# The Structure of Systematicity in the Brain

arXiv: https://arxiv.org/abs/2108.03387 · HTML: https://mlsanity.com/paper/2108.03387/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Aug 2021.

## Built on by (most cited first, 4 of 4)

- [The Relational Bottleneck as an Inductive Bias for Efficient Abstraction](https://mlsanity.com/paper/2309.06629/) (2023)
- [Visual symbolic mechanisms: Emergent symbol processing in vision language models](https://mlsanity.com/paper/2506.15871/) (2025)
- [From Frege to chatGPT: Compositionality in language, cognition, and deep neural networks](https://mlsanity.com/paper/2405.15164/) (2024)
- [Attention-based Iterative Decomposition for Tensor Product Representation](https://mlsanity.com/paper/2406.01012/) (2024)

## Abstract (arXiv)

A hallmark of human intelligence is the ability to adapt to new situations, by applying learned rules to new content (systematicity) and thereby enabling an open-ended number of inferences and actions (generativity). Here, we propose that the human brain accomplishes these feats through pathways in the parietal cortex that encode the abstract structure of space, events, and tasks, and pathways in the temporal cortex that encode information about specific people, places, and things (content). Recent neural network models show how the separation of structure and content might emerge through a combination of architectural biases and learning, and these networks show dramatic improvements in the ability to capture systematic, generative behavior. We close by considering how the hippocampal formation may form integrative memories that enable rapid learning of new structure and content representations.