Instance memory models as a general computational framework for exploring language processing: bringing the lexicon to life
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 Instance models have been successfully applied to a range of problems including memory, language, attention, learning, action, decision-making, and categorization. According to instance theory, the individual experience constitutes the fundamental unit of knowledge and knowledge of the general emerges during parallel retrieval from memory. Until recently, applications of instance theory to the problem of language were constrained to small and contrived laboratory experiments. However, recent advances in large-scale computational modeling have allowed the approach to be applied at scale to the large and complicated problem of natural language. With those demonstrations now in hand, we argue that the framework can present an articulate mechanistic underbelly to usage-based theories of language that highlights the role of specific language experience in general language behavior. Overall, this article argues that instance memory models provide an opportunity to gain insight into and deepen our understanding of language as a dynamic and contextually embedded process, serving to bridge the gap between cognitive psychology and the language sciences.
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 | 0.003 |
| 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 | 0.001 | 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 it