Psyntient's Internal Program
Psyntient's in-house research program.
This page describes the scientific work Psyntient conducts in-house — the studies, pipeline, and questions driving our own contributions to the Noetic Archive. For how external labs, PIs, and SAB candidates can use the Archive in their own work, see For Researchers.
Working Paper · Draft · Not Peer Reviewed
The Noetic Archive: Infrastructure for a Reproducible Science of Mind
The foundational document for the Archive. It lays out why a shared, decentralized neural-and-experiential dataset is needed, how the Archive is architected to be that dataset, and the methodological framework Psyntient is committing to. It is offered as orientation, infrastructure justification, and a research agenda — not as an empirical report. Empirical findings will follow in future peer-reviewed publications as the Archive ingests real-world data.
§ 01
Research goals
The central goal is to identify recurring neural patterns associated with distinct experiential states, and to determine the conditions under which those patterns generalize across individuals and contexts.
§ 02
Where the science lands
Research output isn't an end in itself — every validated archetype sharpens four downstream domains at once:
- → training and alignment data for frontier AI labs
- → ground truth for brain–computer interface state inference
- → consumer neurofeedback and entrainment via Ground
- → empirical anchor points for philosophy of mind
§ 03
Methodology
The research pipeline proceeds in five stages, each feeding the next:
- 01
Neural data collection
EEG recordings captured on Ground under standardized session protocols.
- 02
Phenomenological reporting
Structured first-person reports submitted alongside each neural recording.
- 03
Machine learning clustering
Unsupervised methods identify candidate neural patterns across participants.
- 04
Architect classification
The Architect agent evaluates candidate clusters against phenomenological tags and assigns or proposes archetypes.
- 05
Taxonomy refinement
The Architect updates the living taxonomy as new data enters the Archive.
§ 04
Key research questions
- Q1. Can neural archetypes be reliably detected across individuals?
- Q2. How stable are these archetypes across contexts and over time?
- Q3. Can AI systems infer experiential states directly from neural signals with calibrated uncertainty?
- Q4. What level of resolution does experiential inference require, and where does additional resolution stop helping?
§ 05
Open to the field
Psyntient's program is one input into the Archive — not the only one. The Archive is built as shared infrastructure: external researchers can ingest their own datasets, analyze their own data against the broader corpus through the Noetic Interface, and cite specific Editions for reproducibility.
If you're a PI, methodologist, or research group whose work touches neural correlates of experience, the next page is written for you.