The components and modality-specificity of word representations in the human visual system: an adaptation study
Bibliographic record
Abstract
Background: While many studies have used adaptation to probe the neural representation of faces, few have used this to examine how words are represented in the human visual system. Last year we established that word aftereffects exist and are invariant for script style (Hanif et al, J Vis 2012: 12(9): 1060). Objective: Our goals were, first, to use adaptation to examine the contribution of components of words to the word aftereffect and second, to determine if there was cross-modal transfer of aftereffects. Methods: 30 subjects participated in Experiment 1. Two pairs of compound words of equal length were chosen as base stimuli, in upper case Arial font. Ambiguous probe stimuli were created by merging different degrees of transparencies of the pairs together in an overlay, with added Gaussian noise. The 5-second adapting stimuli were either the original words, words with the component morphemes re-arranged, or a rearrangement of the original words’ letters into a meaningless string. 12 subjects participated in Experiment 2. Two pairs of words were chosen. Probe stimuli were generated by either the same method, or a morphing procedure, for comparison. In the visual condition, the original words were presented as 5-second adapting stimuli, while in the auditory condition, the adaptor was a 4.8-second tape of different individuals saying the original word every 800ms. Results: Experiment 1 generated a 17% aftereffect for whole words, while the re-arranged morphemes generated a small 5% aftereffect, and letter strings generated no aftereffect. Experiment 2 generated a 10% aftereffect for whole visual words, irrespective of probe type, but no aftereffect from auditory words. Conclusion: Visual words have a strong representation at the whole-word level, and a minor grapheme component. As found previously for face expression and age aftereffects, there was no cross-modal transfer from the auditory sense. Meeting abstract presented at VSS 2013
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".