Unmasking the Effects of Orthography, Semantics, and Phonology on 2AFC Visual Word Perceptual Identification
Bibliographic record
Abstract
In the two-alternative forced-choice (2AFC) task, the target stimulus is presented very briefly, and the participants must choose between two options as to which was the presented target. Some past research (Grossi et al., 2009; Haro et al., 2019) has assumed that the 2AFC word identification task isolates orthographic effects, despite orthographic, semantic, and phonological differences between the alternative options. If so, performance should not differ between word target/nonword foil pairs and British/American word pairs, the latter of which only differ orthographically. In Experiment 1, accuracy and sensitivity were higher during word/nonword trials than British/American trials when participants stated their response was not a guess, demonstrating that phonological/semantic processing contributes to 2AFC performance. In Experiment 2, target visibility was manipulated by increasing the contrast between target and mask for half the trials. Experiment 2 showed that target visibility did not interact with pair type on reaction time, which suggests phonological/semantic processing did not result in feedback to orthographic encoding in this task. This study demonstrates the influence of phonological/semantic processing on word perceptual identification, and shows that 2AFC word identification does not isolate orthographic effects when word/nonword pairs are used, but using British/American word pairs provides a method for doing so. Implications for models and future research are discussed.
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 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 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".