Symbolism itself does not improve memory for elements on the periodic table
Bibliographic record
Abstract
Recent work demonstrates that symbols (e.g., $) are reliably better remembered than their word counterparts (e.g., 'dollar'). It remains an open question whether the memory benefit observed for symbols is due to their unique visual form, or because they offer a symbolic representation of to-be-remembered information. Here, we assessed memory for symbols on the periodic table of elements, which could be presented in symbol format (e.g., H) or word format (e.g., Hydrogen), and compared both to memory for meaningless letters (e.g., J). These stimuli were selected because they all share the same visual features and the former two share the same meaning. Memory was compared across individuals with and without a background in chemistry. In non-experts, memory was highest for words relative to symbols and meaningless letters. In experts (students who had passed an introductory chemistry course), however, memory for words and symbols was equivalent, with both higher than for meaningless letters. Results suggest that prior knowledge of what a symbol means is necessary to gain a memory benefit over semantically-void information, but is not enough to boost memory relative to words. We suggest that using a concrete visual symbol to represent an abstract concept is not enough to confer a memory advantage relative to words; a meaningful and visually distinctive symbol may be necessary.
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.
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 | 0.000 | 0.002 |
| 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 | 0.001 |
| 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".