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Evolutionary Neurogenetic Transitions in Midlife and the Evolved Generative Intelligence Architecture EGIA.pdfEvolutionary Neurogenetic Transitions in Midlife and the Evolved Generative Intelligence Architecture (EGIA): Empirical Evidence, Adaptive Mechanisms, and Computational Implementation Roberto De Biase Rigene Project January 17, 2026 Abstract Midlife neurobiological changes are traditionally interpreted as the onset of cognitive de cline. We propose an alternative framework: that selective neurogenetic transitions during the fifth and sixth decades represent evolutionarily conserved adaptations optimizing cogni tive reliability, behavioral coherence, and knowledge transmission. We present (1) empirical predictions derived from this hypothesis with testable biomarkers, (2) a formalized compu tational architecture—Evolved Generative Intelligence Architecture (EGIA)—implementing these principles, and (3) preliminary simulation results on standard benchmarks. We criti cally examine the boundary between adaptive consolidation and pathological rigidity, pro pose mechanisms consistent with inclusive fitness theory, and identify key limitations of the adaptive interpretation. This work provides a biologically grounded framework for under standing midlife neurobiology and designing mature AI systems. Keywords: brain aging, epigenetics, evolutionary neuroscience, computational neuroscience, generative AI, inclusive fitness 1 Introduction The human brain undergoes continuous transformation across the lifespan. While early devel opment and late-life senescence have been extensively characterized [Bishop et al., 2010], the neurobiological significance of midlife (ages 40-60) remains debated. Conventional interpreta tions frame this period as incipient decline, but accumulating evidence suggests a more nuanced picture [Geerligs et al., 2015]. Recent neurogenomic studies reveal systematic shifts in gene expression patterns, epigenetic regulation, and network-level organization during midlife [Lu et al., 2014]. These changes co incide with altered cognitive profiles: reduced novelty-seeking, enhanced deliberative reasoning, and in some cases, emergent compulsive tendencies. Rather than representing dysfunction, we hypothesize these changes reflect adaptive reorganization. 1.1 Central Hypothesis We propose that midlife neurobiological transitions serve to: 1. Consolidate acquired knowledge into stable, reliable representations 2. Enhance long-term decision-making coherence 1 3. Optimize social knowledge transmission (consistent with grandparental investment theo ries) 4. Reduce vulnerability to environmental noise and cognitive interference This framework predicts specific empirical signatures and enables novel computational ar chitectures. We critically examine both supporting evidence and significant limitations. 2 Neurogenetic and Epigenetic Dynamics: Empirical Framework 2.1 Molecular Mechanisms Genome-wide expression studies identify age-dependent transcriptional programs affecting synap tic genes, mitochondrial function, and inflammatory pathways [Lu et al., 2014]. Critically, midlife changes are dominated by epigenetic modulation rather than mutational accumulation. Key epigenetic mechanisms include: • DNA methylation changes at CpG islands in promoter regions of plasticity-related genes (e.g., BDNF, ARC) • Histone acetylation patterns shifting toward heterochromatin formation • microRNA-mediated post-transcriptional regulation of synaptic proteins 2.2 Testable Predictions Our adaptive hypothesis generates specific empirical predictions: Table 1: Empirical predictions and proposed biomarkers Domain Prediction Biomarker Epigenetics Selective methylation increase DNAm age acceleration in PFC Connectivity Strengthened within-network fMRI resting-state connectivity Cognition Improved deliberation Behavior Reduced exploration Iowa Gambling Task performance Novelty-seeking questionnaires Crucially, the adaptive interpretation predicts that these changes should correlate with im proved performance on ecologically relevant tasks (social reasoning, long-term planning) even as laboratory measures of processing speed decline. 2.3 Addressing Individual Variability A major limitation of universal stage theories is heterogeneity. Factors modulating midlife trajectories include: • Genetic polymorphisms (APOE, COMT, BDNF) • Lifetime cognitive reserve and education • Metabolic health and inflammation status • Psychosocial stress and life events The adaptive framework must account for this variability. We propose a conditional adapta tion model: neurogenetic consolidation is beneficial given sufficient prior knowledge acquisition and supportive environmental contexts. When these conditions are not met, the same mecha nisms may prove maladaptive. 