When New Experience Leads to New Knowledge: A Computational Framework for Formalizing Epistemically Transformative Experiences
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The discovery of a new kind of experience can teach an agent what that kind of experience is like. Such a discovery can be epistemically transformative, teaching an agent something they could not have learned without having that kind of experience. However, learning something new does not always require new experience. In some cases, an agent can merely expand their existing knowledge using, e.g., inference or imagination that draws on prior knowledge. We present a computational framework, grounded in the language of partially observable Markov Decision Processes (POMDPs), to formalize this distinction. We propose that epistemically transformative experiences leave a measurable “signature” distinguishing them from experiences that are not epistemically transformative. For epistemically transformative experiences, learning in a new environment may be comparable to “learning from scratch” (since prior knowledge has become obsolete). In contrast, for experiences that are not transformative, learning in a new environment can be facilitated by prior knowledge of that same kind (since new knowledge can be built upon the old). We demonstrate this in a synthetic experiment inspired by Edwin Abbott’s Flatland, where an agent learns to navigate a 2D world and is subsequently transferred either to a 3D world (epistemically transformative change) or to an expanded 2D world (epistemically non-transformative change). Beyond the contribution to understanding epistemic change, our work shows how tools in computational cognitive science can formalize and evaluate philosophical intuitions in new ways.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it