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Route sequence knowledge supports the formation of cognitive maps.

Hilton, C. and Wiener, J., 2023. Route sequence knowledge supports the formation of cognitive maps. Hippocampus, 33 (11), 1161-1170.

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DOI: 10.1002/hipo.23574

Abstract

In this study, we examined the extent to which knowledge about the sequence of places encountered during route learning supports the formation of a metric cognitive map. In a between subjects design, participants learned a route until they could navigate it independently without error whilst also learning information about either the identity of places along the route (Recognition Learning condition) or the sequence of places along the route (Sequence Learning condition). In a follow-up Reconstruction of Order Task, we confirmed that participants in the Sequence Learning condition had more accurate route sequence knowledge than those in the Recognition Learning condition, despite requiring the same overall number of trials to learn the route. Participants then completed a Pointing Task to assess the quality of their cognitive map of the environment. Both groups performed above chance level, showing incidental encoding of metric information, but the Sequence Learning group produced significantly lower pointing errors than the Recognition Learning group. Further, we found that route distance between pairs of places was a strong predictor of pointing error in both groups, whilst Euclidean distance between places was a significant, but weak, predictor only for the Sequence Learning condition. The results of this study demonstrate that discrete route sequence knowledge directly supports the formation of metric cognitive maps. We consider how the results are best explained by interactions between striatal route representations and hippocampal metric representations, centered around the sequence of places acting as a scaffold for the encoding of metric information.

Item Type:Article
ISSN:1050-9631
Uncontrolled Keywords:cognitive map; navigation; route learning; sequence memory
Group:Faculty of Science & Technology
ID Code:38980
Deposited By: Symplectic RT2
Deposited On:12 Sep 2023 10:24
Last Modified:05 Jun 2024 07:17

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