Objective: We investigated whether naturalistic, intuitive (pattern recognition–based) decision making can be developed via implicit statistical learning in a simulated real-world environment. Background: To our knowledge, no definitive studies have actually shown that implicit learning plays a causal role in the development of intuitive decision making when the latter is defined as pattern recognition of real-world, or simulated real-world, environmental situations. Method: The simulated environment was presented dynamically so as to induce a sense of simulated locomotion through the scene and over sequences of objects on the ground. During training, participants passively viewed the objects sequences; during test, participants made intuitive decisions about related or unrelated sequences. Results: Intuitive decision making can be developed via implicit learning. Articulatory suppression, which affects working memory, exerted a significant inhibitory effect on the training of intuitive decision making. Intuitive decision making trained in the simulated environment fully transferred to a flat display (but not vice versa). Conclusion: Intuitive decision making is developed by an implicit learning process that is engaged by the meaning inherent in naturalistic scenes. Application: Implicit learning can be used for training intuitive decision making. Keywords: intuitive decision making, pattern rec- ognition, implicit learning, artifcial grammar learning, immersive environment INTRODUCTION People make decisions across a wide range of situations as they go about their daily lives. An individual may have to decide with split- second timing whether to take an alternate route if it appears that the typical route to work is blocked by a traffic accident. Or an individual may suddenly develop a sense of foreboding while exploring the attractions along a city street and decide not to go farther. These kinds of decisions rely on unconscious pattern recog- nition, which occurs in many domains of exper- tise, such as fighting fires, diagnosing infants with disease, and engaging an enemy during combat (Klein, 1998). Decision making based on situational pattern recognition is called intuitive (Klein, 1998, 2008; Lopes & Oden, 1991; Westcott, 1968; Zsambok & Klein, 1997). Intuitive decision making is seen as one of two forms, or modes, of decision making in the dual-process frame- work (Evans, 2003, 2008; Hammond, Hamm, Grassia, & Pearson, 1997; Hogarth, 2001; Kahneman & Frederick, 2002; Kahneman & Klein, 2009; Sloman, 1996). Intuitive decision making involves a fast and nonconscious, non- deliberative process that is relatively effortless and not constrained by working memory limita- tions. The other form of decision making is called analytical. Analytical decision making refers to decision making via deliberation, a relatively slow and conscious process that is effortful and constrained by working memory limitations. We report in this article an experimental investigation of the training of intuitive deci- sion making in a simulated real-world environ- ment. We induced implicit statistical learning, which was a means by which knowledge about (artificial) situational patterns was acquired, in Address correspondence to Robert Earl Patterson, Air Force Research Laboratory, RHA/711 HPW Wright-Patterson AFB, OH 45433; e-mail: Robert.Patterson@wpafb.af.mil. Authors’ Note: The authors of this article are U.S. government employees and created the article within the scope of their employment. As a work of the U.S. federal government, the content of the article is in the public domain. HUMAN FACTORS Vol. 55, No. 2, April 2013, pp. 333-345 DOI:10.1177/0018720812454432 Training Intuitive Decision Making in a Simulated Real-World Environment Robert Earl Patterson, Air Force Research Laboratory, Wright-Patterson Air Force Base, Ohio, Byron J. Pierce, Renaissance Sciences Corporation, Chandler, Arizona, Alan S. Boydstun, Lisa M. Ramsey, and Jodi Shannan, L-3 Communications, Mesa, Arizona, and Lisa Tripp and Herb Bell, Air Force Research Laboratory, Mesa, Arizona