The Inertia of Desire: Dual-Process Asynchrony and the Decoupling of Evolutionary Mate-Choice Cues

Human mate preferences evolved as specialized psychological adaptations to navigate recurrent reproductive challenges within ancestral ecologies. However, rapid institutional, technological, and economic restructuring has decoupled the historical fitness payoffs of specific ancestral cues from contemporary relationship outcomes. While evolutionary mismatch models explain why evolved mechanisms can generate suboptimal outcomes in novel environments, they do not explain why conscious, reflective recognition of altered ecological conditions often fails to recalibrate affective attraction. Concurrently, while domain-general associative-propositional models document that implicit evaluations update more slowly than propositional judgments, they do not require phylogenetic content specificity. This theoretical paper formalizes the Dual-Process Mismatch Model (DPMM) and the Affective Latency Thesis, integrating evolutionary mismatch, affective-propositional dissociation, cue-function transformation, and mate-choice theory. We propose a three-speed temporal asynchrony (τreflective ≪ τcultural/technological ≪ τbiological) and conceptualize affective mate valuation as exhibiting resistance to direct propositional revision. We define candidate evolved affective evaluations independently of measurement channels, classify functional cue modifications into Attenuation, Neutralization, and Inversion, audit five flagship exemplars, and engage the central discriminating question: Is the inertia of mate-choice desire merely the ordinary inertia of associative learning, or does evolutionary history contribute additional resistance to propositional recalibration? Crucially, we identify Paradigm 4 (the matched arbitrary-learned control experiment) as the load-bearing empirical test of DPMM: absent empirical confirmation from this paradigm, the model's distinct novelty claim over domain-general associative learning remains theoretically motivated but unconfirmed. We formulate derived taxonomic hypotheses, establish an explicit measurement-validity framework, define model-level and cue-level falsification criteria, and systematically differentiate DPMM from competing social and cognitive accounts.