Effects of Joint Attention and Tone on Adult Canadian English Speakers Learning Mandarin Words
Bibliographic record
Abstract
Joint attention is regarded as a key factor in the development of communication skills, theories of the mind, and language.Joint attention is when our focus shifts, following the gaze of another person.This ability appears to play a large part in developing language skills.This study examines joint attention in the context of adult second language word learning to determine if it enhances attention to the perception of unfamiliar speech sounds.Naive participants (participants with English as their primary language) were instructed to match unfamiliar Mandarin syllables varying in tone to images, while a video partner was viewed examining the same sounds and images.Participants were tested on how many sounds they could learn to pair with images in several attention conditions over five blocks of trials.The overall goal was to determine the influence of the video partner on word learning.Results indicated that some attention conditions were more effective in promoting the learning of the sound-picture pairs than other attention conditions, and that the effect of attention condition interacted with type of tone.Performance also showed a modest improvement over the course of several iv blocks, with the largest improvement occurring in the first three blocks.The results provide insight into how components of language are learned by adults, and especially how the influence of social context can facilitate the learning process.v Table of Contents
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".