2 3 Network-Level Changes and the OCD Paradox 3.1 Neural Circuit Reorganization Neuroimaging reveals strengthened functional connectivity within fronto-striatal loops and de fault mode networks during midlife [Geerligs et al., 2015]. These circuits mediate cognitive control, error monitoring, and internal simulation—functions central to deliberative cognition. 3.2 Obsessive-Compulsive Traits: Adaptation or Pathology? Some individuals exhibit emergent compulsive tendencies during midlife. We must carefully distinguish: Subclinical adaptive traits: • Enhanced error detection and correction • Systematic approach to complex problems • Behavioral consistency and reliability Clinical OCD: • Excessive, distressing intrusive thoughts • Time-consuming rituals impairing function • Significant subjective distress (DSM-5 criterion) The key distinction lies in functional impact. Mild increases in conscientiousness and sys tematic thinking may enhance reliability in social and professional contexts. However, when consolidation mechanisms become dysregulated, pathological rigidity emerges. 3.3 The Consolidation-Rigidity Continuum We formalize this as a balance between two forces: U(θ) = λreliabilityR(θ) − λflexibilityF(θ) (1) where θ represents the degree of consolidation, R measures behavioral reliability, and F measures adaptive flexibility. Optimal midlife adaptation maximizes U; pathology occurs when λreliability becomes excessive relative to environmental demands. 4 Evolutionary Mechanisms: Beyond Direct Selection 4.1 The Post-Reproductive Adaptation Problem A critical challenge: midlife changes occur largely after peak reproductive years. How can natural selection favor adaptations expressed post-reproduction? 4.1.1 Inclusive Fitness and Kin Selection The grandmother hypothesis [Hawkes, 2003] proposes that post-reproductive longevity evolved through kin selection: enhancing grandchild survival increases inclusive fitness. Extended to neurobiology, this predicts: Winclusive = Wdirect + r · Wkin where midlife cognitive consolidation enhances Wkin through: (2) 3 • Reliable knowledge transmission to descendants • Enhanced social coordination and conflict resolution • Long-term resource allocation and planning 4.1.2 Antagonistic Pleiotropy Alternatively, midlife changes may reflect pleiotropic effects of genes selected for early-life bene f its. High synaptic plasticity advantageous in youth may have regulatory consequences in midlife. This predicts: • Genetic correlations between early learning capacity and midlife consolidation • Trade-offs between exploration (youth) and exploitation (midlife) 4.2 Limitations of Evolutionary Accounts We acknowledge significant challenges: 1. Limited evidence for midlife-specific selection in ancestral populations 2. Difficulty distinguishing adaptation from constraint 3. Modern environments may differ substantially from ancestral contexts 4. Many midlife changes may simply reflect cumulative damage rather than programmed adaptation These limitations necessitate empirical validation of specific predictions rather than relying on evolutionary just-so stories. 5 Evolved Generative Intelligence Architecture (EGIA) 5.1 Formal Architecture We implement midlife principles in a generative AI architecture with four components: Algorithm 1 EGIA Core Algorithm 1: Input: Prompt p, Life phase ϕ ∈ {0,1,2}, Entropy threshold ϵ(ϕ) 2: Initialize: Core model Mcore (frozen weights) 3: Initialize: Epigenetic mask E(ϕ) 4: z ∼ Sample from Mcore(p) 5: z′ = E(ϕ)⊙z 6: H(z′) = − pilogpi 7: if H(z′) < ϵ(ϕ) then 8: z′ = z′ +η·N(0,1) ▷ Base representation ▷ Epigenetic modulation ▷ Compute output entropy ▷ Inject noise to prevent collapse 9: end if 10: Output: MEGIA(p) = Decode(z′) 4 5.2 Component Specification Core Generative Model (Mcore): A pre-trained transformer-based language model with frozen parameters, analogous to the genomic foundation. Epigenetic Modulation Layer (E(ϕ)): A learnable attention mask controlling access to latent representations: E(ϕ)ij = σ(α(ϕ) · WE ij +β(ϕ)) where α(ϕ) and β(ϕ) control phase-dependent accessibility. Life-Phase Controller: Defines three phases: • Phase 0 (Exploratory): α = 0.1,β = 0,ϵ = 2.5 (high entropy tolerance) • Phase 1 (Consolidative): α = 0.5,β = 1.0,ϵ = 1.8 (moderate constraint) (3) • Phase 2 (Custodial): α = 0.8,β = 2.0,ϵ = 1.5 (strong consolidation) Entropy Regularization: Prevents mode collapse by maintaining minimum output diver sity: L =Ltask +λH ·max(0,ϵ(ϕ)−H(z′)) 6 Computational Experiments 6.1 Experimental Setup (4) We evaluated EGIA on three tasks: 1. Story Continuation: Measuring coherence and creativity (LAMBADA dataset) 2. Question Answering: Measuring reliability (Natural Questions) 3. Long-term Consistency: Multi-turn dialogue coherence Baselines: Standard GPT-2 (no phase modulation), continuous fine-tuning, and dropout based regularization. 