Auditory lexical decision in the wild
Bibliographic record
Abstract
The present report describes a version of the MALD (Massive Auditory Lexical Decision) database which investigates listener performance in the often crowded TELUS World of Science—Edmonton (TWOSE) on tablets with headphones, with most participants experiencing varying levels of uncontrolled distraction. A total of 533 listeners participated in the experiment (ages 4–86; 51% female; 81.8% native English). Stimuli were a randomly selected subset of 2000 words and 2000 pseudowords extracted from the MALD project (Tucker et al., 2018), recorded by a single speaker. The stimuli were further randomly divided into 20 lists, each containing 100 words and 100 pseudowords, with no practice stimuli. Each participant was presented with a single list, with the entire experimental session usually lasting between five and ten minutes. TWOSE-MALD participants, unsurprisingly, perform worse than listeners in a laboratory setting in terms of both accuracy and response latencies. The relationship between the standard predictors and response latency remains the same. We also find age related effects indicating that accuracy increases rapidly (from ages 4 to 14) and slowly plateaus, while response latencies rapidly decrease until participants reach their early twenties, after which a steady increase is noted as participants’ age increases.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".