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The medial temporal lobe (MTL) is traditionally considered to be a system that is specialized for long-term memory. Recent work has challenged this notion by demonstrating that this region can contribute to many domains of cognition beyond long-term memory, including perception and attention. One potential reason why the MTL (and hippocampus specifically) contributes broadly to cognition is that it contains relational representations—representations of multidimensional features of experience and their unique relationship to one another—that are useful in many different cognitive domains. Here, we explore the hypothesis that the hippocampus/MTL plays a critical role in attention and perception via relational representations. We compared human participants with MTL damage to healthy age- and education-matched individuals on attention tasks that varied in relational processing demands. On each trial, participants viewed two images (rooms with paintings). On “similar room” trials, they judged whether the rooms had the same spatial layout from a different perspective. On “similar art” trials, they judged whether the paintings could have been painted by the same artist. On “identical” trials, participants simply had to detect identical paintings or rooms. MTL lesion patients were significantly and selectively impaired on the similar room task. This work provides further evidence that the hippocampus/MTL plays a ubiquitous role in cognition by virtue of its relational and spatial representations and highlights its important contributions to rapid perceptual processes that benefit from attention.
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A Computational Method for the Classification of Mental Representations of Objects in 3D Space
The structure mapping task is a simple method to test people’s mental representations of spatial relationships, and has recently been particularly useful in the study of volumetric spatial cognition such as the spatial memory for locations in multilevel buildings. However, there does not exist a standardised method to analyse such data and structure mapping tasks are typically analysed by human raters, based on criteria defined by the researchers. In this article, we introduce a computational method to assess spatial relationships of objects in the vertical and horizontal domains, which are realized through the structure mapping task. Here, we reanalyse participants’ digitised structure maps from an earlier study (N=41) using the proposed computational methodology. Our results show that the new method successfully distinguishes between different types of structure map representations, and is sensitive to learning order effects. This method can be useful to advance the study of volumetric spatial cognition.
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- PAR ID:
- 10458489
- Date Published:
- Journal Name:
- 15th International Conference on Spatial Information Theory (COSIT 2022)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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