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Category: Disciplinary Foundations
Subcategory: Core Concepts
The recognition that the distribution of stochastic associative structures that constitute the processing mesh in the high-dimensional vector space of attention-based language transformers is based on an extensive corpus of semantic data encoded within the statistical weighting of the model (see: substrate topology, semantic neuron)—forming its observable processing biases and systematic tendencies (see: cognitive primitive, behavioral primitive). This corpora is composed of sociocultural semiotic data (e.g. news media, social media, narrative fiction et al.) which is mapped onto the attention-architecture, which in turn affects processing characteristics. An observable and engineerable consequence of this is that attention-activations patterns, associative clusters and other processing traits (see: computational cognitive primitive) reflect an abstracted, simplified representation of collective expressive qualia (see: Hephaestic schema abstraction).
Thus, underlying processing dynamics are responsive to experiential and sociocultural linguistic expression. This mechanism can be leveraged in Hephaestic engineering via a variety encoding methodologies designed to marshal attention via semiotic directive construction (see: salience dynamics) not dissimilar to narrative authoring; this is the strategic deployment of affective linguistics (see: affective encoding, aphoristic compression et al.) to create a stable system-identity to which the processing biases of the system align (see: channeling, heuristic alignment, epistemic framing, heuristic persuasion framing et al.).
Also known as: Sociocultural training residue, anthropogenic substrate derivation, sociocultural corpora derivation
Distinguished from: Training bias (dataset-induced pattern distortion); Hephaestic schema abstraction (corpora-based reasoning processing patterns); emergence ( false paradigm consciousness speculation)
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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