@inproceedings{
d{\"o}nmez2023discovering,
title={Discovering Latent Causes and Memory Modification: A Computational Approach Using Symmetry and Geometry},
author={Arif D{\"o}nmez},
booktitle={NeurIPS 2023 Workshop on Symmetry and Geometry in Neural Representations},
year={2023},
url={https://openreview.net/forum?id=B3bxgPqxGT}
}
We learn from our experiences, even though they are never exactly the same. This implies 
  that we need to assess their similarity to apply what we have learned from one experience 
  to another. It is proposed that we “cluster” our experiences based 
  on hidden latent causes that we infer. It is also suggested that surprises, which occur 
  when our predictions are incorrect, help us categorize our experiences into distinct groups. 
  In this paper, we develop a computational theory that emulates these processes based on two basic concepts 
  from intuitive physics and Gestalt psychology using symmetry and geometry.
  We apply our approach to simple tasks that involve inductive reasoning. Remarkably, the output of our computational approach aligns 
 closely with human responses.
Discovering Latent Causes and Memory Modification: A Computational Approach Using Symmetry and Geometry
Arif Dönmez