Little is known about the strategies people use to effectively hide objects from others, or to search for objects others have hidden. The present research extends a recent investigation of people’s hiding and searching strategies in a simple room with 9 cache location. In the present studies, people hid and searched for three objects under more than 70 floor tiles in complex real and virtual rooms. Experiment 1 replicated several finding of Talbot et al within the more complex real and virtual environments. Specifically, people traveled further from origin and selected more dispersed locations when hiding than when searching. Experiments 2 and 3 showed that: 1) people were attracted to an area of darkness when searching and avoided locations close to a window when hiding, 2) when search attempts were limited to three choices, people searched farther from origin and dispersed their locations more when hiding than when searching, and 3) informing people that they would need to recover their hidden objects altered their hiding behavior and increased recovery accuracy. Across all experiments, consistencies in location preferences emerged, with more preference for the middle of the room during hiding and more preference for corners of the room during searching. Even though the same people participated in both the hiding and searching tasks, it appears that people use different strategies to select hiding places than to search for objects hidden by others.
Hiding and seeking is a cognitive ability frequently demonstrated by humans in both real life and video games. We use machine learning to automatically construct the first computational model of hide/seek behavior in adult humans in a video game like setting. The model is then run generatively in a novel environment and its behavior is found indistinguishable from actual human behavior by a panel of human judges. In doing so the artificial intelligence agent using the model appears to have passed a version of the Turing test for hiding and seeking.
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