Bibliographic record
Abstract
“Of all the prerequisites for language, none is more vital, though more easily overlooked than memory; yet language is possible only because of memory … [or rather the] representation of the objects, actions, or properties stored in memory” (Premack, 1978, p. 877). All researchers agree that recognition memory is a primitive form of memory available to quite simple animals and to very young infants. It is, rather, recall memory that is important for language acquisition. Recall is defined as “accessing (bringing to awareness) information about something that is not perceptually present. By definition, recall is a conscious product” (Mandler, 1990, p. 486). In the previous chapter, I focused on the development of representational processes in infancy that are basic to recall. In this chapter, I focus on the relationship between recall memory and language acquisition. I begin by examining memory in nonhuman primates – not only recall memory but also recognition memory involving representation of a sequence or array. These aspects of nonhuman primate recognition memory are included because sequential memory appears to have important implications for children's acquisition of language. Memory in Monkeys and Apes Most of the recent research on memory in nonhuman primates involves experiments with captive monkeys, with research on captive apes largely limited to early studies. However, some evidence exists on spatial memory in both wild and captive apes and on delayed imitation in rehabilitant apes.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".