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The Umwelt Representation Hypothesis: rethinking Universality
Victoria Bosch1, Rowan P Sommers1, Adrien Doerig2
1Institute of Cognitive Science, University of Osnabrück, Osnabrück, Germany.
Trends in Cognitive Sciences
|August 8, 2026
Summary
The claim that artificial neural networks (ANNs) and brains share universal representations is premature. Alignment likely stems from shared ecological constraints, not convergence on a single optimum.
Area of Science:
- Cognitive Science
- Neuroscience
- Artificial Intelligence
Background:
- Recent studies show representational alignment between artificial neural networks (ANNs) and biological brains.
- This has led to the hypothesis that all capable systems converge on universal representations.
- The universality claim suggests a single, optimal way to represent reality.
Purpose of the Study:
- To challenge the premature claim of universality in representational alignment.
- To propose the Umwelt Representation Hypothesis.
- To reframe the comparison of ANNs and biological systems.
Main Methods:
- Reviewing empirical evidence on representational differences across species, individuals, and ANNs.
- Analyzing the role of ecological constraints in system development.
- Developing a new framework for comparing ANN models.
Main Results:
- Representational differences between species, individuals, and ANNs are systematic and adaptive.
- This empirical evidence challenges the notion of universal representations.
- Alignment is better explained by overlapping ecological constraints.
Conclusions:
- The claim of universality in ANNs and brain representations is premature.
- The Umwelt Representation Hypothesis offers an alternative explanation for alignment.
- ANN model comparison should map alignment clusters within ecological constraint spaces, not seek a single world model.
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