The first letter position effect in visual word recognition: The role of spatial attention.
Bibliographic record
Abstract
A prominent question in visual word recognition is whether letters within a word are processed in parallel or in a left to right sequence. Although most contemporary models posit parallel processing, this notion seems at odds with well-established serial position effects in word identification that indicate preferential processing for the initial letter. The present study reports 4 experiments designed to further probe the locus of the first position processing advantage. The paradigm involved masked target words presented for short durations and required participants to subsequently select from 2 alternatives, 1 which was identical to the target and 1 that differed by a single letter. Experiment 1 manipulated the case between the target and the alternatives to ensure that previous evidence for a first position effect was not due to simple perceptual matching. The results continued to yield a robust first position advantage. Experiment 2 attempted to eliminate postperceptual decision processes as the explanatory mechanism by presenting single letters as targets and requiring participants to select an entire word that contained the target letter at different positions. Here the first position advantage was eliminated, suggesting postperceptual decision processes do not underlie the effect. The final 2 experiments presented masked stimuli either all vertically (Experiment 3) or randomly intermixed vertical and horizontal orientation (Experiment 4). In both cases, a robust first position advantage was still obtained. The authors consider alternative interpretations of this effect and suggest that these results are consistent with a rapid deployment of spatial attention to the beginning of a target string which occurs poststimulus onset. (PsycINFO Database Record
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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 | 0.006 |
| 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 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".