{"paper_id":"2cc4b00d-b65d-47df-a323-2ac79991a616","body_text":"This is a Preprint and has not been peer reviewed. This is version 1 of this Preprint.\nYou must log in to post a comment.\nThere are no comments or no comments have been made public for this article.\nThis is a Preprint and has not been peer reviewed. This is version 1 of this Preprint.\nAdd a Comment\nYou must log in to post a comment.\nComments\nThere are no comments or no comments have been made public for this article.\nWhen does optimisation for performance yield representations that track world structure? We develop a mathematical theory of agents with a single fixed encoding shared across tasks, and use it to resolve the broader debate over whether selection favors fitness-tuned interfaces or veridical perception. Selection favors ecological veridicality: preserving exactly those world-state distinctions required by the task ecology. The governing object is a separation condition on the task distribution $\\mu$: if a pair of states is distinguished on tasks with positive $\\mu$-measure, optimal encodings must separate it. In evolutionary terms, distinctions that systematically affect fitness across the ecology are selected to persist, while distinctions with zero aggregate fitness consequence can collapse neutrally without risk penalty. We prove static optimality results and deterministic evolutionary convergence (Price decomposition plus quasispecies recursion) to the best mutation-accessible optimum, with global convergence under primitive mutation and Wright--Fisher finite-population approximation on fixed horizons. As task diversity increases, resolved ecological complexity $k_T = |W/{\\sim_T}|$ grows monotonically (graded cascade). The framework recovers both established poles of the debate: in the single-task limit, fitness-tuned interface encodings can dominate truth-tracking encodings, while in the fixed-encoding multi-task regime, selection favors ecological veridicality up to capacity limits.\nhttps://doi.org/10.32942/X27S94\nApplied Mathematics, Biostatistics, Cognitive Neuroscience, Evolution\nMathematical biology, Evolution of Perception, Perceptual representation, Multi-task learning, Evolutionary dynamics\nPublished: 2026-03-05 05:09\nLast Updated: 2026-03-05 05:09\nCC BY Attribution 4.0 International\nData and Code Availability Statement:\nhttps://github.com/gvdr/evo_interface_veridicality\nLanguage:\nEnglish","source_license":"CC-BY-4.0","license_restricted":false}