Text adaptation: Aftereffects for word-identity and handwriting-style, and the effect of the orthogonal variable.
Bibliographic record
Abstract
Background: High-level face aftereffects have been used to explore face representations. Written words are another high-level stimuli, which activate a similar network on fMRI as faces, only left dominant rather than right. Adaptation for word stimuli has been less investigated: if word aftereffects were found, this might prove useful for exploring the nature of word representations as well. Objective: We used a perceptual-bias paradigm to investigate aftereffects for two orthogonal properties of text, word identity and handwriting style, and see if such aftereffects are affected by variations in the orthogonal dimension. Methods: Two 4-letter and two 5-letter words were selected from the MRC psycholinguistic database, matched for familiarity, imagability and concreteness. Each set of words was handwritten by two people. For word-identity adaptation, test images were created by morphing between the two words of the same length in the same handwriting. Trials showed an unmorphed word for 5 seconds, followed by a brief view of an ambiguous test, after which subjects indicated which word the test most resembled. In one block, the adaptor and test had the same handwriting, differed in the second. For handwriting-style adaptation, morphs were between two handwritings for the same word. Trials showed an unmorphed word for 5 seconds, followed by an ambiguous test, subjects indicated which handwriting the test most resembled. In one block, the adaptor and test shared the same word; differed in the second. Results: We found a word-identity aftereffect but no handwriting aftereffect. The word-identity aftereffect was equally strong when the handwriting differed between adapting and test stimuli, indicating complete transfer of word-identity adaptation across handwriting style. Conclusion: Similar to face aftereffects, adaptation for word-identity can be shown. Complete transfer across handwriting style both supports a high-level origin of this aftereffect and suggests that word representations are independent of the carrier style. Meeting abstract presented at VSS 2012
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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".