Neural Basis of Spatial Navigation Training for Small-Scale Spatial Ability
Poster No:
2067
Submission Type:
Abstract Submission
Authors:
Jin Yu1
Institutions:
1Beijing Normal University, Beijing, Beijing
First Author:
Introduction:
Spatial abilities are generally divided into large-scale (e.g., navigation) and small-scale (e.g., mental rotation) types (Hegarty et al., 2006). While some studies have found a correlation between the two (Kozhevnikov & Hegarty, 2001), others have not (Wang et al., 2014). Both types of tasks activate visual-spatial processing regions (Li et al., 2019), but show task-specific activations (e.g., hippocampus for large-scale, IFG for small-scale tasks), leaving the relationship between the two unclear.
Although small-scale training has been shown to improve large-scale abilities (Jansen et al., 2010), it is unclear whether large-scale training can benefit small-scale abilities or what neural mechanisms underlie this potential transfer. This study aims to: 1) map brain activation in large- and small-scale spatial tasks; 2) assess whether on-site navigation training enhances both types of spatial abilities; and 3) identify neural changes linked to training, advancing our understanding of spatial ability transfer across scales.
Although small-scale training has been shown to improve large-scale abilities (Jansen et al., 2010), it is unclear whether large-scale training can benefit small-scale abilities or what neural mechanisms underlie this potential transfer. This study aims to: 1) map brain activation in large- and small-scale spatial tasks; 2) assess whether on-site navigation training enhances both types of spatial abilities; and 3) identify neural changes linked to training, advancing our understanding of spatial ability transfer across scales.
Methods:
Thirty-two university students (mean age 19.67) were randomly and evenly assigned to a training group or a control group. Both groups completed large- and small-scale spatial ability tests underwent MRI scans before and after a 20-day period, during which only the training group completed 30 minutes of on-site campus navigation training daily. . Pre- and post-test spatial tasks included a distance judgment task (Hirshhorn et al., 2012) for large-scale ability and a paper folding task (Milivojevic et al., 2003) for small-scale ability.
Functional MRI data were collected using a 3T Siemens scanner and preprocessed with FSL's FEAT tool. A mixed-effects model was used to compare pre- and post-test scans between groups, with the training effect measured as the difference in activation between groups.
Functional MRI data were collected using a 3T Siemens scanner and preprocessed with FSL's FEAT tool. A mixed-effects model was used to compare pre- and post-test scans between groups, with the training effect measured as the difference in activation between groups.
Results:
A repeated-measures ANOVA revealed a significant group × test time interaction for both large- and small-scale task performance (ps < .001, η² > .40). The training group showed significantly higher accuracy in both tasks post-test (ps < .001, η² > .30), whereas the control group showed no change.
Both tasks activated the MFG, IFG, and posterior parietal cortex in the pre-test (p < .01, cluster-level p < .05, corrected; Figure 1). The hippocampus and parahippocampal regions were activated only in large-scale tasks, while small-scale tasks showed widespread activation in the parietal lobe.
To examine the training effect, we analyzed the group × test time interaction in gray matter activation (p < .01, cluster-level p < .05, corrected; Figure 2). In the large-scale task, the interaction was significant in two clusters, located in the right MFG (48 voxels; peak coordinates: 26, 4, 66) and the bilateral PCC (29 voxels; peak coordinates: -2, -32, 26). The training group showed stronger activation at post-test in both clusters, while the control group showed no changes.
In the small-scale task, the interaction was significant in three clusters, located in the right postcentral gyrus (26 voxels; peak coordinates: 58, -20, 28), the right precuneus (28 voxels; peak coordinates: 16, -62, 28), and the left STG (33 voxels; peak coordinates: -48, 6, 2). These clusters were negatively activated at pre-test and showed decreased activation after training, while the control group showed no change.
Both tasks activated the MFG, IFG, and posterior parietal cortex in the pre-test (p < .01, cluster-level p < .05, corrected; Figure 1). The hippocampus and parahippocampal regions were activated only in large-scale tasks, while small-scale tasks showed widespread activation in the parietal lobe.
To examine the training effect, we analyzed the group × test time interaction in gray matter activation (p < .01, cluster-level p < .05, corrected; Figure 2). In the large-scale task, the interaction was significant in two clusters, located in the right MFG (48 voxels; peak coordinates: 26, 4, 66) and the bilateral PCC (29 voxels; peak coordinates: -2, -32, 26). The training group showed stronger activation at post-test in both clusters, while the control group showed no changes.
In the small-scale task, the interaction was significant in three clusters, located in the right postcentral gyrus (26 voxels; peak coordinates: 58, -20, 28), the right precuneus (28 voxels; peak coordinates: 16, -62, 28), and the left STG (33 voxels; peak coordinates: -48, 6, 2). These clusters were negatively activated at pre-test and showed decreased activation after training, while the control group showed no change.
Conclusions:
Navigation training improved large-scale spatial performance and transferred to the small-scale task. This was reflected in increased activation in the right MFG and bilateral PCC for the large-scale task, and reduced inhibition in the left STG, right precuneus, and right postcentral gyrus for the small-scale task.
Higher Cognitive Functions:
Space, Time and Number Coding
Learning and Memory:
Skill Learning 2
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI)
Perception, Attention and Motor Behavior:
Perception: Visual 1
Keywords:
Learning
MRI
Perception
Vision
Other - spatial navigation
1|2Indicates the priority used for review
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Provide references using APA citation style.
Hirshhorn, M. et al. (2012). The hippocampus is involved in mental navigation for a recently learned, but not a highly familiar environment: a longitudinal fMRI study. Hippocampus, 22(4), 842-852.
Jansen, P. et al. (2010). Manual rotation training improves direction-estimations in a virtual environmental space. European Journal of Cognitive Psychology, 22(1), 6-17.
Kozhevnikov, M. et al. (2001). A dissociation between object manipulation spatial ability and spatial orientation ability. Memory & cognition, 29, 745-756.
Li, Y. et al. (2019). Shared and distinct neural bases of large-and small-scale spatial ability: a coordinate-based activation likelihood estimation meta-analysis. Frontiers in neuroscience, 12, 1021.
Milivojevic, B. et al. (2003). Non-identical neural mechanisms for two types of mental transformation: event-related potentials during mental rotation and mental paper folding. Neuropsychologia, 41(10), 1345-1356.
Wang, L. et al. (2014). Spatial ability at two scales of representation: A meta-analysis. Learning and Individual Differences, 36, 140-144.