Accessing the meanings of sublexical forms during visual word recognition
Bibliographic record
Abstract
How are complex words recognized during the early moments of visual word recognition? What roles do full word and constituent frequency play in semantic processing? The present study addressed these questions by employing a word-picture relatedness task with brief stimuli presentations designed to tap the early mapping of orthographic input onto semantic representations. The main manipulation involved first presenting a picture depicting the target word’s constituent (200 ms), followed by the presentation of the target word (56 ms). We compared the rate of positive relatedness judgements elicited by picture-word pairs between suffixed (SKI-skier), pseudo-suffixed (MOTH-mother), and non-suffixed words (CAN-canoe). Results suggest that the “constituents” of all three word types are semantically accessed, although with a suffixed word advantage. Regression analyses did not corroborate behavioral findings as no full word and constituent frequency effects were obtained. We discuss the implications of these findings for models of the visual word recognition system.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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; both teacher heads agree on what is shown here.
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".