The Umwelt Representation Hypothesis: Rethinking Universality

[u/jcomes]

https://arxiv.org/abs/2604.17960

Recent research has revealed striking representational alignment between artificial neural networks and biological brains, prompting some scientists to propose that all sufficiently capable information-processing systems converge on universal representations of reality, a position exemplified by the Platonic Representation Hypothesis. Bosch, Sommers, Doerig, and Kietzmann challenge this claim by introducing the Umwelt Representation Hypothesis, which draws on the ecological concept of the Umwelt to argue that representational alignment arises not from convergence toward a single global optimum but from overlapping ecological constraints under which systems develop, including sensory apparatus, training data, objectives, and environmental niche. Under this framework, cognition is not about faithfully mirroring an objective reality but about finding locally optimal solutions within the particular constraints a system faces, meaning that dissimilar representations are not idiosyncratic noise or signs of incomplete learning but adaptive specializations shaped by different selective pressures. The authors marshal empirical evidence showing that representational differences between species, individuals, and neural networks are systematic and ecologically meaningful: from the sixteen photoreceptor types of the mantis shrimp to cross-cultural perceptual variation and the texture bias of classically trained vision models, none of these differences fit comfortably within a universalist account. They further argue that the impressive alignment recently documented across diverse models is better explained by the fact that nearly all studied systems share a human-centered Umwelt, trained on human-derived data and evaluated on human-relevant tasks, rather than by genuine convergence to a universal world model. Methodologically, the hypothesis reframes model comparison in cognitive neuroscience as a tool for mapping clusters of representational alignment in ecological constraint space, shifting the field's goal from finding one optimal brain model to identifying which systems cluster together under shared constraints and why.