Acoustic reduction, context, and inter-stimulus interval in cross-modal priming
Bibliographic record
Abstract
Work such as Tucker (2011) and van de Ven et al. (2011) show that reduced acoustic contrasts can impede lexical access for a listener. Tucker (2011) also found preliminary evidence of a nonlinear (U-shaped) relationship between the degree of intensity dip in stops and lexical decision response times. The present talk summarizes results from a cross-modal identity priming experiment designed to explore this potential nonlinearity further. Utilizing three different inter-stimulus-intervals, trials were presented in which conversational words with variously reduced intervocalic stops served as auditory primes. Visual targets included the same word as the prime, words with a large degree of phonological overlap, and unrelated controls, as well as phonologically overlapping and non-overlapping pseudowords. Additionally, the auditory primes were presented with three degrees of surrounding context: isolation (the word only), phonetic context (including the vowels in neighboring syllables, providing primarily speech-rate information), or complete utterances. This talk explores the resulting picture of the relationship between reduction, context, and inter-stimulus-intervals to processing demand as evidenced in the response latencies. We then discuss the implications for models of perception and spoken word recognition.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".