Lessons from reinforcement learning for biological representations of space

TL;DR

This paper explores reinforcement learning models that do not build explicit 3D maps but still support spatial tasks, offering insights into biological spatial representations beyond traditional Cartesian maps.

Contribution

It demonstrates that non-Cartesian, learned representations can support geometrically consistent spatial tasks, challenging the dominance of the cognitive map hypothesis.

Findings

Reinforcement learning can support spatial tasks without explicit 3D maps.

Feature persistence improves geometric task performance.

Non-Cartesian representations offer a promising alternative to traditional spatial models.

Abstract

Neuroscientists postulate 3D representations in the brain in a variety of different coordinate frames (e.g. 'head-centred', 'hand-centred' and 'world-based'). Recent advances in reinforcement learning demonstrate a quite different approach that may provide a more promising model for biological representations underlying spatial perception and navigation. In this paper, we focus on reinforcement learning methods that reward an agent for arriving at a target image without any attempt to build up a 3D 'map'. We test the ability of this type of representation to support geometrically consistent spatial tasks such as interpolating between learned locations using decoding of feature vectors. We introduce a hand-crafted representation that has, by design, a high degree of geometric consistency and demonstrate that, in this case, information about the persistence of features as the camera translates (e.g. distant features persist) can improve performance on the geometric tasks. These examples avoid Cartesian (in this case, 2D) representations of space. Non-Cartesian, learned representations provide an important stimulus in neuroscience to the search for alternatives to a 'cognitive map'.