Structuring spatial knowledge through domain-general, non-spatial learning mechanisms

Introduction

Is space the main organizer of our mental reality? The answer to this question is apparently "yes". According to recent views, nearly any type of knowledge would be organized through low-dimensional geometries relying on the same computations that are at play in the navigation of the physical space, as attested by the involvement of the hippocampal-entorhinal region in high-level cognition.

Evolutionary Perspective

From an evolutionary standpoint, spatial processing mechanisms might have thus developed from originally mapping the navigable environment to representing cognitive spaces.

Research Hypothesis

Moving beyond this spatiocentric view of the human mind, OutOfSpace will test the fascinating yet apparently counterintuitive hypothesis that non-spatial associative learning mechanisms are active (if not the main) ingredients in structuring spatial representations.

Methodology

To pursue this aim, OutOfSpace will employ cognitively plausible computational models (i.e., distributional semantic models) based on non-spatial associative learning mechanisms to extract latent knowledge from natural language. This non-spatial information will then be used to predict a variety of spatial representations, using a pioneering interdisciplinary approach that combines:

1. Computational work
2. Behavioural studies
3. Eye tracking
4. fMRI
5. TMS-EEG
6. Intracranial EEG work

This will be conducted in both sighted and blind individuals.

Expected Contributions

OutOfSpace will thus make a breakthrough on two fronts:

1. Probing whether language - a non-spatial learning environment - can encode and recode spatial knowledge without the need for a dedicated spatial memory system.
2. Attesting the strict interplay between spatial and non-spatial learning mechanisms in structuring mental representations.

Conclusion

More generally, these findings will open the venue to developing a more comprehensive, empirically-based cognitive neuroscience framework for processing and representing spatial information.