# Learning abstract perceptual notions: the example of space

## TL;DR

This paper explores how abstract notions like space can be understood as laws of sensory transformations, proposing a framework that combines machine learning with sensorimotor theory to learn such concepts through simulation.

## Contribution

It introduces a method for learning the concept of space as laws of sensory transformations, bridging machine learning and sensorimotor theory with a simple simulation example.

## Key findings

- Space can be represented as laws of sensory transformations.
- A naive agent can learn to encode displacements in space.
- The approach suggests pathways for unsupervised learning of abstract notions.

## Abstract

Humans are extremely swift learners. We are able to grasp highly abstract notions, whether they come from art perception or pure mathematics. Current machine learning techniques demonstrate astonishing results in extracting patterns in information. Yet the abstract notions we possess are more than just statistical patterns in the incoming information. Sensorimotor theory suggests that they represent functions, laws, describing how the information can be transformed, or, in other words, they represent the statistics of sensorimotor changes rather than sensory inputs themselves. The aim of our work is to suggest a way for machine learning and sensorimotor theory to benefit from each other so as to pave the way toward new horizons in learning. We show in this study that a highly abstract notion, that of space, can be seen as a collection of laws of transformations of sensory information and that these laws could in theory be learned by a naive agent. As an illustration we do a one-dimensional simulation in which an agent extracts spatial knowledge in the form of internalized ("sensible") rigid displacements. The agent uses them to encode its own displacements in a way which is isometrically related to external space. Though the algorithm allowing acquisition of rigid displacements is designed \emph{ad hoc}, we believe it can stimulate the development of unsupervised learning techniques leading to similar results.