Adaptive Compression Advantage Theory: Logographic Writing Systems, Neural Compression Efficiency, and Human–AI Cognitive Synergy

This paper proposes the Adaptive Compression Advantage Theory (ACAT), a theoretical framework integrating findings from computational neuroscience, cognitive psychology, information theory, philosophy of mind, urban scaling science, and human–AI interaction research to explain individual differences in memory processing efficiency and their interaction with language structure and artificial intelligence systems. Drawing on Rate– Distortion Theory as applied to neural coding and episodic memory (Gershman, 2023; Nagy et al., 2020; Sims, 2016), Fuzzy-Trace Theory's gist–verbatim dissociation (Brainerd & Reyna, 2002; Reyna, 2012, 2021), the Neural Efficiency Hypothesis (Neubauer & Fink, 2009; Grabner et al., 2006), automatic semantic encoding (Campoy et al., 2015), the Dual-Access Model of logographic script processing (Buchweitz et al., 2012; Kadota, 2002; Nakamura et al., 2005), and the Extended Mind thesis (Clark & Chalmers, 1998; Clark, 2025), we propose that individuals vary along a Compression–First to Retention–First continuum in their default memory processing strategy. We introduce the Language Density Factor, arguing that logographic languages—particularly Japanese, with its unique mixedscript system and extensive grammatical ellipsis (Coupé et al., 2019)—provide structural advantages for Compression–First cognitive architectures. Grounding the Co-evolutionary Spiral in superlinear scaling theory from urban science (Bettencourt et al., 2007), we model the dynamics of sustained human–AI dialogue and demonstrate convergence with large-scale empirical findings on AI-augmented productivity (Dell'Acqua et al., 2023; Brynjolfsson et al., 2025; Vaccaro et al., 2024). A novel recursive recompression model (Equation 18) demonstrates how cognitive growth achieves unbounded scope within bounded biological capacity. The framework generates twelve testable predictions for neuroimaging, behavioral, cross-linguistic, and computational validation. Keywords: memory compression, gist extraction, neural efficiency, logographic cognition, kanji processing, human–AI interaction, rate–distortion theory, fuzzy-trace theory, information density, Japanese psycholinguistics, Default Mode Network, LLM compression, extended mind, urban scaling, co-evolution, recursive recompression, forgetting as optimization