Orthographic-Semantic consistency effects in lexical decision: What types of neighbors are responsible for the Effects?
Bibliographic record
Abstract
Recent research (e.g., Marelli & Amenta, 2018; Siegelman, Rueckl, Lo, Kearns, Morris & Compton, 2022) has demonstrated a significant orthographic-semantic (O-S) consistency effect on lexical decision performance. Specifically, lexical decision latencies were faster for words with a consistent O-S relationship than for words that do not have a consistent O-S relationship, with consistency being defined in terms of the semantics of those words’ “orthographic neighbors”. Interestingly, however, the words assumed to be orthographic neighbors were different across the studies and, therefore, different factors may have been at work in the two situations. In order to more closely examine the origin of O-S consistency effects in lexical decision tasks, we first attempted to replicate both of those results. Then, we examined O-S consistency effects based on addition (e.g., cats-CAT, pant-PAN), substitution (e.g., cot-CAT, pin-PAN) and deletion (seat-SAT, road-ROD) neighbors separately for mono-morphemic English words in both the datasets used in the previous studies and, based on the former two neighbor types, in a lexical decision experiment. Throughout our data analyses, we observed that addition neighbors play an important role in producing an O-S consistency effect in lexical decision performance. In contrast, we failed to observe a significant O-S consistency effect when consistencies were computed based only on the substitution (or deletion) neighbors. Because addition neighbors involve many morphologically-related neighbors, we further examined the roles that morphologically-related and unrelated neighbors play in producing the O-S consistency effect. Those analyses revealed that the O-S consistency effect for addition neighbors is largely produced by the combination of a processing advantage when a word has many morphologically-related neighbors and a processing disadvantage when a word has many morphologically-unrelated neighbors. More broadly, this research demonstrates that readers pick up on the statistical relationships between spelling and meaning.
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".