<i>Latent Ecologies of the Mind</i> : Laying Down the Path for Ecopoietic Hyperfeedback Systems
Bibliographic record
Abstract
Abstract Latent Ecologies of the Mind is an interdisciplinary project that leverages real-time neurofeedback, hyperscanning, and generative AI to project physiological signals into digital ecosystems. This approach creates participatory environments where cognitive states dynamically shape ecological narratives. Central to this work is the concept of the ecotone—a fertile interface where distinct systems meet—here reimagined as a space for shared congnitive and ecological transformation. The paper contributes (i) conceptual propositions about ecotones of the mind and shared embodiment and (ii) design mappings from physiology to ecological representations. This perspective points toward how integrating biosignals, AI, and ecological metaphors can inspire new forms of relational awareness, offering innovative pathways for nurturing personal and planetary well-being.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".