Coding Locations Relative to One or Many Landmarks in Childhood

Negen, J; Bou Ali, L; Chere, B; Roome, HE; Park, Y; Nardini, M

Abstract

Cognitive development studies how information processing in the brain changes over the course of development. A key part of this question is how information is represented and stored in memory. This study examined allocentric (world-based) spatial memory, an important cognitive tool for planning routes and interacting with the space around us. This is typically theorized to use multiple landmarks all at once whenever it operates. In contrast, here we show that allocentric spatial memory frequently operates over a limited spatial window, much less than the full proximal scene, for children between 3.5 and 8.5 years old. The use of multiple landmarks increases gradually with age. Participants were asked to point to a remembered target location after a change of view in immersive virtual reality. A k-fold cross-validation model-comparison selected a model where young children usually use the target location’s vector to the single nearest landmark and rarely take advantage of the vectors to other nearby landmarks. The comparison models, which attempt to explain the errors as generic forms of noise rather than encoding to a single spatial cue, did not capture the distribution of responses as well. Parameter fits of this new single- versus multi-cue model are also easily interpretable and related to other variables of interest in development (age, executive function). Based on this, we theorize that spatial memory in humans develops through three advancing levels (but not strict stages): most likely to encode locations egocentrically (relative to the self), then allocentrically (relative to the world) but using only one landmark, and finally, most likely to encode locations relative to multiple parts of the scene.

Citation

Negen, J., Bou Ali, L., Chere, B., Roome, H., Park, Y., & Nardini, M. (2019). Coding Locations Relative to One or Many Landmarks in Childhood. PLoS Computational Biology, 15(10), Article e1007380. https://doi.org/10.1371/journal.pcbi.1007380

• Journal Article Type: Article
• Acceptance Date: Sep 4, 2019
• Online Publication Date: Oct 28, 2019
• Publication Date: Oct 30, 2019
• Deposit Date: Sep 26, 2019
• Publicly Available Date: Nov 1, 2019
• Journal: PLoS Computational Biology
• Print ISSN: 1553-734X
• Electronic ISSN: 1553-7358
• Publisher: Public Library of Science
• Peer Reviewed: Peer Reviewed
• Volume: 15
• Issue: 10
• Article Number: e1007380
• DOI: https://doi.org/10.1371/journal.pcbi.1007380
• Public URL: https://durham-repository.worktribe.com/output/1320184