Spatial listening and semantic interference in a dual-language context
Bibliographic record
Abstract
Understanding target speech amid competing speech (i.e., a masker) is an everyday challenge, and for bilinguals it may be compounded when the masker is another known language (i.e., a dual-language context). This thesis examines how forms of masking affect bilingual listening. Chapter 2 examines energetic masking (EM), a type of acoustic disruption that can be alleviated through spatial separation in a process known as spatial release from masking (SRM). For bilinguals, prior research suggests EM hinders L2 processing more than L1, suggesting larger SRM benefits for L2 than L1 listening. Across selective and divided listening tasks, Chapter 2 shows SRM benefited listening, regardless of whether the target language was L1 or L2. However, this benefit was reduced when listeners tracked both talkers simultaneously, likely due to cognitive demands associated with ear-switching. Chapter 3 investigates informational masking (IM), examining whether semantic overlap between target and masker speech disrupts listening. Previous research demonstrates that masker meaningfulness per se impacts listening, and that lexical-semantic links are stronger for L1 than L2. Therefore, maskers semantically related to a target might impact listening more than semantically unrelated maskers, particularly when the masker is L1 compared to L2 (for an L1 target). Across experiments, semantic overlap did not affect listening, regardless of masker language. Yet in single-language (L1-L1) contexts with minimal acoustic cues for stream segregation, listeners appeared to use semantic coherence when deciding if a heard word belonged to the target or masker. Therefore, semantic overlap between speech streams did not affect target transcription but did impact error types. Overall, while semantic cues from the masker do not impact listening, bilinguals nevertheless rely on low-level acoustic cues to stream speech in adverse listening conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".