Language Experience in Cognitive Maps and Image Spaces

Decomposing geographical judgments into spatial, temporal and linguistic components

When mentally exploring maps representing large-scale environments (e.g., countries or continents), humans are assumed to mainly rely on spatial information derived from direct perceptual experience (e.g., prior visual experience with the geographical map itself). In the present study, we rather tested whether also temporal and linguistic information could account for the way humans explore and ultimately represent this type of maps. We quantified temporal distance as the minimum time needed to travel by train across Italian cities, while linguistic distance was retrieved from natural language through cognitively plausible AI models based on non-spatial associative learning mechanisms (i.e., distributional semantic models). In a first experiment, we show that temporal and linguistic distances capture with high-confidence real geographical distances. Next, in a second behavioral experiment, we show that linguistic information can account for human performance over and above real spatial information (which plays the major role in explaining participants' performance) in a task in which participants have to judge the distance between cities (while temporal information was found to be not relevant). These findings indicate that, when exploring maps representing large-scale environments, humans do take advantage of both perceptual and linguistic information, suggesting in turn that the formation of cognitive maps possibly relies on a strict interplay between spatial and non-spatial learning principles.

Task-related connectivity of decision points during spatial navigation in a schematic map

Successful navigation is largely dependent on the ability to make correct decisions at navigational decision points. However, the interaction between the brain regions associated with the navigational decision point in a schematic map is unclear. In this study, we adopted a 2D subway paradigm to study the neural basis underlying decision points. Twenty-eight subjects performed a spatial navigation task using a subway map during fMRI scanning. We adopted a voxel-wise general linear model (GLM) approach and found four brain regions, the left hippocampus (HIP), left parahippocampal gyrus (PHG), left ventromedial prefrontal cortex (vmPFC), and right retrosplenial cortex (RSC), activated at a navigational decision point in a schematic map. Using a psychophysiological interactions (PPI) method, we found that (1) both the left vmPFC and right HIP interacted cooperatively with the right RSC, and (2) the left HIP and the left vmPFC interacted cooperatively at the decision point. These findings may be helpful for revealing the neural mechanisms underlying decision points in a schematic map during spatial navigation.