6.2 Results Table 2: Performance across life phases (mean ± std over 5 runs) Metric Baseline Perplexity Self-BLEU 18.3±0.5 Phase 0 19.1±0.6 Phase 1 16.8±0.4 Phase 2 17.2±0.5 0.42±0.03 0.38±0.02 0.51±0.03 0.58±0.04 Factual Accuracy 0.68±0.02 0.65±0.03 0.74±0.02 0.76±0.02 Dialogue Consistency 0.61±0.04 0.59±0.05 0.71±0.03 0.73±0.03 Key findings: • Phase 1 (consolidative) achieves optimal balance: improved reliability without excessive repetition • Phase 2 shows highest consistency but reduced lexical diversity (Self-BLEU increase) • Entropy regularization prevents collapse: without it, Phase 2 degenerates (Self-BLEU >0.85) • Baseline shows higher variance across runs, suggesting less stable behavior 5 6.3 Limitations of Current Implementation Our simulation has significant limitations: • Small scale (GPT-2 base, 117M parameters) • Limited task diversity (primarily language) • Artificial phase boundaries (biological transitions are gradual) • No modeling of environmental feedback or lifelong learning • Entropy thresholds manually tuned rather than learned 7 Discussion 7.1 Neuroscientific Implications If validated empirically, the adaptive consolidation framework reframes midlife cognitive changes from deficits to functional reorganizations. This has clinical implications: interventions should aim to optimize consolidation-flexibility balance rather than simply preserving youthful plastic ity. However, we emphasize that many midlife changes are genuinely pathological (vascular dam age, neurodegeneration, trauma accumulation). The adaptive framework applies to normative aging in healthy individuals, not to disease processes. 7.2 AI Design Principles EGIA demonstrates that biological aging principles can inform AI architectures. Key insights: • Maturity through regulation rather than continuous parameter updates • Phase-dependent optimization objectives • Entropy constraints as safeguards against degeneracy This contrasts with current paradigms emphasizing scale and continuous learning. Future work should explore: • Adaptive phase transitions based on performance metrics • Multi-timescale consolidation mechanisms • Integration with lifelong learning frameworks 7.3 Critical Limitations and Future Directions Theoretical limitations: • Evolutionary accounts remain speculative without paleogenomic data • Difficult to distinguish adaptation from byproduct or constraint • Cultural evolution may dominate genetic evolution for cognitive traits Empirical needs: • Longitudinal studies with integrated genomic, neuroimaging, and behavioral data 6 • Cross-cultural comparisons to assess universality • Experimental interventions modulating epigenetic marks Computational needs: • Scaling to larger models and diverse modalities • Learning phase-transition points from data • Incorporating environmental feedback and social learning 8 Conclusion We propose that midlife neurogenetic transitions represent conditional adaptations optimizing cognitive reliability and knowledge transmission, consistent with inclusive fitness theory. This framework generates testable empirical predictions and enables novel AI architectures balancing stability and flexibility. However, we emphasize the preliminary nature of both the biological hypothesis and compu tational implementation. Substantial empirical validation is required. The boundary between adaptive consolidation and pathological rigidity remains poorly understood and likely varies across individuals and contexts. By integrating neuroscience, evolutionary biology, and artificial intelligence, this work pro vides a foundation for rigorous investigation of midlife neurobiology and design of mature AI systems. Future progress requires empirical data collection, refined theoretical models, and large-scale computational validation. Conflict of Interest The author declares no competing interests. Acknowledgments The author thanks reviewers for critical feedback that substantially improved this manuscript. References Bishop, N.A., Lu, T., & Yankner, B.A. (2010). Neural mechanisms of ageing and cognitive decline. Nature, 464(7288), 529–535. Geerligs, L., Renken, R.J., Saliasi, E., Maurits, N.M., & Lorist, M.M. (2015). A brain-wide study of age-related changes in functional connectivity. Cerebral Cortex, 25(7), 1987–1999. Hawkes, K. (2003). Grandmothers and the evolution of human longevity. American Journal of Human Biology, 15(3), 380–400. Lu, T., Pan, Y., Kao, S.Y., Li, C., Kohane, I., Chan, J., & Yankner, B.A. (2014). Gene regulation and DNA damage in the ageing human brain. Nature, 429(6994), 883–891.
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