Foveal Splitting of Compounds and Pseudocompounds using Anaglyphs
Bibliographic record
Abstract
We investigated the nature of linguistic codes at the earliest moments of visual word recognition by employing red-blue anaglyph glasses to split compounds (e.g., snowball) and pseudocompounds (e.g., cartridge) along the fovea’s vertical meridian. By hypothesis, anaglyphs allowed us to manipulate the role of retinotopic projections during visual processing, whereby word segments were projected to the right or left hemisphere via the ipsilateral or contralateral pathways. Furthermore, this technique allows the embedded words (i.e., constituents) of compounds and pseudocompounds to be presented independently in the visual word form area. Seventy-one participants performed a visual masked lexical decision task, where they made word-nonword judgements. Stimuli were presented for 133 milliseconds either completely in black (both pathways), red/blue (ipsilateral pathways) and blue/red (contralateral pathways). The compounds and pseudocompounds were split into their constituents (legal split) or one letter to the left or to right of the morpheme boundary (illegal split). Compounds varied in the degree of semantic transparency, either transparent (T) or opaque (O), of constituents (e.g., TT, snowball; OT, crowbar; TO, jailbird; OO, hogwash). The accuracy and response times (RTs) to the lexical decision task were analyzed using linear mixed effects models. Results suggest an advantage for words processed through both visual pathways than when they are projected contralaterally (both in accuracy and RTs) and ipsilaterally (in accuracy only). Furthermore, legally split stimuli were judged faster and more accurately than illegal split stimuli regardless of word type. Responses to compounds were more accurate when compared to pseudocompounds. Taken together, these findings suggest that the early visual word recognition system is sensitive to the internal structure of compounds and pseudocompounds, but blind to the semantic contribution of their constituents.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".