# Unsupervised Cognition

arXiv: https://arxiv.org/abs/2409.18624 · HTML: https://mlsanity.com/paper/2409.18624/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 55 references that are in the map.
- First posted on arXiv 27 Sep 2024.

## Builds on (most cited first, 19 of 19)

- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [World Models](https://mlsanity.com/paper/1803.10122/) (2018)
- [Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture](https://mlsanity.com/paper/2301.08243/) (2023)
- [Invariant Information Clustering for Unsupervised Image Classification and Segmentation](https://mlsanity.com/paper/1807.06653/) (2018)
- [MineRL: A Large-Scale Dataset of Minecraft Demonstrations](https://mlsanity.com/paper/1907.13440/) (2019)
- [Neural-Symbolic Learning and Reasoning: A Survey and Interpretation](https://mlsanity.com/paper/1711.03902/) (2017)
- [Unsupervised Speech Recognition](https://mlsanity.com/paper/2105.11084/) (2021)
- [A Survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part I: Models and Data Transformations](https://mlsanity.com/paper/2111.06077/) (2021)
- [Shaking the foundations: delusions in sequence models for interaction and control](https://mlsanity.com/paper/2110.10819/) (2021)
- [Robust High-dimensional Memory-augmented Neural Networks](https://mlsanity.com/paper/2010.01939/) (2020)
- [Dense Unsupervised Learning for Video Segmentation](https://mlsanity.com/paper/2111.06265/) (2021)
- [Unsupervised Representation Learning by InvariancePropagation](https://mlsanity.com/paper/2010.11694/) (2020)
- [How do neurons operate on sparse distributed representations? A mathematical theory of sparsity, neurons and active dendrites](https://mlsanity.com/paper/1601.00720/) (2016)
- [On the Diagnostic of Road Pathway Visibility](https://mlsanity.com/paper/1601.05535/) (2016)
- [Towards Learning Abductive Reasoning using VSA Distributed Representations](https://mlsanity.com/paper/2406.19121/) (2024)
- [Continual General Chunking Problem and SyncMap](https://mlsanity.com/paper/2006.07853/) (2020)
- [From Manifestations to Cognitive Architectures: a Scalable Framework](https://mlsanity.com/paper/2406.09823/) (2024)
- [Edgeless-GNN: Unsupervised Representation Learning for Edgeless Nodes](https://mlsanity.com/paper/2104.05225/) (2021)
- [Approximate Hubel-Wiesel Modules and the Data Structures of Neural Computation](https://mlsanity.com/paper/1512.08457/) (2015)

## Built on by (most cited first, 2 of 2)

- [Beating Transformers using Synthetic Cognition](https://mlsanity.com/paper/2504.07619/) (2025)
- [Optimisation Is Not What You Need](https://mlsanity.com/paper/2507.03045/) (2025)

## Abstract (arXiv)

Unsupervised learning methods have a soft inspiration in cognition models. To this day, the most successful unsupervised learning methods revolve around clustering samples in a mathematical space. In this paper we propose a primitive-based, unsupervised learning approach for decision-making inspired by a novel cognition framework. This representation-centric approach models the input space constructively as a distributed hierarchical structure in an input-agnostic way. We compared our approach with both current state-of-the-art unsupervised learning classification, with current state-of-the-art small and incomplete datasets classification, and with current state-of-the-art cancer type classification. We show how our proposal outperforms previous state-of-the-art. We also evaluate some cognition-like properties of our proposal where it not only outperforms the compared algorithms (even supervised learning ones), but it also shows a different, more cognition-like, behaviour.