Decomposing geographical judgments into spatial, temporal and linguistic components.

Area of Science:

• Cognitive Psychology
• Neuroscience
• Artificial Intelligence

Background:

• Human spatial cognition for large-scale environments typically assumes reliance on direct perceptual experience.
• The role of non-spatial information, such as temporal and linguistic data, in forming cognitive maps is less understood.

Purpose of the Study:

• To investigate whether temporal and linguistic information influence human mental exploration of large-scale maps.
• To determine if linguistic information can explain human performance in distance judgment tasks beyond spatial information.

Main Methods:

• Quantified temporal distance using minimum train travel times between Italian cities.
• Retrieved linguistic distance using AI-based distributional semantic models from natural language.
• Conducted behavioral experiments to assess distance judgments between cities.

Main Results:

• Temporal and linguistic distances accurately reflected real geographical distances.
• Linguistic information significantly improved distance judgment performance, even when spatial information was considered.
• Temporal information did not show relevance in the distance judgment task.

Conclusions:

• Human cognitive map formation for large-scale environments integrates perceptual and non-spatial information.
• Linguistic processing, facilitated by AI models, plays a crucial role in spatial cognition.
• Cognitive map development likely involves an interplay between spatial and non-spatial learning principles.