How Loss of Meaning with Preservation of Phonological Word Form Affects Immediate Serial Recall Performance: A Linguistic Account
Bibliographic record
Abstract
We present HP, a patient who following the occurrence of herpes simplex encephalitis, lost the ability to understand a subset of words while others remained preserved. Of particular interest is the fact that the meaningless items retained their lexical status. HP's immediate serial recall of meaningless words was thus compared with that of meaningful words to assess the unique contribution of semantic knowledge without the confounding influence of phonological word (lexical) form. The results revealed a clear recall advantage for meaningful over meaningless words, indicating a specific contribution to recall from the semantic level of representation. Furthermore, an error analysis showed that phonemic errors were most common when semantic information was lacking. Interestingly, the same error pattern was found for pseudo-words that shared phonological elements with meaningless words. These findings support a linguistic and interactive activation account of short-term serial recall, which assumes that all levels of representation, including semantic knowledge about words, contribute to recall performance. In addition, the findings provide preliminary evidence that this view may be extended to the recall of pseudo-words, as there appear to be some influences of semantic representation on pseudo-word recall.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".