Integration without Integrated Models or Theories

It is traditionally thought that integration in cognitive science requires combining different perspectives, elements, and insights into an integrated model or theory of the target phenomenon. In this paper I argue that this type of integration is frequently not possible in cognitive science due to our reliance on using different idealizing and simplifying assumptions in our models and theories. Despite this, I argue that we can still have integration in cognitive science and attain all the benefits that integrated models would provide, without the need for their construction. Models which make incompatible idealizing assumptions about the target phenomenon can still be integrated by understanding how to draw coherent and compatible inferences across them. I discuss how this is possible, and demonstrate how this supports a different kind of integration. This sense of integration allows us to use collections of contradictory models to develop a consistent, comprehensive and non-contradictory understanding of a single unified phenomenon without the need for a single integrated model or theory

An Entorhinal-Hippocampal Model for Simultaneous Cognitive Map Building

Hippocampal place cells and entorhinal grid cells have been hypothesized to be able to form map-like spatial representation of the environment, namely cognitive map. In most prior approaches, either neural network methods or only hippocampal models are used for building cognitive maps, lacking biological fidelity to the entorhinal-hippocampal system. This paper presents a novel computational model to build cognitive maps of real environments using both place cells and grid cells. The proposed model includes two major components: (1) A competitive Hebbian learning algorithm is used to select velocity-coupled grid cell population activities, which path-integrate self-motion signals to determine computation of place cell population activities; (2) Visual cues of environments are used to correct the accumulative errors intrinsically associated with the path integration process. Experiments performed on a mobile robot show that cognitive maps of the real environment can be efficiently built. The proposed model would provide an alternative neuro-inspired approach for robotic mapping, navigation and localization