Using Mental Set to Change the Size of Posner's Attentional Spotlight: Implications for how Words are Processed in Visual Space
Bibliographic record
Abstract
The present thesis investigated how words are processed within the context of visual search. Both explicit and implicit measures were used to assess whether spatial attention is a prerequisite for words to undergo processing. In the explicit search task, subjects searched a display and indicated whether a word was present or absent among nonword distractors. In the implicit task, priming was employed to index word processing. Subjects viewed the same search displays that were used in the explicit task, however, the displays were presented briefly and were followed by a single target letter string to which subjects performed a lexical decision. In Experiments 3 through 6, in which the target was always presented at fixation, no priming was evident. In Experiments 7 and 8 when the location of the target moved from trial to trial, priming was observed. It is argued that attentional resources are narrowly allocated to a location in visual space when target location is certain but diffusely allocated when target location is uncertain. Furthermore, processing only occurs for words that fall within the suffusion of this strategically pliable attentional beam. The results are also interpreted within the domains of perceptual cuing and attentional capture.
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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 | 0.003 |
| 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 | 0.001 | 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".