Do low intelligibility voices induce false memories?
Bibliographic record
Abstract
Spoken language is highly variable with phonetic variation that is attributable to multiple dimensions, including, but not limited to, differences in dialect, mood, native language, sociolinguistic dimensions, and talker-specific idiosyncrasies. Phonetic variation has documented effects on other aspects of linguistic processing. For example, Sumner and Kataoka found that phonetic variation affected semantic processing in semantic priming and false memory tasks, to which they concluding that socially dispreferred (i.e., voices or accents that are socially stigmatized or less desirable) phonetic variation received less attention in encoding, leading to a lack of priming and higher false memory rates [Sumner & Kataoka, 2013, JASA-EL, 134(6)]. In this study we compare the intelligibility and propensity to induce false memories in a between-subjects design. Intelligibility is assessed as response latency in a speeded shadowing task, and false memories are measured using standardized false memory lists where reporting of critical lure items not in the auditorily presented list are counted as false memories. The speaker set consists of five speakers of English, representing two local accents, two nonnative accents, and one native nonlocal accent. The results of this study contribute to our understanding of how phonetic variation affects word recognition and semantic processing.
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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 | 0.003 | 0.015 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".