System 0: AI, Cognition, and Determinism

• System 0 is a multifaceted concept defined as a pre-cognitive layer operating before deliberate thought in AI, embodied, and mathematical physics contexts.
• It underpins mechanisms such as anticipatory personalization and adaptive invisibility in AI while integrating passive dynamics and morphological computation in embodiment.
• In mathematical physics, System 0 refers to a deterministic integrable system derived from multiple radial SLE(0) flows, linking to classical Calogero–Sutherland dynamics.

Searching arXiv for the cited "System 0" papers and closely related work to ground the article. System 0 is a polysemous research term whose meaning depends on disciplinary context. In recent philosophy of AI and cognitive science, it denotes a distributed, AI-mediated layer that operates temporally prior to intuitive and deliberative cognition by filtering, ranking, framing, and pre-generating what thought encounters. In quad-process cognitive theory, it denotes pre-cognitive, embodied processes such as morphological computation and passive dynamics that occur below neural inference. In mathematical physics, it denotes the deterministic multiple radial $\mathrm{SLE}(0)$ system obtained as the classical limit of multiple radial $\mathrm{SLE}(\kappa)$ and identified with a special class of classical Calogero–Sutherland systems (Ganapini et al., 11 Jun 2026, Taniguchi et al., 8 Mar 2025, Makarov et al., 2024).

1. Definitions and disciplinary scope

The term acquired its contemporary AI-philosophical salience through work introduced by Chiriatti in 2021 and developed in subsequent papers, culminating in a formulation where System 0 is “a theoretically distinctive way to understand how contemporary AI systems have begun to operate ‘before you think,’” at a pre-reflective, pre-attentive layer that shapes what System 1 and System 2 will encounter and do (Ganapini et al., 11 Jun 2026). A closely related account defines it as the “emergent, algorithmic layer that precedes and conditions both intuitive (System 1) and deliberative (System 2) thinking,” functioning as a cognitive preprocessor that filters, ranks, nudges, and transforms information before human attention and reasoning engage it (Chiriatti et al., 17 Jun 2025).

A different lineage uses the same label in embodied cognition. There, System 0 is the “foundational layer of cognition comprising pre-cognitive, embodied processes that occur at super-fast timescales,” realized by direct physical interactions between body and environment and operating “without explicit computation,” including morphological computation and passive dynamics (Taniguchi et al., 8 Mar 2025). In mathematical physics, System 0 refers neither to AI nor to embodiment, but to the deterministic multiple radial $\mathrm{SLE}(0)$ system constructed from stationary relations and interpreted through quadratic differentials and Calogero–Sutherland dynamics (Makarov et al., 2024).

Usage	Core definition	Source
AI-mediated cognition	A distributed AI layer operating before Systems 1 and 2, pre-structuring the informational environment	(Ganapini et al., 11 Jun 2026, Chiriatti et al., 17 Jun 2025)
Quad-process embodied cognition	A pre-cognitive embodied layer based on morphology, passive dynamics, and physical interaction	(Taniguchi et al., 8 Mar 2025)
Mathematical physics	Deterministic multiple radial $\mathrm{SLE}(0)$ identified with a restricted classical Calogero–Sutherland system	(Makarov et al., 2024)

2. AI-mediated System 0 as a pre-reflective cognitive layer

In the AI-mediated account, the central claim is architectural: “AI is increasingly functioning as a cognitive layer that operates primarily in temporal terms before Systems 1 and 2 are engaged.” System 0 “consists of AI systems, aka data-driven processes that operate on user-specific behavioral data, pre-structure informational inputs before deliberation, and adapt continuously through feedback loops and personalization” (Ganapini et al., 11 Jun 2026). It is not a single tool or device. Rather, it is a distributed layer across AI services habitually relied on by an agent, bounded by the agent’s situation and interaction capacity rather than by the full computational extent of a platform.

This layer is analyzed at three levels. At the computational level, its signature property is Anticipatory Personalization: the system continuously learns from individual behavior and predicts and shapes future cognitive states rather than merely responding to explicit input. Empirical markers include behavioral data ingestion, continuous updating, pre-query content generation, and individually calibrated outputs. At the psychological level, its signature property is Adaptive Invisibility: increasingly sophisticated systems blend into cognitive workflows so seamlessly that their mediating role becomes difficult to detect. Markers include disorientation or capacity atrophy when supports are removed, as in GPS effects, and phenomenological blurring between one’s own outputs and AI-generated outputs. At the epistemic level, its signature property is Automation of Relevance Judgment: algorithmic ranking, framing, and recommendation decide what deserves attention before conscious engagement (Ganapini et al., 11 Jun 2026).

