Orthographic learning and transfer of complex words: insights from eye tracking during reading and learning tasks
Bibliographic record
Abstract
Background Efficient word identification is directly tied to strong mental representations of words, which include spellings, meanings and pronunciations. Orthographic learning is the process by which spellings for individual words are acquired. Methods In the present study, we combined the classic self‐teaching paradigm with eye tracking to detail the process by which complex pseudowords are learned. With this methodology, we explored the visual processing and learning of complex pseudowords, as well as the transfer of that learning. We explore visual processing across exposures during the initial reading task and then measure learning and transfer in orthographic choice and spelling tasks. Results Online eye movement monitoring during the repeated reading of complex pseudowords revealed that visual processing varied across exposures with key differences based on word type Further, data from both dictation and eye movements recorded during the orthographic choice task suggested stronger learning of morphologically than orthographically complex pseudowords after four encounters. Finally, results suggested that learning transfer occurred, with similar levels of accurate recognition of new pseudowords that were morphologically or orthographically related to pseudowords learned during the reading phase than of new pseudowords never read. Conclusions The present study provides new insights into theory and methodological discussions of orthographic learning.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".