Navigating Knowledge Spaces Using Cognitive Maps

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One of the most fundamental abilities of the brain is to flexibly form a cognitive map of the spatial environment for navigation. Recent work suggests that the concept of cognitive maps in the brain extends far beyond the representation of physical space by demonstrating that cognitive maps can form flexibly and in a task-specific manner and can encompass semantic representations at various levels of abstraction. Such orderly maps of task-relevant, semantic knowledge could serve a number of important roles, including categorization, memorization, and extrapolation towards object properties that were not encountered before. However, it is currently not well understood how cognitive maps of more abstract semantic properties form, how they can support tasks like category learning or visual search and to what extent they generalize across different tasks. Hence, by leveraging the collaborative strength and scientific scope of the ARENA research group, the research proposed here aims to shed new light on the formation and computational benefits of cognitive maps by developing new AI models and testing their predictions in the human brain. First, we will model cognitive maps for abstract representations using state-of-the-art deep generative adversarial networks (GANs). GANs can produce novel, near realistic data, such as complex natural images, and its latent space contains well-ordered, semantically meaningful representations. Building upon these properties, we propose methods to construct low-dimensional interpretable maps from the latent spaces of GANs that encompass characteristic semantic features of visual objects. Then, we apply this approach to the problem of category learning with limited input data. Since GANs can mimic near realistic data, they can be used for data augmentation and we explore how the GANs’ ability to sample data, when guided by suitable cognitive maps, can make category learning based on little available data more efficient. The process of sampling data is akin to mental imagery, and so we hypothesize that sampling guided by a cognitive map could serve as a mechanism for category learning in humans. The model predicts changes in the high dimensional structure of brain activity during category learning and we test these predictions in parallel experiments conducted within ARENA. Finally, through intense collaborations within the ARENA consortium we will analyze the generalizability of cognitive maps as well as their flexibility across different tasks and we will investigate this in the context of category learning and learning scene grammar using computational models that are tightly linked to behavioral experiments in humans.By studying the core principles and functional significance of cognitive maps, we aim to gain an improved understanding of how brains and AI systems can learn abstract knowledge efficiently, and in a task-specific manner.