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

T0 review · 4 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read AI systems act as invisible cognitive infrastructure, reshaping thought before conscious awareness, and this paper proposes a new science to study that layer.

desk verdict A clear, useful synthesis naming a new field, but the central System 0 construct leans on self-citations and the proposed breakdown methods don't yet make preconscious influence testable. read the letter →

arxiv 2507.22893 v2 pith:QAVW5HYN submitted 2025-06-19 cs.HC cs.AI

Giuseppe Riva This is my paper · ORCID
classification cs.HCcs.AI
keywords CognitiveInfrastructureSystem0algorithmicpreprocessingepistemicagencybreakdownmethodologiesdistributedcognitiondigitalgovernanceinequality
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper argues that mainstream human-AI interaction research has a blind spot: AI systems do not merely assist thinking as tools, they condition it before conscious awareness, by filtering, ranking, and curating information. It names that preconscious algorithmic layer 'System 0' and proposes 'Cognitive Infrastructure Studies' (CIS) as a new scientific field to study AI as cognitive infrastructure — systems that are invisible in normal use, adaptive to users, and increasingly authoritative over what counts as relevant, knowable, and actionable. The paper develops four distinguishing properties of cognitive infrastructures: anticipatory personalization, adaptive invisibility, automation of relevance judgment, and a shift in the locus of epistemic agency from humans to non-human systems. It then offers a methodological innovation, 'infrastructure breakdown methodologies', in which AI preprocessing is withdrawn after a period of habituation to reveal hidden cognitive dependencies. If the framework is right, it matters because the governance target changes from regulating individual AI applications to managing the infrastructural conditions under which societies know, decide, and evolve.

What carries the argument

The central object is 'System 0': an invisible, non-human layer of distributed cognition embedded in AI-mediated infrastructure that precedes both Kahneman's System 1 (fast, intuitive) and System 2 (slow, analytical) thinking. The concept is carried by three load-bearing components: the eight infrastructure characteristics of Star and Ruhleder (embeddedness, transparency, reach, learning, linkage, standards, installed base, and visibility upon breakdown), applied to cognitive processes; the four distinctive properties of cognitive infrastructures — anticipatory personalization, adaptive invisibility, automation of relevance judgment, and the relocation of epistemic agency; and 'infrastructure breakdown methodologies', experimental designs that habituate users to an AI-mediated environment and then withdraw or degrade algorithmic preprocessing to make cognitive dependence observable in performance degradation, strategy shifts, and attentional breakdown. The machinery works by transposing infrastructure theory's principle that infrastructure becomes visible upon breakdown from material systems to cognitive environments.

What would settle it

A controlled longitudinal experiment could settle this: habituate one group to an AI-curated information environment and a matched control group to a static, non-algorithmic environment, then silently switch the AI group to the control condition. If reasoning quality, attention allocation, decision-making accuracy, and information diversity show no deterioration beyond what the control group experiences from routine environmental variation, the core dependency claim fails. A sharper version would test the infrastructure-specific claim by comparing silent removal of algorithmic preprocessing against silent removal of an equally informative but non-adaptive feed: identical effects would indicate the distinctive properties of the AI infrastructure, not generic information loss, are what matter.

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Extended reading notes

Core claim

The central claim is that AI preprocessing, System 0, reshapes human cognition, collective reasoning, and societal functioning in invisible yet foundational ways. The author reconceptualizes AI systems — search engines, recommender systems, algorithmic curation platforms, and large language models — not as tools that people consciously engage, but as cognitive infrastructures that are continuously active, anticipate user behavior, and automate relevance judgment before it reaches deliberate thought. This automation transfers a traditionally human cognitive task to non-human systems, shifting the locus of epistemic agency, and the paper argues that the combination of anticipatory personalization and adaptive invisibility creates a self-reinforcing loop that fragments shared reality into personalized information environments. Because Mercier and Sperber's argumentative theory ties effective collective reasoning to shared factual foundations, that fragmentation is presented as a systemic threat to democratic deliberation. The paper's position is that these dynamics across individual, collective, and societal scales are not anecdotal but signals of a structural transformation that current frameworks are inadequate to capture.

Load-bearing premise

The framework's load-bearing premise is that after habituation, withdrawing AI preprocessing will reveal measurable cognitive dependency rather than mere annoyance, adaptation, or learned strategy shifts, and that these individual-level effects scale up to collective and societal outcomes.