This model is explicitly linked to the extended mind tradition of Clark and Chalmers and to Heersmink’s multidimensional integration criteria. The “0” does not designate a full cognitive system in the sense of understanding or reasoning; it marks a pre-cognitive layer that shapes the conditions for thought. On this view, AI may be constitutively integrated into cognition without being “cognitive” in the full System 1 or System 2 sense. The result is a hybrid architecture in which search engines, recommenders, conversational agents, route planners, writing assistants, and wearable prompts modulate salience before reflection begins (Ganapini et al., 11 Jun 2026, Chiriatti et al., 17 Jun 2025).

3. Theoretical distinctiveness, mechanisms, and cognitive colonization

The distinctiveness claim in the recent philosophical literature is that System 0 captures a configuration that neither Tri-System Theory nor Thinkframes fully replicate. Tri-System Theory, associated with Shaw and Nave, models AI as an external advisor consulted in discrete episodes and is centered on “cognitive surrender.” Thinkframes, associated with Branda and Cassinadri, emphasizes AI-mediated cognitive ecologies and population-level convergence. By contrast, System 0 is said to be simultaneously constitutive of individual cognitive architecture and infrastructural at the epistemic layer, while operating below awareness and under external optimization objectives (Ganapini et al., 11 Jun 2026).

Its mechanisms are described as pre-attentive cues and framing, default settings and interface affordances, micro-interactions and predictive text, and adaptive interfaces driven by continuous behavioral feedback loops. Search feeds and AI Overviews curate answers before explicit querying. Route suggestions are experienced as “the way,” not merely as recommendations. Autocomplete and suggested replies create phenomenological continuity between AI prompts and authored content. Wearables deliver unsolicited interpretations such as “you are tired,” potentially displacing introspective access. These mechanisms alter priors, salience, choice architecture, and epistemic uptake before reflective endorsement is even in play (Ganapini et al., 11 Jun 2026).

The most original extension of this line of work is Cognitive Colonization, defined as “the incorporation of externally designed optimization objectives into the architecture of the self, such that the agent’s pre-reflective processing has been shaped by interests that are not the agent’s own.” The proposed diagnostic profile requires four jointly satisfied conditions: constitutive integration with downstream incorporation, exogenous directional governance or authorship, opacity, and misalignment with reflective endorsement. Colonization is distinguished from deference and manipulation because it operates “from within the cognitive system, below the threshold of reflective awareness, and [is] experienced as native” (Ganapini et al., 11 Jun 2026).

The reported consequences span individual and collective levels. Individually, System 0 increases adoption of AI outputs, reduces independent verification, can export bias into unassisted contexts, narrows expressive vocabulary and creative range, and accumulates “cognitive debt” in writing tasks. Collectively, individually calibrated processes aggregate into homogenization of interpretive frameworks, regression toward mean creative outputs, epistemic bubbles, and polarization through homophilic feeds. Normatively, the concern is that a constitutive layer may be functionally aligned in the sense that it works smoothly for the user while remaining normatively misaligned because it serves engagement maximization or other external criteria rather than the user’s reflectively endorsable aims (Ganapini et al., 11 Jun 2026).

4. Design, governance, and human–AI cognitive integration

A more design-oriented account treats System 0 less as a diagnosis of colonization than as a framework for turning AI into a cognitive extension while preserving agency. It argues that contemporary AI satisfies major integration criteria associated with the extended mind hypothesis: information flow, reliability, durability, trust, procedural transparency, informational transparency, and individualization. At the same time, it emphasizes a paradox: AI both extends and constrains cognition, expanding access and speed while narrowing thought through sycophancy and bias amplification (Chiriatti et al., 17 Jun 2025).

This literature proposes seven evidence-based frameworks for effective integration: Enhanced Cognitive Scaffolding, Symbiotic Division of Cognitive Labor, Dialectical Cognitive Enhancement, Agentic Transparency and Control, Expertise Democratization, Social-Emotional Augmentation, and Duration-Optimized Integration. Their common orientation is to treat AI as upstream cognitive infrastructure while retaining human primacy in goal-setting, evaluation, causal reasoning, and authorship. The paper associates these frameworks with reported empirical outcomes including higher-order thinking gains of $g \sim 0.457$, learning perception of $g = 0.456$, synergistic gains in creation tasks of $g = 0.46$, strong gains in problem-based learning of $g = 1.113$, and strongest effects over 4–8 weeks at $g \sim 0.999$ (Chiriatti et al., 17 Jun 2025).

The governance implications overlap substantially with the colonization-focused account. Both literatures argue for making invisible pre-processing visible, exposing optimization criteria and directional governance, requiring inspectability of what systems optimize for, diversifying information ecosystems, and training epistemic vigilance while recognizing that vigilance alone is insufficient when the operative mechanism is constitutive and pre-reflective (Ganapini et al., 11 Jun 2026, Chiriatti et al., 17 Jun 2025). A plausible implication is that the integration-focused and colonization-focused approaches are not contradictory but address different failure modes of the same infrastructural transformation.

