Effects of Iconicity in Recognition Memory
Bibliographic record
Abstract
Iconicity refers to a resemblance between word form and meaning. Previous work has shown that iconic words are learned earlier and processed faster. Here, we examined whether iconic words are recognized better on a recognition memory task. We also manipulated the level at which items were encoded-with a focus on either their meaning or their form-in order to gain insight into the mechanism by which iconicity would affect memory. In comparison with non-iconic words, iconic words were associated with a higher false alarm rate, a lower d' score, and a lower response criterion in Experiment 1. We did not observe any interaction between iconicity and encoding condition. To test the generalizability of these findings, we examined effects of iconicity in a recognition memory megastudy across 3880 items. After controlling for a variety of lexical and semantic variables, iconicity was predictive of more hits and false alarms, and a lower response criterion in this dataset. In Experiment 2, we examined whether these effects were due to increased feelings of familiarity for iconic items by including a familiar versus recollect decision. This experiment replicated the overall results of Experiment 1 and found that participants were more likely to categorize words that they had seen before as familiar (vs. recollected) if they were iconic. Together, these results demonstrate that iconicity has an effect on memory. We discuss implications for theories of iconicity.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".