UNIFIED NEURAL REPRESENTATION MODEL FOR PHYSICAL SPACE AND LINGUISTIC CONCEPTS

Abstract

The spatial processing system of the brain uses grid-like neural representations (grid cells) for supporting vector-based navigation. Experiments also suggest that neural representations for concepts (concept cells) exist in the human brain, and conceptual inference relies on navigation in conceptual spaces. We propose a unified model called "disentangled successor information (DSI)" that explains neural representations for physical space and linguistic concepts. DSI generates grid-like representations in a 2-dimensional space that highly resemble those observed in the brain. Moreover, the same model creates concept-specific representations from linguistic inputs, corresponding to concept cells. Mathematically, DSI vectors approximate value functions for navigation and word vectors obtained by word embedding methods, thus enabling both spatial navigation and conceptual inference based on vector-based calculation. Our results suggest that representations for space and concepts can emerge from a shared mechanism in the human brain.

1. INTRODUCTION

In the brain, grid cells in the entorhinal cortex (EC) represent the space by grid-like representations (Hafting et al., 2005; Doeller et al., 2010; Jacobs et al., 2013) . This neural representation is often related to vector-based spatial navigation because grid cells provide global metric over the space. Theoretically, an animal can estimate the direction to a goal when representations of a current position and a goal position are given (Fiete et al., 2008; Bush et al., 2015) . Furthermore, self-position can be estimated by integrating self-motions when sensory information is not available (McNaughton et al., 2006) . These functions are the basis of robust spatial navigation by animals. There are not only spatial but also conceptual representations in EC. Neurons called as "concept cells" have been found in human medial temporal lobe including EC (Quiroga, 2012; Reber et al., 2019) . Concept cells respond to specific concepts, namely, stimuli related to a specific person, a famous place, or a specific category like "foods" and "clothes". Furthermore, recent experiments also suggest that grid-like representations appear not only for physical space but also for conceptual space if there is a 2-dimensional structure (e.g. lengths of a neck and legs, intensity of two odors), and those representations are the basis of vector-based conceptual inference (Bao et al., 2019; Constantinescu et al., 2016; Park et al., 2021) . Thus, it is expected that there is a shared processing mechanism for physical and conceptual spaces in EC. Existence of shared neural mechanism may also explain why humans use sense of physical space (such as directionality) to communicate abstract concepts (conceptual metaphor (Lakoff & Johnson, 1980) ). However, a principle behind such universal computation in the brain is still unclear. In this paper, we propose a representation model which we call disentangled successor information (DSI) model. DSI is an extension of successor representation (SR), which stems from a theory of reinforcement learning and became one of promising computational models of the hippocampus and EC (Dayan, 1993; Stachenfeld et al., 2017; Momennejad et al., 2017; Momennejad, 2020) . Like eigenvectors of SR, DSI forms grid-like codes in a 2-D space, and those representations support vector-based spatial navigation because DSI approximates value functions for navigation in the framework of linear reinforcement learning (Todorov, 2006; 2009; Piray & Daw, 2021) . Remarkably, when we apply DSI to text data by regarding a sequence of words as a sequence of states, DSI forms concept-specific representations like concept cells. Furthermore, we show mathematical correspondence between DSI and word embedding models in natural language processing (NLP)

