Segmented binaural presentation as a means to examine lexical substructure
Bibliographic record
Abstract
We present an auditory presentation technique called segmented binaural presentation. The technique builds on the dichotic listening paradigm (Shankweiler & Studdert-Kennedy, 1967; Studdert-Kennedy & Shankweiler, 1970) and segmented lexical presentation (Libben, 2003; Betram, Kuperman, Baayen, & Hyönä, 2011). The technique allows the first part of a word to be presented to one ear and the second part of the word to be presented to the other ear. The experimenter may thus manipulate whether a stimulus is segmented in this binaural manner and, if it is segmented, the location of the binaural segmentation within the word. We discuss how the technique may be implemented on the Macintosh platform, using PsyScope and freely available software for audio file creation. We also report on a test implementation of the technique using suffixed and compound English words in a lexical decision task. Results suggest that the technique differentiates between segmentation that occurs within and between compound constituents.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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; both teacher heads agree on what is shown here.
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".