Observer-Dependent Entropy Retrieval in Linguistic Computation: A Foundational Framework and Benchmarking Methodology

Comprehension failure is not prediction error; it is delayed access to retrievable meaning. Unlike prediction-based models, ODER models delayed access to meaning rather than incorrect anticipation. We introduce Observer-Dependent Entropy Retrieval (ODER), a formal framework that models linguistic understanding as an observer-specific process shaped by attention, working memory, and prior knowledge. In a controlled corpus written in Aurian, a structured test language developed for entropy-based analysis, ODER explains 31% of sentence-trace variance with an average R² = 0.76, outperforming Bayesian-mixture, fuzzy-logic, and incremental-surprisal baselines by at least 7.6 AIC units. We benchmark ODER on a natural English sentence to compare retrieval dynamics, and then test fixed-parameter performance across twelve diverse real-language stimuli. This evaluation captures lawful divergence patterns that align with known comprehension bottlenecks, without the need for re-fitting. The model yields two falsifiable predictions: (i) spikes in the contextual gradient ∇C during garden-path resolution correlate with P600 amplitude, but only in low-working-memory observers; and (ii) off-diagonal coherence terms μ in the observer density matrix predict priming-interference effects. Although expressed in quantum notation, ODER does not posit quantum computation in neural tissue; the density matrix serves as a compact representation of concurrent interpretations whose collapse time τ_res aligns with electrophysiological markers. By reframing comprehension as entropy retrieval rather than entropy reduction, ODER explains why identical sentences impose divergent cognitive costs across populations and provides a benchmarkable framework for modeling neurocognitive variability, including retrieval collapse timing, semantic interference, and working-memory constraints, using parameters that align with biologically measurable comprehension dynamics.