The Umwelt Representation Hypothesis: Rethinking Universality

TL;DR

The paper challenges the idea of universal representations in neural systems, proposing that representational alignment results from shared ecological constraints rather than convergence to a single optimal model.

Contribution

It introduces the Umwelt Representation Hypothesis, emphasizing ecological constraints as the basis for representational similarities across systems.

Findings

Representational differences are systematic and adaptive across species and ANNs.

Alignment arises from ecological constraints, not a single optimal representation.

Reframes ANN comparison as mapping ecological constraint clusters.

Abstract

Recent studies reveal striking representational alignment between artificial neural networks (ANNs) and biological brains, leading to proposals that all sufficiently capable systems converge on universal representations of reality. Here, we argue that this claim of Universality is premature. We introduce the Umwelt Representation Hypothesis (URH), proposing that alignment arises not from convergence toward a single global optimum, but from overlap in ecological constraints under which systems develop. We review empirical evidence showing that representational differences between species, individuals, and ANNs are systematic and adaptive, which is difficult to reconcile with Universality. Finally, we reframe ANN model comparison as a method for mapping clusters of alignment in ecological constraint space rather than searching for a single optimal world model.