Reverse-engineering what makes a symbol memorable
Bibliographic record
Abstract
Symbols may represent the first form of human visual communication, yet little is known about the cognitive and neural mechanisms supporting memory for these pervasive graphics. By investigating memory for everyday symbols, we can understand how abstract concepts are concretized with simple referents and later processed in visual memory systems. Recently, symbols have been found to be highly memorable, especially relative to words, but it remains unclear what drives their heightened memorability. We identified the key visual and conceptual attributes driving high memorability for symbols. Participants were tested on their memory for conventional symbols (e.g., !@#$%) before sorting them based on visual or conceptual features. Principle component analyses performed on the sorting data revealed which of these features predict memory for symbols. Generative artificial intelligence was then used to accentuate or downplay these predictive features to create a set of memorable and forgettable novel symbols. A memory test revealed that symbols designed to be memorable were not only better recognized than those designed to be forgettable, but they also afforded superior recall of associated abstract words. This work demonstrates that certain features drive memory for images and offers clear evidence that memory can be intentionally engineered.
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 | 0.001 | 0.006 |
| 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 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".