Activation of lexical and semantic representations without intention along GPC-sublexical and orthographic-lexical reading pathways in a Stroop paradigm.
Bibliographic record
Abstract
Dual route models of reading suggest there are 2 pathways for reading words: an orthographic-lexical pathway, used to read familiar regular words and exception words, and a grapheme-to-phoneme-conversion-(GPC)-sublexical pathway, used to read unfamiliar regular words, pseudohomophones (PHs), and nonwords. It is unclear, however, whether PHs activate lexical and semantic representations without intention in the GPC-sublexical pathway to the same extent as words along the orthographic-lexical pathway. The present study explored this by introducing a novel condition, color pseudohomophone associates (CPHAs; e.g., "skigh"), in 3 experiments using the Stroop paradigm. Experiment 1 examined 4 types of stimuli: color words (CWs), color word associates (CWAs), color PHs (CPHs), and color PH associates (CPHAs), in a mixed list context. Significant Stroop effects were found for all 4 types of stimuli. To ensure the robustness of this effect, Experiment 2 was conducted using pure list contexts whereby participants received only word stimuli (e.g., CWs, CWAs) or only PH stimuli (e.g., CPHs, CPHAs). The results replicated those of Experiment 1, suggesting that CPHAs activate lexical and semantic representations without intention in the GPC-sublexical pathway. Experiment 3 added 2 novel conditions: color exception word associates (which can only be pronounced correctly using the orthographic-lexical pathway) to compare the effects obtained with color exception PH associates (which rely on the GPC-sublexical pathway for correct pronunciation). Stroop effects of similar magnitude were found for both types of stimuli, suggesting lexical and semantic representations are accessed without intention in either reading pathway to a similar degree. Implications for models of reading are discussed.
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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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".