On the relationship between spatial attention and semantics in the context of a Stroop paradigm
Bibliographic record
Abstract
A controversial issue in the literature on single word reading concerns whether semantic activation from a printed word can be stopped. Several reports have claimed that, even when attention is directed to a single letter in a word, semantic interference persists full blown in the context of variants of Stroop's paradigm. Incidental word recognition is thus claimed to be unaffected by directed spatial attention and hence to be automatic by this criterion. In contrast, the literature examining the relation between intentional visual word recognition and spatial attention in tasks like lexical decision and reading aloud suggests that spatial attention is a necessary preliminary to lexical/semantic processing of a word. These opposing conclusions raise the question of whether there is a qualitative difference between incidental and intentional visual word recognition when spatial attention is considered. We first consider the methodology from Stroop experiments in which putatively narrowed spatial attention manipulations failed to prevent interference from semantics. We then report a new experiment that better promotes focused spatial attention. The results yield clear evidence that the effect of semantic activation can indeed be sidelined because one or more prior processes were in large measure stopped. We conclude that incidental word recognition is not automatic in the sense of occurring without any kind of attention.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".