The effect of types of acoustical distortion on lexical access
Bibliographic record
Abstract
A study was carried out to examine the effects of types of acoustical distortion on lexical access. The study aimed to replicate and extend the claims of authors Aydelott and Bates (2004) regarding the effects of acoustical distortion on lexical access. It was expected that for intact sentence contexts, reaction times to target in congruent contexts would be faster compared to reaction times in a neutral context, whereas those in an incongruent context would be slower. Furthermore, if reaction times differ depending on the type of distortion then the pattern suggest how priming may differ in lexical access for different types of distortion. The analysis also revealed that different types of distortion did affect lexical access to different degrees. It concluded that the distortions due to low-pass filtering and signal-to-noise ratio (SNR) produced a release from inhibition and significant decrease, while time compression significantly reduced facilitation and increased inhibition.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".