SingleMALD: Investigating practice effects in auditory lexical decision
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We present SingleMALD, a large-scale auditory lexical decision study in English with a fully crossed design. SingleMALD is freely available and includes over 2 million trials in which 40 native speakers of English responded to over 26,000 different words and over 9000 different pseudowords, each in 67 balanced sessions. SingleMALD features a large number of responses per stimulus, but a smaller number of participants, thus complementing the Massive Auditory Lexical Decision (MALD) dataset which features many listeners but fewer responses per stimulus. In the present report, we also use SingleMALD data to explore how extensive testing affects performance in the auditory lexical decision task. SingleMALD participants show signs of favoring speed over accuracy as the sessions unfold. Additionally, we find that the relationship between participant performance and two lexical predictors - word frequency and phonological neighborhood density - changes as sessions unfold, especially for certain lexical predictor values. We note that none of the changes are drastic, indicating that data collected from participants that have been extensively tested is usable, although we recommend accounting for participant experience with the task when performing statistical analyses of the data.
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.
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 | 0.018 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it