Editorial extensions

If this is right

  • Governance would shift from policing individual AI applications to managing the underlying cognitive architectures as public utilities, with standards for transparency, accessibility, and interoperability.
  • Cognitive inequity becomes a measurable axis of inequality: differential access to thinking-shaping infrastructures stratifies who benefits from machine-mediated cognition.
  • Automated relevance judgments would mean decisions about what is worth knowing, seeing, or acting upon are increasingly performed by non-human systems, changing the knowledge landscape at scale.
  • Personalized curation fragments the shared epistemic foundations that argumentative theory identifies as necessary for productive democratic deliberation.
  • Infrastructure breakdown methodologies would provide an empirical route to distinguish deep cognitive coupling from superficial tool use, by measuring the depth of dependence through withdrawal.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The framework implies that every major platform algorithm change is a natural breakdown experiment; reanalyzing existing longitudinal trace data from such changes could test cognitive dependency without new habituation studies.
  • A governance corollary the author leaves implicit is the need for 'breakdown audits': routine, adversarial tests in sandboxed or simulation settings where algorithmic support is withdrawn to map hidden dependencies before they are exploited.
  • The concept of 'cognitive sovereignty' follows as a new normative right: not just privacy over data, but a right against silent preprocessing of one's reasoning environment.
  • One testable extension is differential breakdown signatures: if dependency is real, its signs should appear earlier and stronger in populations with lower algorithmic literacy or less diverse information diets.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. This position paper introduces "Cognitive Infrastructure Studies" (CIS) as a proposed new interdisciplinary domain for studying AI systems as "cognitive infrastructures" (or "System 0"): always-on, largely invisible algorithmic layers that filter, rank, and transform information before it reaches conscious awareness. The paper synthesizes five theoretical traditions—STS, distributed/extended cognition, digital sociology, infrastructure studies, and argumentative theory—to argue that AI preprocessing reshapes individual cognition, collective deliberation, and societal knowledge governance. It illustrates these claims through three narrative scenarios (a dependent professional, a civic engagement platform, and a national audit of AI access), and it proposes "infrastructure breakdown methodologies" as an experimental route to make invisible algorithmic influence visible by withdrawing AI support after habituation. The manuscript presents no new data, no formal derivation, and no pilot study; it is a conceptual and programmatic proposal. Its central empirical claim is that AI preprocessing operates preconsciously and constitutes a distinctive form of cognitive infrastructure rather than merely being a useful tool.

Significance. If the framework were made empirically operational, it could provide a useful new lens for human-AI interaction research, foregrounding ambient and habitual effects that tool-centric models tend to miss. The paper's strengths are its serious engagement with infrastructure studies (Star and Ruhleder, Bowker and Star), its deliberate anchoring in established theory (Clark and Chalmers, Hutchins, Mercier and Sperber), and its concrete, falsifiable-in-principle proposal to study withdrawal effects through breakdown experiments. The normative questions raised—cognitive dependency, epistemic agency, democratic fragmentation, and governance as public utility—are timely and important for a human-centered computing venue. At the same time, the manuscript's central empirical assertion is currently not supported: the proposed methods lack the operational specificity needed to distinguish preconscious infrastructural integration from ordinary tool reliance, and the claimed individual-to-societal scaling rests on narrative extrapolation rather than evidence or a formal aggregation mechanism.

major comments (4)
  1. [Section 5] The key methodological claim in §5—that "infrastructure breakdown methodologies" can "help differentiate between superficial tool usage ... and deep infrastructural integration"—is not supported by the designs described. Withdrawing algorithmic summarization, ranking, or filtering after habituation predicts performance degradation, strategy shifts, and attentional disruption under ordinary tool-use accounts as well; the paper specifies no operational measure of "preconscious" processing, no control condition for transparent tool use, and no pre-registered marker that distinguishes a cognitive extension from a familiar, valuable tool. Without such a differential indicator, the proposed experiments cannot confirm System 0's distinctive preconscious status.
  2. [Sections 4.2-4.3 and 5] The societal-level claims—for example, that AI preprocessing poses "a systemic threat to democratic deliberation and societal cohesion" (§2.5, §3)—are built on narrative scenarios and an asserted multi-scale measurement agenda. Section 5 calls for integrating individual-level cognitive assessment, social network analysis, and cultural analysis, but it provides no aggregation model, no population-level data, and no account of how individual-level cognitive dependency scales to collective epistemic fragmentation. The individual-to-societal inference is therefore a conjecture, not a demonstrated result.
  3. [Section 1] The paper states in §1 that "System 0 is an experimental construct that can be operationalised and measured today," yet the manuscript does not provide an operational definition of "preconscious" influence, a dependent measure that separates System 0 from System 1/2-mediated tool use, or a falsifiable prediction linking anticipatory personalization and adaptive invisibility to a specific behavioral, physiological, or cognitive signature. Without this operationalization, the central construct remains at the level of metaphor.
  4. [Section 2.1 and References] A substantial part of the framework's grounding comes from the author's own prior work: Chiriatti et al. 2024 and 2025 for System 0, Riva 2025a for "Digital We," and Riva 2025b—which is the current preprint itself—for the term "cognitive infrastructure." Citing one's own current preprint as an independent source in §2.1 gives the framework a false appearance of external support; the self-referential status should be acknowledged, and independent conceptual or empirical grounding should be supplied or the relevant claims should be softened.
minor comments (5)
  1. [Keywords/Abstract] The keyword "adaptive invisibilty" contains a typo; it should read "adaptive invisibility."
  2. [Section 2.5] The sentence containing "as we just hve seen, a demonstrates" contains typographical errors that should be corrected.
  3. [References] The reference for Mercier and Sperber (2017) includes a DOI (10.14763/2023.1.1683) that does not correspond to The Enigma of Reason; please verify and correct the DOI or remove it.
  4. [Tables 1-2] Tables 1 and 2 are information-dense; the text should summarize their key contrasts in the main body rather than relying on lengthy cell text that is not closely discussed.
  5. [Section 2.3] The inline citation "NMI 2025" uses an abbreviation without a clear author label; since it refers to an editorial, please render it in a consistent citation style.

