Spatiotemporal letter processing in visual word recognition uncovered by perceptual oscillations
Bibliographic record
Abstract
Despite decades of intense study, the spatiotemporal processing of letters in visual word recognition has yet to be elucidated, with the debate largely focusing on whether individual letters are processed serially or in parallel. The present study investigated the processing of individual letters and letter combinations through time in visual word recognition using displays where signal-to-noise ratio (SNR) varied randomly throughout a 200 ms exposure duration. In Experiment 1, SNR varied either homogeneously across all letters or independently for each letter position (cf. heterogeneous sampling). Reading accuracy was substantially greater with homogeneous than heterogeneous sampling. Experiment 2 again used heterogeneous sampling and classification images (CIs) were calculated for individual letter positions or conjunctions thereof, reflecting processing efficiency according to time during target exposure. These CIs or their Fourier transforms were passed to a classifier to assess differences in the result patterns across individual letter positions or their conjunctions. Overall, the present results indicate the following: (1) significant parallel letter processing capacity throughout exposure duration; (2) dissociable processing mechanisms for each letter position; and (3) letter position-specific mechanisms for letter conjunctions that are distinct from those for individual letters. The results also provide evidence relevant to the neural code underlying the perceptual mechanisms that were uncovered.
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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 | 0.002 |
| 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 | 0.000 |
| 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".