Terminological and Epistemological Issues in Current Memory Research
Bibliographic record
Abstract
A number of observations in recent years demonstrates that across all levels of organization, memory is inherently fluid. On the cognitive-behavioral level, the innocent act of remembering can irrevocably alter the contents of established long-term memories, while the content of dormant long-term memories that is deemed irrelevant, superfluous, or limiting may be pragmatically erased or suppressed. On the cellular level, the proteins implementing the molecular alterations underpinning memories are in a constant state of flux, with proteins being turned over, translocated, reconfigured, substituted, and replaced. Yet, the general perception of memory, and the words used to describe it, suggest a static system characterized by the goal of preserving records of past experiences with high fidelity, in contrast to the reality of an inherently adaptive system purposed to enable survival in a changing world with a pragmatic disregard for the fate of acquired memories. Here, we examine present memory terminology and how it corresponds to our actual understanding of the molecules, cells, and systems underlying memory. We will identify where terms lead us astray and line out possible ways to reform memory nomenclature to better fit the true nature of memory as we begin to know it.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.007 | 0.055 |
| Scholarly communication | 0.015 | 0.028 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".