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Updated: Aug 6, 2026
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The compositionality continuum as a principle for studying the neural basis of intelligence
Reidar Riveland1, Alexandre Pouget2, Laura Driscoll3,4
1Gatsby Computational Neuroscience Unit, University College London, London, UK. r.riveland@ucl.ac.uk.
Nature Neuroscience
|August 5, 2026
Summary
Compositionality, key to intelligence, is not uniquely human. Both animals and large language models show compositional abilities, challenging the need for explicit symbolic structures in cognition.
Area of Science:
- Cognitive Science
- Neuroscience
- Artificial Intelligence
Background:
- Compositionality, the ability to combine finite elements into infinite expressions, is traditionally seen as a uniquely human trait crucial for general intelligence.
- Natural language exemplifies compositionality, but recent research questions its exclusivity to humans and the necessity of explicit symbolic structures.
Purpose of the Study:
- To investigate whether compositionality is exclusive to human cognition.
- To explore the role of scale versus explicit mechanisms in achieving compositional abilities.
- To compare compositional implementations across biological and artificial systems.
Main Methods:
- Analyzing high-density neural recordings from animals performing compositional tasks.
- Reverse engineering neural network models to understand their compositional computations.
- Developing theoretical models of compositional mechanisms.
Main Results:
- Large language models demonstrate significant compositional abilities through sheer scale.
- Neuroscience reveals animals employ compositional neural codes for novel situations.
- Emerging theories suggest neural networks can implement compositionality without explicit symbolic structures.
Conclusions:
- Compositionality may exist on a continuum, varying in computational building blocks and recombination rules.
- General intelligence might emerge from scaled mechanisms rather than solely explicit compositional strategies.
- Comparative analysis of biological and artificial systems is key to understanding the nature of compositionality in intelligence.
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