Bibliographic record
Abstract
It was previously assumed that a correct, or accurate, initial association between a word and its referent allows optimal language learning, since fewer cognitive resources are required. However, some studies have found that initially incorrect, or inaccurate, associations can cause adults to learn word-referent mappings significantly better, compared to initially accurate ones. The opposite effect was found in children, who typically learn better when a word-referent association is consistently accurate.Our research project further explores these findings. The study involves a cross-situational word-learning paradigm examining whether the correction of inaccurate initial word-referent associations benefits word learning. Eye fixation and pupil dilation data are used to determine initial mappings and cognitive load, respectively. In the familiarisation phase of the study, participants are presented with associations between made-up words and the images to which the words refer; initial accuracy is manipulated such that only half of the presented associations are correct. Then, in the learning phase, each word is presented with its correct referent. Finally, the accuracy of the learned word-referent mappings is tested.Our initial findings confirm that word learning benefits when adults are presented with initially inaccurate associations and subsequent corrections. We will discuss the theories behind these findings, as well as the implications for language learning. In the most recent phase of the project, we are determining whether pupil dilation is a good measure for increased cognitive load associated with word-referent inaccuracy.
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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 | 0.001 | 0.011 |
| 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) | 0.009 | 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".