The inversion effect in word recognition: The effect of language familiarity and handwriting
Bibliographic record
Abstract
Humans have expertise with visual words and faces. One marker of this expertise is the inversion effect. This is attributed to experience with those objects being biased towards a canonical orientation, rather than some inherent property of object structure or perceptual anisotropy. To confirm the role of experience, we measured inversion effects in word matching for familiar and unfamiliar languages. Second, we examined whether there may be more demands on reading expertise with handwritten stimuli rather than computer font, given the greater variability and irregularities in the former, with the prediction of larger inversion effects for handwriting. We recruited two cohorts of subjects, one fluent in Farsi and the other in Punjabi, neither of whom were able to read the other's language. Subjects performed a match-to-sample task with words in either computer fonts or handwritings. Subjects were more accurate and faster with their familiar language, even when it was inverted. Inversion effects were present for the familiar but not the unfamiliar language. The inversion effect in accuracy for handwriting was larger than that for computer fonts in the familiar language. We conclude that the word inversion effect is generated solely by orientation-biased experience, and that demands on this expertise are greater with handwriting than computer font.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".