Perceptual-motor determinants of auditory-verbal serial short-term memory
Bibliographic record
Abstract
The role of the compatibility between obligatory perceptual organization and the active assembly of a motor-plan in auditory-verbal serial recall was examined. The classic finding that serial recall is poorer with ear-alternating items was shown to be related to spatial-source localization, thereby confirming a basic tenet of the perceptual-motor account and disconfirming an early account characterizing the two ears as separate input-channels (Experiment 1). Promoting the streaming-by-location of ear-alternating items—and therefore the incompatibility between perceived and actual order—augmented the ear-alternation effect (Experiment 2) whereas demoting streaming-by-location by reducing the regularity of the alternation attenuated it (Experiment 3). Finally, increasing the perceptual variability of an ear-alternating list while demoting the likelihood of streaming-by-location—by adding uncorrelated voice changes—also reduced the ear-alternation effect as did articulatory suppression for that part of the list (pre-recency) associated with motor-planning (Experiment 4). The results are incompatible with theories in which perceptual variability impairs serial recall due to a deficit in encoding items into a limited-capacity short-term memory space and instead point to a central role for perceptual and motor processes in serial short-term memory performance.
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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 | 0.001 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".