The role of probe-trial distracters in the production/removal of the Spatial Negative Priming Effect
Bibliographic record
Abstract
In spatial negative priming (SNP) tasks, trials are presented in pairs; first the ‘prime’ and then the ‘probe’. Target and/or distractor events appear on both trials and probe target reaction time is significantly lengthened when a target arises at a former distractor-occupied location (ignored-repetition [IR] trial) relative to when it appears at a new location (control [CO] trial). This latency inequality, which is not inevitable, defines the SNP effect. Here the influence of prime and probe trial distractor identity similarity on restoring the SNP effect when its prevention was successfully motivated by the use of a .25 (distracter present)/.75 (distracter absent) condition was examined. Two results were important: (1) the SNP effect was restored when the probe distracter identity fully matched that of the prime trial, but not when distracter identities partially or totally mismatched, showing a retrieval role for the probe distracter, and (2) target-repeat trial facilitation showed the same pattern, present with full matches, otherwise being absent. These results showed that prime-trial processing representations are stored episodically in location tasks and that event identities are part of the episode making distracter event identity matches critical for prime representation retrieval. Additionally, event numbers were not part of the episode so that matching event numbers between prime and probe trials were not important for retrieval of stored prime representations. Acknowledgments: NSERC supported
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".