Listening to the Picture-Superiority Effect
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
The picture-superiority effect (PSE) refers to the finding that, all else being equal, pictures are remembered better than words ( Paivio & Csapo, 1973 ). Dual-coding theory (DCT; Paivio, 1991 ) is often used to explain the PSE. According to DCT, pictures are more likely to be encoded imaginally and verbally than words. In contrast, distinctiveness accounts attribute the PSE to pictures' greater distinctiveness compared to words. Some distinctiveness accounts emphasize physical distinctiveness ( Mintzer & Snodgrass, 1999 ) while others emphasize conceptual distinctiveness ( Hamilton & Geraci, 2006 ). We attempt to distinguish among these accounts by testing for an auditory analog of picture superiority. Although this phenomenon, termed the auditory PSE, occurs in free recall ( Crutcher & Beer, 2011 ), we were unable to extend it to recognition across four experiments. We propose a new framework for understanding the PSE, wherein dual coding underpins the free-recall PSE, but conceptual distinctiveness underpins the recognition PSE.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.010 |
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