Attention, Not Self: Buddhist Abhidharma Meets Computational Phenomenology
Zenodo (CERN European Organization for Nuclear Research) July 1, 2026 Shimomoto, Tatsuya
This essay collection and knowledge graph maps three major Buddhist Abhidharma traditions (Theravāda, Sarvāstivāda, Yogācāra) onto contemporary computational phenomenology frameworks such as predictive processing, active inference, Global Workspace Theory, and Parallel Distributed Processing. The central correspondence developed is between manaskāra (attention as direction-fixing of mind toward an object, universal across all three traditions) and precision-weighting in active inference. The work includes sixteen essays, a cross-tradition dharma comparison table, and a JSON-LD knowledge graph. It also explores themes such as the multiple incompatible senses of the Chinese character 念, meditative cessation and the no-self continuity problem, and epistemic luck across traditions, comparing Dharmakīrti's pramāṇa-vāda with the Gettier problem and the Free Energy Principle.