Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Pseudowords are used as stimuli in many psycholinguistic experiments, yet they remain largely under-researched. To better understand the cognitive processing of pseudowords, we analysed the pseudoword responses in the Massive Auditory Lexical Decision megastudy data set. Linguistic characteristics that influence the processing of real English words – namely, phonotactic probability, phonological neighbourhood density, uniqueness point, and morphological complexity – were also found to influence the processing time of spoken pseudowords. Subsequently, we analysed how the linguistic characteristics of non-unique portions of pseudowords influenced processing time. We again found that the named linguistic characteristics affected processing time, highlighting the dynamicity of activation and competition. We argue these findings also speak to learning new words and spoken word recognition generally. We then discuss what aspects of pseudoword recognition a full model of spoken word recognition must account for. We finish with a re-description of the auditory lexical decision task in light of our results.
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.
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) | 0.001 | 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 it