Effects of language familiarity and style (font vs. handwriting) on the word inversion effect
Bibliographic record
Abstract
Visual words and faces have very different properties, words being two-dimensional high-contrast binary stimuli and faces having complex mobile three-dimensional shapes. However, they are both visual stimuli for which humans have high expertise, and both activate similar cerebral networks (albeit with opposing hemispheric asymmetries), raising the possibility that they might share common perceptual mechanisms and effects. In the present study we examine word processing for one prominent effect described in face processing, the inversion effect. To capture the effect of expertise, we compared recognition for familiar and unfamiliar languages. To determine if stimulus variability played a role, we also compared computerized font and handwriting. We recruited two groups of 20 subjects, one fluent in Farsi and one in Punjabi, with neither familiar with the other language. Stimuli were single words of 5-7 letters in length, in one of 6 handwriting or 6 font styles, shown either upright or inverted. Subjects performed a three-alternative match-to-sample task, with 432 trials total. In addition, subjects performed the Cambridge Face Memory Test (CFMT). Subjects had higher accuracy and faster reaction times with the familiar language, and with computerized font. There was a word inversion effect for the familiar but not the unfamiliar script. The inversion effect for accuracy was almost twice as large for handwriting than computerized font. Performance with the inverted familiar language was still superior to that with the unfamiliar language, indicating that language familiarity still facilitates the processing of inverted stimuli. Word inversion effects did not correlate with face inversion effects on the CFMT. We conclude that experience does generate a word inversion effect, and that this effect is greater for less regular script, when reading requires generalization across natural variations in handwriting.
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 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 | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".