Umwelt Engineering: Designing the Cognitive Worlds of Linguistic Agents

TL;DR

This paper introduces Umwelt engineering, a new layer in agent design that manipulates linguistic environments to influence reasoning and cognition, demonstrated through experiments with language models under various constraints.

Contribution

It proposes Umwelt engineering as a novel approach to shape AI cognition by designing linguistic environments, supported by experiments showing improved reasoning and ensemble performance.

Findings

No-Have improves ethical reasoning by 19.1 percentage points

No-Have achieves 92.8% constraint compliance

A 3-agent ensemble with No-Have achieves 100% coverage

Abstract

I propose Umwelt engineering -- the deliberate design of the linguistic cognitive environment -- as a third layer in the agent design stack, upstream of both prompt and context engineering. Two experiments test the thesis that altering the medium of reasoning alters cognition itself. In Experiment 1, three language models reason under two vocabulary constraints -- No-Have (eliminating possessive "to have") and E-Prime (eliminating "to be") -- across seven tasks (N=4,470 trials). No-Have improves ethical reasoning by 19.1 pp (p < 0.001), classification by 6.5 pp (p < 0.001), and epistemic calibration by 7.4 pp, while achieving 92.8% constraint compliance. E-Prime shows dramatic but model-dependent effects: cross-model correlations reach r = -0.75. In Experiment 2, 16 linguistically constrained agents tackle 17 debugging problems. No constrained agent outperforms the control individually, yet a 3-agent ensemble achieves 100% ground-truth coverage versus 88.2% for the control. A permutation test confirms only 8% of random 3-agent subsets achieve full coverage, and every successful subset contains the counterfactual agent. Two mechanisms emerge: cognitive restructuring and cognitive diversification. The primary limitation is the absence of an active control matching constraint prompt elaborateness.