A minimal dynamical model of human-AI cognitive coevolution
Abstract
The rapid deployment of generative AI raises the question of whether pervasive cognitive offloading drives a measurable, potentially irreversible decline in unassisted human reasoning. We examine this question with a phenomenological dynamical model based on the Cattell-Horn-Carroll distinction between fluid intelligence (Gf) and crystallized intelligence (Gc). Starting from the discrete-time Bivariate Latent Change Score formalism used in cognitive-aging research, we derive its continuous-time linear counterpart and introduce a fast third variable representing composite machine-intelligence capability. The machine variable enters the human subsystem through Hill-function modifications of the self-feedback and cross-coupling coefficients. We emphasize that our phenomenological model captures both the positive and negative effects of AI on human cognition. By formalizing the qualitative concepts of positive and negative impacts, we introduce definitions for AI-induced cognitive decline and AI-induced cognitive augmentation. A singular-perturbation reduction, justified via Tikhonov's theorem, collapses the resulting three-dimensional system onto a two-dimensional slow manifold. On this manifold we locate two bifurcations of the equilibrium. In one parameter regime, weakening the contribution of fluid reasoning to crystallized knowledge produces a saddle-node bifurcation that eliminates the stable positive equilibrium. In another, reversing the contribution of crystallized knowledge to fluid reasoning creates opposing feedback; increasing fluid self-feedback then reduces damping and produces a supercritical Hopf bifurcation with a stable periodic orbit. We discuss limitations and future directions, interpret our results as a phenomenological account of a coupled socio-technical system, and state the conditions under which each dynamical regime applies.
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BibTeX
@misc{jolakoski2026minimal,
title = {{A minimal dynamical model of human-AI cognitive coevolution}},
author = {Petar Jolakoski and Dimitar Trajanov and Qiyu Sun and Zhen Wu and Ljupco Kocarev},
howpublished = {arXiv},
year = {2026},
month = nov,
doi = {10.48550/arxiv.2611.06928},
eprint = {2611.06928},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2611.06928},
}
RIS
TY - UNPB
TI - A minimal dynamical model of human-AI cognitive coevolution
AU - Petar Jolakoski
AU - Dimitar Trajanov
AU - Qiyu Sun
AU - Zhen Wu
AU - Ljupco Kocarev
PY - 2026
DA - 2026/11/01
PB - arXiv
DO - 10.48550/arxiv.2611.06928
UR - https://arxiv.org/abs/2611.06928
AB - The rapid deployment of generative AI raises the question of whether pervasive cognitive offloading drives a measurable, potentially irreversible decline in unassisted human reasoning. We examine this question with a phenomenological dynamical model based on the Cattell-Horn-Carroll distinction between fluid intelligence (G_f) and crystallized intelligence (G_c). Starting from the discrete-time Bivariate Latent Change Score formalism used in cognitive-aging research, we derive its continuous-time linear counterpart and introduce a fast third variable representing composite machine-intelligence capability. The machine variable enters the human subsystem through Hill-function modifications of the self-feedback and cross-coupling coefficients. We emphasize that our phenomenological model captures both the positive and negative effects of AI on human cognition. By formalizing the qualitative concepts of positive and negative impacts, we introduce definitions for AI-induced cognitive decline and AI-induced cognitive augmentation. A singular-perturbation reduction, justified via Tikhonov's theorem, collapses the resulting three-dimensional system onto a two-dimensional slow manifold. On this manifold we locate two bifurcations of the equilibrium. In one parameter regime, weakening the contribution of fluid reasoning to crystallized knowledge produces a saddle-node bifurcation that eliminates the stable positive equilibrium. In another, reversing the contribution of crystallized knowledge to fluid reasoning creates opposing feedback; increasing fluid self-feedback then reduces damping and produces a supercritical Hopf bifurcation with a stable periodic orbit. We discuss limitations and future directions, interpret our results as a phenomenological account of a coupled socio-technical system, and state the conditions under which each dynamical regime applies.
ER -