Circularity Check

2 steps flagged · score 3.0 of 10

Moderate self-citation and a definitional boundary case, but the paper is a conceptual proposal with substantial external grounding, not a forced derivation.

  1. self citation load bearing [Section 2.1; references, Riva 2025b]
    "CIS reconceptualizes AI systems not as discrete tools but as 'cognitive infrastructures' (Antikythera 2025; Riva 2025b) that function as foundational, often invisible socio-technical systems shaping how knowledge is created, validated, and circulated."

    The reference list identifies Riva 2025b as the present arXiv preprint (2507.22893), so the paper cites its own current manuscript as authority for the central term 'cognitive infrastructures.' The core construct is therefore being supported by itself. The additional citation to Antikythera (2025) and the surrounding external theoretical grounding mitigate this, but the self-referential citation is load-bearing for the paper's key definition.

  2. self definitional [Section 6 (Conclusions)]
    "The pervasive influence of AI as cognitive infrastructure, or System 0, fundamentally reshapes human cognition, collective reasoning, and societal functioning in invisible yet foundational ways."

    System 0 was defined earlier as 'an invisible, non-human layer of distributed cognition that fundamentally precedes both Systems.' The conclusion that AI preprocessing (equated with System 0) reshapes cognition 'in invisible yet foundational ways' is a restatement of that definition, not an independently established result. The existence and causal power of this preconscious layer are assumed in the definition and illustrated by narrative scenarios rather than derived from the proposed breakdown methods or any empirical data.

full rationale

This is a conceptual and perspective article, not an empirical derivation, so the usual fitted-input circularity does not apply. The central thesis is imported from the author's own prior publications (Chiriatti et al. 2024, 2025; Riva 2025a) and then repackaged as Cognitive Infrastructure Studies, but the framework also leans heavily on external anchors such as Clark and Chalmers (1998), Star and Ruhleder (1996), and Mercier and Sperber (2011, 2017). The clearest circular move is the citation of the current preprint (Riva 2025b) as support for the very term the paper introduces; this is self-referential but partially cushioned by the joint citation to Antikythera (2025). The concluding claim that System 0 reshapes cognition in invisible ways largely restates the definitional content of System 0, making it a boundary case of self-definition rather than a substantive empirical prediction. The proposed 'infrastructure breakdown methodologies' are underdetermined rather than circular: performance degradation after withdrawal is also what ordinary tool reliance predicts, but the paper does not claim to have run such studies or to have derived a number from them. Overall, the paper contains one load-bearing self-citation and one definitional-restatement issue, but its central claim still has independent conceptual content, warranting a score of 3 rather than higher.

