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

• URL: http://arxiv.org/abs/2507.22893v2
• Date: Wed, 27 Aug 2025 14:58:29 GMT
• Title: Invisible Architectures of Thought: Toward a New Science of AI as Cognitive Infrastructure
• Authors: Giuseppe Riva,
• Abstract summary: "Cognitive Infrastructure Studies" (CIS) is a new interdisciplinary domain to reconceptualize AI as "cognitive infrastructures"<n>CIS aims to address how AI preprocessing reshapes distributed cognition across individual, collective, and cultural scales.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

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