🤖 AI Summary
Current claims that artificial neural networks and biological brains converge on a single universal solution—termed “universality”—are limited in scope. This work proposes the Umwelt Representation Hypothesis, arguing that representational alignment arises from overlapping ecological constraints rather than convergence to a global optimum. Through empirical analyses of representational alignment across species, individuals, and artificial neural networks—combined with ecological constraint modeling and comparative neuroscience methods—the study demonstrates that representational differences are systematic and adaptive, challenging explanations based on universality. By rejecting the notion of a universal representational space, this research redefines the paradigm for model comparison, introducing alignment clusters defined within an ecological constraint space, thereby offering a novel framework for understanding the relationship between artificial and biological intelligence representations.
📝 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.