Task-dependent learning of non-adjacent dependencies: Success in a familiarity rating task but failure in a two-alternative forced-choice task
Bibliographic record
Abstract
Acquiring non-adjacent dependencies (NADs) from continuous sequences can be challenging for adults, with prior research showing varied outcomes depending on the properties of the stimuli and methods of assessments. This study investigated whether different behavioral tasks vary in their ability to detect NAD learning. All participants (N = 322) underwent equivalent training phases involving exposure to NAD trigrams in a continuous speech stream. During the test phase, their learning of the grammatical NAD patterns was evaluated using either a two-alternative forced-choice (2AFC, N = 200) task or a familiarity rating task (N = 122). Participants in the 2AFC task performed at chance, regardless of whether the ungrammatical trigram differed minimally or maximally from the grammatical trigram. In contrast, participants in the familiarity rating task rated grammatical trigrams as more familiar than ungrammatical ones, suggesting that the familiarity rating task may be more sensitive to subtle learning effects. These findings highlight the importance of task design in the detection of NAD learning, with implications for the broader field of statistical learning research.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".