Skip to content

Cognitive Atrophy versus Epistemic Augmentation: The Bifurcation of Human Agency and the Structural Imperative of Human-in-Command in Collaborative Intelligence

Oct 2026 · Zenodo (CERN European Organization for Nuclear Research)
Ethics and Social Impacts of AI Cybernetics and Technology in Society

Abstract

【Abstract】This strategic systems and epistemological monograph addresses the defining civilizational question of whether artificial intelligence induces cognitive atrophy or enables unprecedented intellectual augmentation. Rejecting simplistic technological determinism, the paper establishes that cognitive outcomes are governed by a fundamental bifurcation of human agency. Under the "Dependent Mode," users engage in uncritical cognitive offloading—surrendering dialectical inquiry, logical verification, and normative judgment to automated black boxes—resulting in severe deskilling, automation bias, and the atrophy of critical faculties. Conversely, under the "Sovereign Mode" (Human-in-Command), artificial intelligence is operationalized not as an authoritative oracle, but as an externalized dialectical catalyst within Clark and Chalmers' "Extended Mind" framework. In this collaborative intelligence topology, the human retains sovereign intentionality, posing structural hypotheses, testing boundary conditions, and evaluating geopolitical and historical coherence, while the machine executes ultra-high-speed combinatorial search and simulation. The monograph proves that genuine intellectual sovereignty requires maintaining rigorous physical-layer epistemic grounding and ethical accountability, transforming generative models from instruments of passive cognitive erosion into powerful scaffolds for expanding human insight.

View source

Similar papers

#artificial intelligence Conference Open access Jun 2018

Gibson Env: Real-World Perception for Embodied Agents

This paper investigates developing real-world perception for active agents, proposes Gibson Environment for this purpose, and showcases a set of perceptual tasks learned therein.

F. Xia, Amir Zamir, Zhi-Yang He et al. · 1,069 citations · ⚡112
#artificial intelligence Open access May 2023

Evaluating the Performance of Large Language Models on GAOKAO Benchmark

GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations...

Xiaotian Zhang, Chun-yan Li, Yi Zong et al. · 216 citations · ⚡17

KTO: Model Alignment as Prospect Theoretic Optimization

This work proposes a HALO that directly maximizes the utility of generations instead of maximizing the log-likelihood of preferences, and suggests that the best loss depends on the inductive biases most appropriate for a given setting, an oft-overlooked consideration.

Kawin Ethayarajh, Winnie Xu, Niklas Muennighoff et al. · 195 citations · ⚡28
#artificial intelligence Open access Jul 2024

Gender, Race, and Intersectional Bias in Resume Screening via Language Model Retrieval

This work investigates the possibilities of using LLMs in a resume screening setting via a document retrieval framework that simulates job candidate selection and finds that the MTEs are biased, significantly favoring White-associated names in 85% of cases and female-associated names in only 11.1% of cases.

Kyra Wilson, Aylin Caliskan · 131 citations · ⚡8
#artificial intelligence Review Nov 2024

How to Build a Quantum Supercomputer: Scaling from Hundreds to Millions of Qubits

This work shows that orders of magnitude enhancement in performance could be obtained by a combination of hardware improvements and tight quantum-HPC integration and introduces high-performance architectures for quantum-probabilistic computing with custom-designed accelerators to tackle today's industry-scale classical...

Masoud Mohseni, Artur Scherer, K. Johnson et al. · 121 citations · ⚡9

Related blog posts

Hugging Face Blog Oct 20, 2026

MPS in PyTorch 2.15

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.