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Cognitive Computation
Published in:

11-10-2022

Uncovering the Secrets of the Concept of Place in Cognitive Maps Aided by Artificial Intelligence

Authors: Jose A. Fernandez-Leon, Gerardo G. Acosta

Published in: Cognitive Computation | Issue 5/2024

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Abstract

Uncovering how mental representations acquire, recall, and decode spatial information about relative locations and environmental attributes (cognitive map) involves different challenges. This work is geared towards theoretical discussions on the controversial issue of cognitive scalability for understanding cognitive map emergence from place and grid cells at the intersection between neuroscience and artificial intelligence. In our view, different place maps emerge from parallel and hierarchical neural structures supporting a global cognitive map. The mechanisms sustaining these maps do not only process sensory input but also assign the input to a location. Contentious issues are presented around these concepts and provide concrete suggestions for moving the field forward. We recommend approaching the described challenges guided by AI-based theoretical aspects of encoded place instead of based chiefly on technological aspects to study the brain. SIGNIFICANCE: A formal difference exists between the concepts of spatial representations between experimental neuroscientists and computer scientists and engineers in the so-called neural-based autonomous navigation field. From a neuroscience perspective, we consider the position of an organism’s body to be entirely determined by translational spatial information (e.g., visited places and velocities). An organism predicts where it is at a specific time using continuous or discrete spatial functions embedded into navigation systems. From these functions, we infer that the concept of place has emerged. However, from an engineering standpoint, we represent structured scaffolds of behavioral processes to determine movements from the organism’s current position to some other spatial locations. These scaffolds are certainly affected by the system’s designer. Therefore, the coding of place, in this case, is predetermined. The contrast between emergent cognitive map through inputs versus predefined spatial recognition between two fields creates an inconsistency. Clarifying this tension can inform us on how the brain encodes abstract knowledge to represent spatial positions, which hints at a universal theory of cognition.
previous article Editorial: What AI and Neuroscience Can Learn from Each Other—Open Problems in Models and Theories
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Metadata
Title
Uncovering the Secrets of the Concept of Place in Cognitive Maps Aided by Artificial Intelligence
Authors
Jose A. Fernandez-Leon
Gerardo G. Acosta
Publication date
11-10-2022
Publisher
Springer US
Published in
Cognitive Computation / Issue 5/2024
Print ISSN: 1866-9956
Electronic ISSN: 1866-9964
DOI
https://doi.org/10.1007/s12559-022-10064-w

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