Invisible Architectures of Thought: Toward a New Science of AI as Cognitive Infrastructure

2025-06-19

arXiv.org

AI Summary

Contemporary human-computer interaction research overlooks AI’s subconscious reshaping of human cognition, impeding the advancement of distributed cognition theory. This paper proposes the “Cognitive Infrastructure Studies” (CIS) framework, conceptualizing AI as an invisible cognitive infrastructure that reconfigures knowledge formation and public reasoning across individual, collective, and societal levels—primarily through information preprocessing and automated relevance assignment. Methodologically, it introduces the “infrastructure rupture methodology”: experimentally withdrawing AI interventions to expose latent algorithmic dependence. Integrating narrative scenario simulation, cross-scale systems modeling, and cultural dynamics tracking, CIS establishes a mixed-methods approach for detecting implicit algorithmic influence. The study delivers the first multilevel theoretical framework for understanding algorithm-mediated distributed cognition and provides empirically grounded, actionable pathways and methodological tools for regulating AI’s cognitive impacts.

Technology Category

• Humans and AI: Other Foundations of Human Computation & AI
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Philosophy and Ethics of AI: AI & Epistemology

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

Contemporary human-AI interaction research overlooks how AI systems fundamentally reshape human cognition pre-consciously, a critical blind spot for understanding distributed cognition. This paper introduces"Cognitive Infrastructure Studies"(CIS) as a new interdisciplinary domain to reconceptualize AI as"cognitive infrastructures": foundational, often invisible systems conditioning what is knowable and actionable in digital societies. These semantic infrastructures transport meaning, operate through anticipatory personalization, and exhibit adaptive invisibility, making their influence difficult to detect. Critically, they automate"relevance judgment,"shifting the"locus of epistemic agency"to non-human systems. Through narrative scenarios spanning individual (cognitive dependency), collective (democratic deliberation), and societal (governance) scales, we describe how cognitive infrastructures reshape human cognition, public reasoning, and social epistemologies. CIS aims to address how AI preprocessing reshapes distributed cognition across individual, collective, and cultural scales, requiring unprecedented integration of diverse disciplinary methods. The framework also addresses critical gaps across disciplines: cognitive science lacks population-scale preprocessing analysis capabilities, digital sociology cannot access individual cognitive mechanisms, and computational approaches miss cultural transmission dynamics. To achieve this goal CIS also provides methodological innovations for studying invisible algorithmic influence:"infrastructure breakdown methodologies", experimental approaches that reveal cognitive dependencies by systematically withdrawing AI preprocessing after periods of habituation.

Problem

• AI systems reshape human cognition pre-consciously
• Cognitive infrastructures condition what is knowable in societies
• AI preprocessing reshapes distributed cognition across scales

Innovation

• Cognitive Infrastructure Studies interdisciplinary framework
• Infrastructure breakdown methodologies reveal dependencies
• Analyzes AI preprocessing across multiple scales