Bibliographic record
Abstract
This study looks at the mechanisms behind how people learn words of a new language. Syllables that occur within words have a higher chance of occurring together than the syllables between words. Both infants and adults use these transitional probabilities to extract the words in language. However, previous research has examined speech segmentation when learners are presented just with speech. In natural context, we look while we listen and what we see is correlated with what we hear. The goal of my study was to explore how visual context affects adult speech segmentation. To do so, we have three conditions: one where adults were presented with only a word stream, one where while listening adults saw animations that corresponded to words they heard, and one where the animations that the adults saw did not correspond to the words they heard. One hypothesis is that participants in the audio-visual conditions perform better at the segmentation task because the statistical boundaries in the audio are reinforced by the visual boundaries between animations. However, it is also possible that the visual information impairs performance because learners engage in learning the meanings of words in addition to speech segmentation. Preliminary results support the latter hypothesis.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".