# A Unified Account of Concepts and Chunks

arXiv: https://arxiv.org/abs/2609.30414 · HTML: https://mlsanity.com/paper/2609.30414/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 32 references that are in the map.
- First posted on arXiv 24 Sep 2026.

## Builds on (most cited first, 4 of 4)

- [Convolutional Cobweb: A Model of Incremental Learning from 2D Images](https://mlsanity.com/paper/2201.06740/) (2022)
- [Efficient Induction of Language Models Via Probabilistic Concept Formation](https://mlsanity.com/paper/2212.11937/) (2022)
- [Hierarchical Semantic Retrieval with Cobweb](https://mlsanity.com/paper/2510.02539/) (2025)
- [TRESTLE: A Model of Concept Formation in Structured Domains](https://mlsanity.com/paper/2410.10588/) (2024)

## Abstract (arXiv)

Cognitive psychology has studied how people encode, use, and learn concepts that describe categories, and how they represent, recognize, and acquire chunks for familiar patterns of elements. The literatures on these two topics are nearly disjoint, which poses a challenge for unified theories of cognition. In this paper, we review Cobweb, a computational account of categorization and concept formation, then propose an extended theory that incorporates chunks and their acquisition. The theory makes no commitments about modality, applying to any experience that decomposes into elements and relations among them. We also present trellis/, an implementation of this theory, and illustrate its application to learning context-free grammars, which we adopt as a testbed because they involve both concept-like and chunk-like elements. In addition, we report experimental results on learning for three synthetic grammars that demonstrate the system's ability to represent syntactic knowledge, use it to parse and generate sentences, and learn compositional structures from sample parses. We conclude by discussing related work on concepts and chunks, along with directions for future research on the problem.