) and accuracy data from both studies using DM. Unlike traditional methods for analyzing RT and decision accuracy, which treat them as separate dependent variables (Luce, 1986), DM combines them to model the underlying cognitive processes: processing speed (Delta), response caution (Alpha), stimulus encoding (Tau), and response bias (Beta). While some of the findings generalized across the two studies, others did not. Differences may be due to the type of decision stimuli used: static imagery vs. motion pictures. In some cases, however, the EEG results were able to reliably differentiate experts from novices even when the behaviorally-based measures did not. The paper concludes with practitioner-oriented guidelines for using EEG and DM methods to study intuitive decision making.