Iowa Gambling Task Modified for Military Domain

Abstract

One key component of optimal military decision making is that the decision maker demonstrates reinforcement learning. The modification of psychological tasks gives insight into understanding how to effectively train military decision makers and how experienced decision makers arrive at optimal or near optimal decisions. We developed a task modeled after the Iowa Gambling Task (IGT) to measure military decision making performance. This new task focuses on high stakes and uncertain environments particular to military decision making conditions. Thirty-four U.S. military officers from all branches of service completed the tasks yielding decision data for validation. The new task retains essential characteristics of the foundational task and gives insight into reinforcement learning of military decision makers. Results indicate that the additional metric of regret defines higher performance at a trial-by-trial level, and clustering by multiple metrics defines high performance groups.

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