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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".