Investigating the Influence of Continuous Babble on Auditory Short-Term Memory Performance
Bibliographic record
Abstract
A number of factors could explain the adverse effect that babble noise has on memory for spoken words (Murphy, Craik, Li, & Schneider, 2000). Babble could degrade the perceptual representation of words to such an extent that it compromises their subsequent processing, or the presence of speech noise in the period between word presentations could interfere with rehearsal. Thirdly, the top-down processes needed to extract the words from the babble could draw on resources that otherwise would be used for encoding. We tested all these hypotheses by presenting babble either only during word presentation or rehearsal, or by gating the babble on and off 500 ms before and after each word pair. Only the last condition led to a decline in memory. We propose that this decline in memory occurred because participants were focusing their attention on the auditory stream (to enable them to better segregate the words from the noise background) rather than on remembering the words they had heard. To further support our claim we show that a similar memory deficit results when participants perform the same memory task in quiet together with a nonauditory attention-demanding secondary task.
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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 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".