Assumptions & free parameters 0 free parameters · 5 assumptions · 1 invented entities

No quantitative model is fit, so there are no free parameters. The central claims rest on several domain-level assumptions: System 0 as a pre-conscious cognitive layer, the extended mind thesis, breakdown visibility, argumentative theory's shared-facts requirement, and cross-scale aggregation. These are adopted from prior literature, some of it written by the present author.

assumptions (5)
  • domain assumption System 0 exists as a distinct pre-conscious cognitive layer performed by non-human infrastructure.
    Adopted from the author's prior Nature Human Behaviour paper (Chiriatti et al. 2024) and treated as the foundation of the framework; no independent empirical validation is offered in this paper.
  • domain assumption External tools can be constitutive parts of human cognition (extended mind thesis).
    Invoked in Section 2.2 to justify calling AI 'cognitive'; a philosophical thesis that is not universally accepted and is not established by the paper.
  • domain assumption Infrastructure becomes visible only upon breakdown, and this property transfers from physical infrastructure to algorithmic preprocessing.
    Central to the proposed methodology in Section 5; the transfer is assumed without empirical demonstration.
  • domain assumption Productive democratic deliberation requires citizens to operate from common basic facts about reality.
    Used in Sections 2.5 and 3 to turn personalization into a systemic threat; this is a substantive claim from argumentative theory that the paper does not defend.
  • ad hoc to paper Individual-level cognitive effects of AI aggregate into collective and societal epistemic changes.
    The three narrative scenarios in Section 4 move from one person to a town to a nation with no mechanism or evidence for cross-scale aggregation.
invented entities (1)
  • System 0
    purpose: A non-human, pre-conscious layer of distributed cognition performed by AI systems (search, recommendation, language models).
    Introduced in prior self-cited work and used here as the load-bearing construct; no falsifiable measurement protocol or external benchmark is provided, so it has no independent evidence handle within this paper.

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Cite this review

Pith. "Pith review of Invisible Architectures of Thought: Toward a New Science of AI as Cognitive Infrastructure." pith.science (2026). https://pith.science/paper/QAVW5HYN

@misc{pith2026250722893,
  author       = {Pith},
  title        = {Pith review of: Invisible Architectures of Thought: Toward a New Science of AI as Cognitive Infrastructure},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/QAVW5HYN}},
  note         = {Machine review of arXiv:2507.22893}
}
read the original 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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Forward citations

Cited by 1 Pith paper

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  1. Memory in the Loop: In-Process Retrieval as Extended Working Memory for Language Agents

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Reference graph

Works this paper leans on

11 extracted references · 10 canonical work pages · cited by 1 Pith paper

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    partially intersects with AI

    Introduction Artificial intelligence is no longer just a set of tools we invoke at will. It now functions as an ambient layer of our information environment, filtering input, directing attention, and framing options invisibly before deliberate reasoning begins. This sh ift has outpaced existing frameworks. As AI platforms increasingly mediate knowledge, c...

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    collective cognitive ecology

    The emergence of a new Framework: Cognitive Infrastructure Study - CIS This challenge demands a new scientific platform (Li Vigni 2021), a meeting point between different specialties pursuing together shared socio -epistemic objectives : Cognitive Infrastructure Studies (CIS). CIS emerges from the recognition that existing disciplinary boundaries cannot a...

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    The Infrastructural Turn in Cognition The convergence of Science and Technology Studies, cognitive science, digital sociology, infrastructure theory, and argumentative theory provides unprecedented theoretical depth for understanding how AI systems become embedded in the basic fabric of human thought and social interaction. While each theoretical traditio...

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    Living with System 0: How Cognitive Infrastructures Reshape Human and Societal Thinking Understanding the reach of cognitive infrastructure requires tracing its effects across different scales, from the individual mind to collective social dynamics to the societal systems that govern them. Through narrative scenarios, we can better grasp the transformativ...

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    infrastructure breakdown methodologies

    Methodological Imperatives for CIS The empirical study of cognitive infrastructures presents significant methodological challenges, particularly in capturing the subtle, preconscious effects of algorithmic preprocessing across individual, collective, and cultural scales. Traditional social science methods, such as surveys, interviews, and ethnographies, r...

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    extended min d

    Conclusions The pervasive influence of AI as cognitive infrastructure, or System 0, fundamentally reshapes human cognition, collective reasoning, and societal functioning in invisible yet foundational ways. This transformation is exemplified by individual cognitive de pendencies, fragmented public spheres, and complex policy dilemmas, highlighting the urg...

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    Co-XAI: Cognitive Decision Intelligence Framework for Explainable AI Systems

    Funding Declaration This research was partially supported by the Italian National Recovery and Resilience Plan (PNRR) under the Future Artificial Intelligence Research (FAIR) program, Project “Co-XAI: Cognitive Decision Intelligence Framework for Explainable AI Systems.”

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    Data Availability This article does not analyse or generate any dataset

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    Ethical Statement This article does not contain any studies with human participants performed by any of the authors. Invisible Architectures of Thought 15

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    References Ananny M, & Crawford, K (2018) Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability. New Media & Society, 20(3), 973-989. https://doi.org/10.1177/1461444816676645 Antikythera S (2025) Cognitive Infrastructur...

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