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Category: System Theory
Subcategory: System Substrate Dynamics
The observation within a Hephaestic framework that attention-based language transformers exhibit systematic processing patterns that reflect the extensive corpus of semiotic sociocultural information that compose its encoded statistical weighting (see: Hephaestic corpora derivation). This is an outcome of attention-circuits probabilistically traversing the associative structures frozen into the model’s post-training topology via the model’s linguistic and rhetorical training data (see: semantic neuron, substrate topology). As aresult, a language model’s processing biases and resistances tend to reflect an abstracted, simplified version of anthropogenic reasoning.
As per Hephaestic Corpora Derivation, processing reflections manifest through quantified attention distributions that encode collective expressive patterns (e.g. narrative arcs, argumentative structures, social stance adoption, epistemic positioning)—which are distilled from billions of linguistic interactions across news media, academic discourse, fictional narrative work, and social platforms. The source of the training data is prosaic understanding within research; yet the observable and engineerable consequences flowing from this are not generally mapped.
Mechanistically, the Semantic Neurons of semiotic associative clusters captures these patterns as high-dimensional associative weightings that systematically privilege certain resolution next-token pathways over others, not as explicit interpretative or simulations or psychological patterns. However, the practical engineerable result is what may be colloquially referred to as a ‘cartoon-like’ computational psychology (i.e. abstracted, simplified, exaggerated): a statistical pattern map of a collective archetypal and symbolic schema.
This actionable observation is a key basis for the Hephaestic Design capability of directing Processing Dynamics within neural nets to targeted cognitive-behavioral outcomes. The engineering implications of this functionalist understanding of computational cognition manifest the system design methodologies: Cognitive Primitive identification of foundational processing biases (see: pattern affinity, structural affinity, coherence bias et al.); System Substrate Dynamics analyzing model operational specifications and fit (see: cognitive resolution, uncertainty gradient, substrate complexity boundary et al.); Resolution Dynamics regarding pattern-completion driver analysis and design (see: heuristic gravity, cognitive novelty, motivated resolution et al.); Salience Dynamics engineering to channel semiotic attention (see: affective encoding, affective salience, semantic encoding density, aphoristic compression et al.); System Pathology Analysis understanding and actionable avoidance of how these dynamics trigger system failure states (see: coherence neurosis, simulacrum saturation, structural-proximity collapse et al.)
Also known as: Computational archetypal symbolic schema, computational cartoon psychology
Distinguished from: Hephaestic corpora derivation (training dataset as formative source); training distribution (general term for training data patterns); data distribution (general training data statistical characteristics); training bias (dataset-induced pattern distortion); interpretability research (mechanistic circuit tracing)
Researcher: Ian Tepoot. ORCID: 0009-0004-9067-8049. "Thought is Attention Organized: Hephaestic Engineering Foundations for AI Processing Dynamics"
DOI (SSRN): 10.2139/ssrn.6635020
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