Published April 19, 2026 | Version v1
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Completing the Cognitive Exoskeleton: The TASTE Layer for Aesthetic Selection in AI Creative Production

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Position paper completing the three-layer cognitive exoskeleton model for AI creative production. The first paper in this series established SPARQ for execution quality (CRAFT layer); the second introduced GHOSTY COLLIDER for creative leaps (LEAP layer). This paper addresses the remaining capacity: aesthetic selection.
 
The TASTE layer consists of two mechanisms. First, hyper-elimination—a selection process that identifies the irreducible essence of the work under evaluation and systematically removes everything that does not serve it, testing for inevitability rather than ranking optimality.
 
Second, B-PRISM (Brain Processing Resource Index for Stimulation Mapping)—a quality framework with five axes grounded in major neural processing systems: Modality integration (M), Social cognition (S), Language/semantic processing (L), Emotion processing (E), and Prediction error (P). The central thesis is that quality is brain response: exceptional creative outputs engage multiple neural processing systems in a balanced, structured manner.
 
The framework distinguishes two activation profiles: ILLUMINATE (balanced engagement across all axes) and SHADOW (imbalanced engagement concentrated on a subset of axes). The SHADOW pattern has a plausible neuroscientific grounding through Berlyne's inverted-U curve.
 
Preliminary application to 30 music videos and a longitudinal production case study suggests that B-PRISM scoring surfaces structural weaknesses invisible to conventional quality assessment, and that hyper-elimination converges toward selections that practitioners judge as unusually inevitable.
 
Specific implementation protocols—including scoring rubrics, weighting schemes, threshold parameters, and anti-gaming mechanisms—are proprietary and covered by Japanese Patent Application (filed April 2026). The public contribution is the framework architecture, sufficient for independent evaluation and falsification though not for exact replication.
 
Related publications:
- Paper 1: Cognitive Exoskeletons (CRAFT layer) — https://doi.org/10.5281/zenodo.19209961
- Paper 2: From Theory to Protocol (LEAP layer) — arXiv:2604.09597

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Preprint: 10.5281/zenodo.19209961 (DOI)
Preprint: arXiv:2604.09597 (arXiv)

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