@inproceedings{
kaplanis2023learning,
title={Learning Causally Emergent Representations},
author={Christos Kaplanis and Pedro Mediano and Fernando Rosas},
booktitle={NeurIPS 2023 workshop: Information-Theoretic Principles in Cognitive Systems},
year={2023},
url={https://openreview.net/forum?id=4kkYR1kklR}
}
Cognitive processes usually take place at a macroscopic scale in systems characterised by emergent properties, which make the whole `more than the sum of its parts.' While recent proposals have provided quantitative, information-theoretic metrics to detect emergence in time series data, it is often highly non-trivial to identify the relevant macroscopic variables a priori. In this paper we leverage recent advances in representation learning and differentiable information estimators to put forward a data-driven method to find emergent variables. The proposed method successfully detects emergent variables and recovers the ground-truth emergence values in a synthetic dataset. This proof-of-concept paves the ground for future analyses uncovering the emergent structure of cognitive representations in biological and artificial intelligence systems.
Learning Causally Emergent Representations
Christos Kaplanis Pedro Mediano Fernando Rosas