The effects of memory scanning on the late Nd and P300: An interference study
Bibliographic record
Abstract
Singhal and Fowler (2004) showed that the late negative difference (Nd) waveform elicited during dichotic listening was attenuated by concurrent visual short term memory (STM) scanning, but not long term memory (LTM) scanning. P300 was reduced by both tasks. The present study compared the effects of STM load on the late Nd and P300 by combining dichotic listening and visual memory scanning with varying set sizes. The results showed that the late Nd was sensitive to the introduction of the scanning task, but not to an increase in load. Furthermore, both the auditory and visual P300s were reduced when a second task was introduced, but only the visual P300 decreased as a function of memory-set size. These data suggest that (a) the auditory late Nd reflects working memory, but not memory scanning, (b) late Nd and P300 reflect parallel but distinct working memory processes, and (c) stimulus modality is an important determinant of P300 amplitude.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".