Distinctiveness, not dual coding, explains the picture-superiority effect
Bibliographic record
Abstract
The picture-superiority effect is the finding that memory for pictures exceeds memory for words on many tasks. According to dual-coding theory, the pictures' mnemonic advantage stems from their greater likelihood to be labelled relative to words being imaged. In contrast, distinctiveness accounts hold that the greater variability of pictures compared to words leads to their mnemonic advantage. Ensor, Surprenant, et al. tested these accounts in old/new and forced-choice recognition by increasing the physical distinctiveness of words and decreasing the physical distinctiveness of pictures. Half of the words were presented in standard black font, and half were presented in varying font styles, font sizes, font colours, and capitalisation patterns. Half of the pictures were presented in black and white and half in colour. Consistent with the physical-distinctiveness account but contrary to the dual-coding account, the picture-superiority effect was eliminated when comparing the black-and-white pictures to distinctive words. In the present study, we extend Ensor, Surprenant, et al.'s results to associative recognition and free recall. Results were consistent with physical distinctiveness. We argue that dual-coding theory is no longer a viable explanation of the picture-superiority effect.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".