What you see is what you hear: How visual prosody affects artificial language learning in adults and children
Bibliographic record
Abstract
Speech perception is a multimodal phenomenon, with what we see impacting what we hear. In this study, we examine how visual information impacts English listeners’ segmentation of words from an artificial language containing no cues to word boundaries other than the transitional probabilities (TPs) between syllables. Participants (N = 60) were assigned to one of three conditions: Still (still image), trochaic (image loomed toward the listener at syllable onsets), or Iambic (image loomed toward the listener at syllable offsets). Participants also heard either an easy or difficult variant of the language. Importantly, both languages lacked auditory prosody. Overall performance in a 2AFC test was better in the easy (67%) than difficult language (57%). In addition, across languages, listeners performed best in the trochaic condition (67%) and worst in the Iambic condition (56%). Performance in the still condition fell in between (61%). We know English listeners perceive strong syllables as word onsets. Thus, participants likely found the Trochaic Condition easiest because the moving image led them to perceive temporally co-occurring syllables as strong. We are currently testing 6-year-olds (N = 25) with these materials. Thus far, children’s performance collapsed across conditions is similar to adults (60%). However, visual information may impact children's performance less.
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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".