arXiv cs.AI· Petar Jolakoski, Dimitar Trajanov, Qiyu Sun, Zhen Wu, Ljupco Kocarev·· 11 小时前AI 评分20
人类与 AI 认知协同演化的一类极简动力学模型
A minimal dynamical model of human-AI cognitive coevolution
AI 导读
研究者基于 Cattell-Horn-Carroll 的流体智力（G_f）与晶体智力（G_c）区分，构建了一个人类-AI 认知协同演化的唯象动力学模型，并引入代表机器智能综合能力的快速第三变量。
正文 · 原文
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 ($G_f$) 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.
| Subjects: | Neurons and Cognition (q-bio.NC); Artificial Intelligence (cs.AI); Adaptation and Self-Organizing Systems (nlin.AO); Physics and Society (physics.soc-ph) |
| Cite as: | arXiv:2611.06928 [q-bio.NC] |
| (or arXiv:2611.06928v1 [q-bio.NC] for this version) | |
| https://doi.org/10.48550/arXiv.2611.06928 arXiv-issued DOI via DataCite |
Submission history
From: Petar Jolakoski [view email]
[v1]
Sun, 20 Sep 2026 08:31:21 UTC (130 KB)
来源：arXiv cs.AI · arxiv.org