Effects of reading ability on native and nonnative talker recognition
Bibliographic record
Abstract
Recent findings suggest that phonological knowledge influences talker identification. Specifically, talker identification is improved for native compared to nonnative talkers, and adults with reading disability show impaired talker identification even for native talkers [Perrachione et al., Science, 333, 595 (2011)]. Here, we examine whether effects of reading ability on talker identification emerge among unimpaired readers. Monolingual English adults were assigned to either the high or low reading group based on standardized assessments of reading and reading sub-skills. All readers learned to identify the voices of four English talkers and four French talkers. Training consisted of a two-alternative forced choice task with feedback provided on every trial. After training, retention of learning was tested using a four-alternative forced choice task without feedback. The results to date suggest that the high reading group learned both the native and nonnative voices faster compared to the low reading group. Moreover, the high reading group showed increased retention of learning compared to the low reading group, but only for the nonnative voices. These results are consistent with recent findings demonstrating an effect of language proficiency on talker identification, and extend them to include a gradient role for native language phonological ability on nonnative talker identification.
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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) | 0.000 | 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 | 0.001 |
| 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".