A conceptual–perceptual distinctiveness processing account of the superior recognition memory of pictures over environmental sounds
Bibliographic record
Abstract
Researchers have proposed a coarser or gist-based representation for sounds, whereas a more verbatim-based representation is retrieved from long-term memory to account for higher recognition performance for pictures. This study examined the mechanism for the recognition advantage for pictures. In Experiment 1A, pictures and sounds were presented in separate trials in a mixed list during the study phase and participants showed in a yes-no test, a higher proportion of correct responses for targets, exemplar foils categorically related to the target, and novel foils for pictures compared with sounds. In Experiment 1B, the picture recognition advantage was replicated in a two-alternative forced-choice test for the novel and exemplar foil conditions. For Experiment 2A, even when verbal labels (i.e., written labels) were presented for sounds during the study phase, a recognition advantage for pictures was shown for both targets and exemplar foils. Experiment 2B showed that the presence of written labels for sounds, during both the study and test phases did not eliminate the advantage of recognition of pictures in terms of correct rejection of exemplar foils. Finally, in two additional experiments, we examined whether the degree of similarity within pictures and sounds could account for the recognition advantage of pictures. The mean similarity rating for pictures was higher than the mean similarity rating for sounds in the exemplar test condition, whereas mean similarity rating for sounds was higher than pictures in the novel test condition. These results pose a challenge for some versions of distinctiveness accounts of the picture superiority effect. We propose a conceptual-perceptual distinctiveness processing account of recognition memory for pictures and sounds.
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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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".