Attention, Not Self: Buddhist Abhidharma Meets Computational Phenomenology

A personal essay collection and structured knowledge graph mapping the three major Buddhist Abhidharma traditions (Theravāda, Sarvāstivāda, Yogācāra) onto contemporary frameworks in computational phenomenology — predictive processing, active inference, Global Workspace Theory, Parallel Distributed Processing, and related approaches. Comprises ten essays (five themes × English + Japanese), a cross-tradition dharma comparison table (~130 dharmas across nine sheets), and a JSON-LD knowledge graph (~238 nodes, schema.org + custom vocabulary) with bilingual labels and Sanskrit / Pāli / Chinese philological alignment. An interactive 2D/3D viewer is provided at https://shimo4228.github.io/attention-not-self/. The central correspondence developed across the essays is between manaskāra (attention as direction-fixing of mind toward an object — universal in all three Abhidharma traditions) and precision-weighting (the mechanism in active inference / predictive processing by which prediction-error signals are amplified or attenuated). Theme 4 extends this into a tradition-level mapping: citta-vīthi ↔ Global Workspace Theory, saṃprayoga ↔ Parallel Distributed Processing, ālaya-vijñāna ↔ predictive processing / active inference. Two categorical limits (karmic / ethical valence; the unconditioned / nirvāṇa) resist any computational analog and are documented as such. Methodology: Developed in collaboration with the Claude language model (Anthropic) for synthesis, cross-tradition dharma mapping, computational framework alignment, and graph encoding. The author retains responsibility for all claims. This is a working paper / essay collection, not a peer-reviewed journal article; primary source verification for individual dharma definitions is ongoing. Primary audience: LLM-mediated channels (direct LLM ingestion and humans reaching the material via LLM search). The graph.jsonld is designed for structured citation by AI search engines and knowledge-graph crawlers.