5. Embodied System 0 in quad-process theory

In quad-process theory, System 0 is not an AI-mediated informational layer but the pre-representational substrate of embodied adaptation. It comprises super-fast physical and dynamical processes realized by direct interactions between organism or robot body and environment. These processes are “pre-cognitive,” “pre-representational,” and “operate without explicit computation,” often by leveraging morphology, materials, gravity, inertia, friction, elasticity, and compliance to generate adaptive behavior (Taniguchi et al., 8 Mar 2025).

This account places System 0 within a Bergsonian multi-timescale framework. System 0 occupies the fastest band; System 1 corresponds to fast intuitive processes, System 2 to slow deliberative processes, and System 3 to super-slow collective dynamics and symbol emergence. The paper associates the “psychological present” with around 3–5 seconds, with lower perceptual temporal resolution near approximately 2 milliseconds, and locates System 0 largely beneath conscious temporal access. It further introduces canalization across physical law, evolutionary morphology, individual learning, and societal norms, with System 0 anchored by the fastest canalizations of physics and embodiment (Taniguchi et al., 8 Mar 2025).

Its exemplary mechanisms are passive dynamics, morphological computation, compliance and elasticity, and physical reservoir computing. Canonical demonstrations include the passive dynamic walker, which walks down a slope “without an actuator or computer”; soft grippers, where passive deformation encloses objects robustly under the same motor signal; elastic hopping robots, where elastic elements yield a stable hopping attractor; peg insertion with a soft wrist, where passive deformation accommodates contact constraints; and biological preflexes, where muscle–tendon mechanics respond faster than neural reflex arcs. Physical reservoir computing examples include octopus-like soft arms, soft fingers for haptic identification, flapping-wing robots whose bending patterns classify wind direction, and soft arms stirring water to infer object location from motion patterns in altered flow fields (Taniguchi et al., 8 Mar 2025).

The paper does not introduce new equations or a unified quantitative metric for morphological computation. Its formal mathematics appears instead at the System 3 level in collective predictive coding, where the observations $o$ and actions $\mathrm{SLE}(\kappa)$0 entering the generative and inference models are said to be generated through the physical interactions of System 0 with the environment. System 0 therefore scaffolds higher systems by pre-structuring the sensorimotor stream, reducing control complexity, and constraining what symbols and collective regularities can emerge (Taniguchi et al., 8 Mar 2025).

6. Deterministic System 0 in mathematical physics

In mathematical physics, System 0 denotes the deterministic multiple radial $\mathrm{SLE}(\kappa)$1 system, obtained as the classical limit of random multiple radial $\mathrm{SLE}(\kappa)$2 as $\mathrm{SLE}(\kappa)$3. The construction proceeds through partition functions, stationary relations, and a master function associated with trigonometric Knizhnik–Zamolodchikov equations. The critical points of the master function determine admissible deterministic systems and an equivalence class of residue-free quadratic differentials whose horizontal trajectories are precisely the traces of the multiple radial $\mathrm{SLE}(\kappa)$4 flow (Makarov et al., 2024).

The formal structure is explicitly geometric. For distinct boundary points $\mathrm{SLE}(\kappa)$5, the class $\mathrm{SLE}(\kappa)$6 consists of quadratic differentials with involution symmetry, double zeros at each $\mathrm{SLE}(\kappa)$7, fourth-order residue-free poles at screening charges $\mathrm{SLE}(\kappa)$8, and poles at $\mathrm{SLE}(\kappa)$9 and $\mathrm{SLE}(0)$0 of order $\mathrm{SLE}(0)$1. The residue-free condition is equivalent to the stationary relations. The main geometric theorem states that if the poles are involution-symmetric and satisfy the stationary relations, then the Loewner hulls generated by the multiple radial $\mathrm{SLE}(0)$2 flow are contained in the horizontal trajectories of some $\mathrm{SLE}(0)$3, with limiting ends at the boundary zeros (Makarov et al., 2024).

Under a common capacity parametrization $\mathrm{SLE}(0)$4, the boundary-angle dynamics close and match a classical Calogero–Sutherland system on the circle. With momenta

$\mathrm{SLE}(0)$5

the Hamiltonian becomes

$\mathrm{SLE}(0)$6

and the Newton equations are

$\mathrm{SLE}(0)$7

The paper further shows that the “null vector Hamiltonians” have Poisson brackets

$\mathrm{SLE}(0)$8

so the corresponding Hamiltonian vector fields commute along the submanifolds where the $\mathrm{SLE}(0)$9 are equal. In this setting, System 0 is thus a precisely defined deterministic integrable system rather than a cognitive or philosophical concept (Makarov et al., 2024).