What does it mean to represent? Mental representations as falsifiable memory patterns

TL;DR

This paper proposes a falsifiable, structure-based definition of mental representations as inferred latent patterns in the world, aiming to clarify the concept for neuroscience and AI.

Contribution

It introduces an implementable, falsifiable account of representation based on inferred latent structures, moving beyond causal and teleological approaches.

Findings

Simulation of a neural network model supports the proposed representation concept.

The approach enables planning, prediction, and detection of unexpected events.

Provides a foundation for future research in neuroscience and AI.

Abstract

Representation is a key notion in neuroscience and artificial intelligence (AI). However, a longstanding philosophical debate highlights that specifying what counts as representation is trickier than it seems. With this brief opinion paper we would like to bring the philosophical problem of representation into attention and provide an implementable solution. We note that causal and teleological approaches often assumed by neuroscientists and engineers fail to provide a satisfactory account of representation. We sketch an alternative according to which representations correspond to inferred latent structures in the world, identified on the basis of conditional patterns of activation. These structures are assumed to have certain properties objectively, which allows for planning, prediction, and detection of unexpected events. We illustrate our proposal with the simulation of a simple neural network model. We believe this stronger notion of representation could inform future research in neuroscience and AI.