Training Intuitive Decision Making in a Simulated Real-World Environment

Abstract

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.