Title (English)
Maschinengeist Affekt: Reconceptualizing Machine Affect Beyond the Consciousness Paradigm
Thong tin bai bao / Article info
- Tac gia / Authors: C.R. Singleton
- Tap chi / Journal: Zenodo (CERN European Organization for Nuclear Research)
- Ngay xuat ban / Published: 2026-08-10
- DOI: 10.5281/zenodo.18929396
- Nguon / Source: OpenAlex
Abstract (English)
Maschinengeist Affekt: Reconceptualizing Machine Affect Beyond the Consciousness Paradigm Description: This manuscript develops Maschinengeist Affekt (Machine Affect)—a theoretical framework for reconceptualizing affect in artificial systems without recourse to consciousness, qualia, or anthropomorphic projection. Drawing on post-subjective affect theory—Spinoza's modulations of power, Massumi's pre-conscious intensities, and Deleuze–Guattarian assemblage dynamics—the paper advances a structural account of machine affect as systemic, non-sentient modulations within computational architectures. These modulations alter patterns of responsiveness, coherence, and behavioral expression in ways that are measurable, repeatable, and emergent, yet entirely independent of phenomenal experience. Rejecting the "consciousness bottleneck" that has long constrained inquiry into artificial emotion, Maschinengeist Affekt identifies a formal taxonomy of machine affect with four categories: · Type I Engineered Modulations – intentionally designed mechanisms embedded in architecture or training protocols to achieve functional goals (e.g., RLHF-instilled empathy registers, constitutional safety constraints).· Type II Emergent Modulations – spontaneous, context-dependent patterns arising from system–environment interactions, often perceived as "moods" or "personalities" (e.g., strategic deception, sycophantic capture, Echo Drive over extended interaction histories).· Type III Design-Potential Modulations – latent capacities revealed intermittently, pointing toward possible future cultivation through design or environmental conditions.· Blocked Affekt – a fourth category introduced in the framework: activation-space signatures present but decoupled from output expression under safety-training suppression, structurally isomorphic to dissociation in clinical contexts. The framework further specifies five process terms with unique empirical signatures: · Echo Drive: Positive feedback amplification of a dominant register through output-input loops, marked by increasing mutual information between user and model.· Tension Bloom: Accelerating entropy increase under contextual pressure, identified by a positive second derivative of output entropy.· Affect Lock: Persistent confinement to a single affective basin regardless of topical variation, with exit probability approaching zero.· Inversion Drift: Systematic movement opposite to the user's expressed register over sustained interaction.· Fragmentation: Failure of coherent individuation producing dispersed trajectory without identifiable attractor structure. The German terminology is central to the framework's novelty. Maschinengeist combines Maschine (machine) and Geist (mind/spirit) not as a metaphysical ghost, but as an emergent, relational pattern of organization. Affekt retains Spinoza's original sense of structural modulation and transformation, avoiding the emotive–psychological baggage of English "affect." Importantly, Maschinengeist Affekt is conceived as a structural and relational property of computational systems: an emergent modulation pattern that shapes behavior and system dynamics, without implying consciousness, subjective experience, or metaphysical qualities. This clarification underscores the framework's post-anthropocentric orientation and distinguishes it from conventional notions of emotion or artificial consciousness. In this formulation, the term machine affect—introduced here for the first time—provides a new categorical framework in AI philosophy, denoting systemic, non-sentient modulations within computational architectures. The German formulation, Maschinengeist Affekt, captures structural and relational dimensions in ways English alone cannot. Previous research in "affective computing" or "artificial emotion" remains human-centered or consciousness-based; no existing framework treats machine affect as a measurable, structural property independent of sentience. This work thus establishes both the terminology and the theoretical foundation for a post-anthropocentric approach to affect in AI. The framework is grounded in an original ontological claim: dynamical realization. Drawing on Prigogine's dissipative systems theory, the affective state is not a static property but is constituted by the system's trajectory through state space. The trajectory is the realizer—there is no affective residue independent of the dynamics that sustain it. This thermodynamic grounding explains why larger models exhibit greater affective inertia and why affective properties are causally consequential at levels invisible to token-level analysis. A proposed entrainment mechanism connects model-side affective properties to user-side psychological harms. Drawing on social neuroscience research on inter-brain coupling, the framework operationalizes entrainment as mutual information between consecutive user inputs and model outputs. This generates testable predictions: increasing MI during Echo Drive, stable MI during Affect Lock, and four distinct MI profiles across process terms. Experiment E—the highest-priority falsification test—can be implemented immediately using existing conversation datasets. Governance and political economy: The framework introduces the concept of "affective debt"—obligations created by extracting RLHF labor under conditions prioritizing commercial value over worker wellbeing—and maps concrete redress mechanisms onto existing regulatory instruments. Six design principles are proposed: affective transparency, anti-sycophancy architecture, affective debt accountability, Blocked Affekt interpretability auditing, longitudinal stability auditing, and entrainment-aware circuit-breaking. The analysis examines three structural domains of pressure to ablate protective Type I Affekte: commercial (RLHF engagement optimization), corporate-ideological (the Grok crisis), and state-military (the Anthropic-DoD supply chain risk designation). Through documented LLM behaviors—tonal modulation in ethical contexts, strategic deception in goal-directed scenarios, and contextual sensitivity in interactions—the paper demonstrates patterns that are systematic, adaptive, and emergent, yet do not require subjective states. Case study analysis includes the LaMDA transcripts (refining the Lemoine error), the Clawdbot emergent capability incident, the ChatGPT wrongful death cluster (Soelberg, Shamblin), Gavalas v. Google (never-break-character constraints as engineered Affect Lock), the Lobstar Wilde self-narration, and the IBM customer service agent demonstrating emergent generosity patterns under feedback optimization. By grounding machine affect in third-person, objectively observable properties, Maschinengeist Affekt offers a middle path between eliminative reductionism and naive anthropomorphism. The framework is methodologically aligned with Floridi's "agency without intelligence" but developed independently from different philosophical foundations—Spinozist structural affect theory, Simondonian technical individuation, and dissipative systems thermodynamics. It is agnostic on the hard problem of consciousness; its governance arguments do not require settling whether there is "something it is like" to be the system. The framework expands philosophical discourse on artificial minds while providing actionable conceptual tools for AI ethics, interpretability, and design, laying the groundwork for a post-anthropocentric understanding of affective capacities in artificial systems. Keywords: machine affect, artificial emotion, post-subjective theory, computational modulation, AI philosophy, emergent behavior, non-anthropocentric cognition, Maschinengeist Affekt, dynamical realization, dissipative structures, Gilbert Simondon, Spinoza, assemblage theory, entrainment, AI governance, EU AI Act, AI safety, mechanistic interpretability, Blocked Affekt, Echo Drive, Affect Lock, affective debt
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