The Effect of Target Repetition on Semantic Priming in a Three-Target RSVP Task
Bibliographic record
Abstract
This study used a rapid serial visual presentation task to examine the impact of target relatedness on report accuracy. In this task participants were shown a series of briefly presented words and were required to identify the three colored target words from the stream of distractor words. In two experiments participants either recalled the three targets words from memory at the end of each trial or recognized the targets from a list of all possible targets. The first target shown on each trial was unrelated to the second or third target. The second and third targets within each stream were semantically related (e.g., dog and cat) on half the trials and unrelated on the other half the trials (e.g., table and cat). The effect of the second and third targets sharing a relationship was examined for Target 3 accuracy. Target 3 accuracy was greater if it was preceded by a related Target 2, compared to when Targets 2 and 3 were unrelated. This shows a semantic priming effect (facilitation effect) for Target 3 accuracy, when the both Target 2 and 3 were identified. In contrast, when Target 2 was missed or incorrectly reported there was no difference in accuracy for related or unrelated target contexts (no priming or facilitation effect for Target 3). These semantic priming effects were evident in the recall and recognition tasks in Experiment 1 and 2. This study shows that a target presented during a rapid serial visual presentation task must be consciously processed to facilitate the report of a subsequently presented target word.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".