Symbol superiority: Why $ is better remembered than ‘dollar’
Bibliographic record
Abstract
Memory is often superior for pictures relative to words. Dual-coding theory (Paivio, 1969) proposes that this is because pictures lead to imagery plus verbal labelling, taking advantage of two codes, whereas words provide only a verbal representation in memory. We investigated whether common symbols (e.g., !@#$%&) are processed with dual codes, like pictures, or a single code, like words. Participants’ memory were tested for symbols or words (e.g., $ or ‘dollar’). We predicted that symbols are processed using imagery, much like pictures, and as a result memory for symbols should be superior to words. Our prediction was supported across four experiments: Symbols were consistently better remembered than words, regardless of setting, design, or retrieval test type. In a fifth experiment, memory for symbols was driven in-part by participants' familiarity with the stimuli as well as the highly memorable visual properties that symbols possess (as estimated by the ResMem neural network). These findings are consistent with the idea that symbols benefit memory by eliciting distinct representations at encoding.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".