Finding the Pattern: On-Line Extraction of Spatial Structure During Virtual Navigation

While navigating the world, we pick up on patterns of where things tend to appear. According to theories of memory and studies of animal behavior, knowledge of these patterns emerges gradually over days or weeks via consolidation of individual navigation episodes. Here, we discovered that navigation patterns can also be extracted on-line, prior to the opportunity for off-line consolidation, as a result of rapid statistical learning. Thirty human participants navigated a virtual water maze in which platform locations were drawn from a spatial distribution. Within a single session, participants increasingly navigated through the mean of the distribution. This behavior was better simulated by random walks from a model that had only an explicit representation of the current mean, compared with a model that had only memory for the individual platform locations. These results suggest that participants rapidly summarized the underlying spatial distribution and used this statistical knowledge to guide future navigation.