Talker and background noise specificity in spoken word recognition memory
Bibliographic record
Abstract
Prior research has demonstrated that listeners are sensitive to changes in the indexical (talker-specific) characteristics of speech input, suggesting that these signal-intrinsic features are integrally encoded in memory for spoken words. Given that listeners frequently must contend with concurrent environmental noise, to what extent do they also encode signal-extrinsic details? Native English listeners’ explicit memory for spoken English monosyllabic and disyllabic words was assessed as a function of consistency versus variation in the talker’s voice (talker condition) and background noise (noise condition) using a delayed recognition memory paradigm. The speech and noise signals were spectrally-separated, such that changes in a simultaneously presented non-speech signal (background noise) from exposure to test would not be accompanied by concomitant changes in the target speech signal. The results revealed that listeners can encode both signal-intrinsic talker and signal-extrinsic noise information into integrated cognitive representations, critically even when the two auditory streams are spectrally non-overlapping. However, the extent to which extra-linguistic episodic information is encoded alongside linguistic information appears to be modulated by syllabic characteristics, with specificity effects found only for monosyllabic items. These findings suggest that encoding and retrieval of episodic information during spoken word processing may be modulated by lexical characteristics.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".