Effects of iconicity in lexical decision
Bibliographic record
Abstract
abstract In contrast to arbitrariness, a recent perspective is that words contain both arbitrary and iconic aspects. We investigated iconicity in word recognition, and the possibility that iconic words have special links between phonological and semantic features that may facilitate their processing. In Experiment 1, participants completed a lexical decision task (“Is this letter string a word?”) including words varying in their iconicity. Notably, we manipulated stimulus presentation conditions such that the items were visually degraded for half of the participants; this manipulation has been shown to increase reliance on phonology. Responses to words higher in iconicity were faster and more accurate, but this did not interact with condition. In Experiment 2 we explicitly directed participants’ attention to phonology by using a phonological lexical decision task (“Does this letter string sound like a word?”). Responses to words that were higher in iconicity were once again faster. These results demonstrate facilitatory effects of iconicity in lexical processing, thus showing that the benefits of iconic mappings extend beyond those reported for language learning and those argued for language evolution.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.004 | 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".