Stimulus, Task, and Learning Effects on Measures of Temporal Resolution: Implications for Predictors of Language Outcome
Bibliographic record
Abstract
PURPOSE: Some studies find that temporal processing ability predicts language outcome whereas other studies do not. Resolution of this debate is hindered by the variety of temporal measures used, nonsensory loading of the tasks, and differential amounts of practice across studies. The goal of this study was to examine the effects of stimulus properties, experimental task, and perceptual learning on listeners' gap detection performance. METHOD: Gap detection thresholds were obtained from adults with normal hearing and language ability. The effects of marker frequency similarity and marker duration on thresholds were examined in yes-no, two-interval forced-choice (2IFC), and dual-pair comparison tasks (which vary in nonsensory loading) over 4 days of testing. RESULTS: Thresholds were highest for gaps defined by markers with disparate frequencies (1000 and 4000 Hz; i.e., between-channel gap detection), and with longer (300 ms) trailing markers, obtained using yes-no and 2IFC tasks. However, these effects were attenuated with training or the initial use of the dual-pair comparison task. CONCLUSIONS: These results suggest that gap detection thresholds reflect a variety of sensory and nonsensory factors. Understanding these underlying factors is critical to any evaluation of the relation between temporal processing and language outcome.
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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 | 0.009 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".