Word frequency, familiarity, and laterality effects in a dichotic listening task
Bibliographic record
Abstract
Although word frequency and familiarity effects are a well-established finding in visual research (Marlsen-Wilson, 1990), these variables are often overshadowed in the selection of words used in dichotic listening tasks in favour of choosing words that fuse together (Connine, Mullenix, Shernoff, & Yenn, 1990). The present study investigates the influence of word frequency and word familiarity on the right ear advantage typically found in dichotic listening. A task using words that did not fuse, and manipulating word frequency and word familiarity was developed. In the task participants were presented with dichotic pairs of words and asked to select the word that they heard the clearest. This task showed that the right ear advantage (REA) was larger when the frequency of the words presented to each ear was the same than when it was different. In addition, the magnitude of the REA was modulated independently by the familiarity of the word presented to the left and to the right ear. Overall, the results of the present study support the notion that word frequency and familiarity should be considered in dichotic tasks. The findings are interpreted in terms of their implications for models of dichotic listening.
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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 | 0.001 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".