AI Insight
This paper proposes "Cognitive Infrastructure Studies" (CIS) as a new interdisciplinary framework for understanding how AI systems fundamentally reshape human cognition at pre-conscious levels by acting as invisible infrastructures that determine what information is relevant and accessible. The authors argue that AI systems automate "relevance judgment" and shift epistemic agency from humans to algorithms through processes like anticipatory personalization and adaptive invisibility. The framework introduces "infrastructure breakdown methodologies" as experimental approaches to reveal cognitive dependencies by systematically removing AI preprocessing after users have become habituated to it.
Why it matters
This framework addresses critical gaps in understanding how AI influences human thinking, democratic deliberation, and knowledge formation across individual and societal scales. The proposed methodologies could enable researchers to measure and study the otherwise invisible cognitive effects of algorithmic curation and personalization systems that increasingly mediate human access to information.
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⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
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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.
Source: Toward a New Science of AI as Cognitive Infrastructure