Bibliographic record
Abstract
Consider a paradigm in which a visual stimulus is first presented and then a feature of that stimulus appears as either a target or distractor in a subsequent visual search task. It has been shown that if a response is made to the initial appearance of the stimulus, a validity effect is found; search times are faster when the stimulus is part of a target than when it is part of a distractor. This validity effect disappears if a response is withheld to the initial stimulus. Our study demonstrates that there is a validity effect and an inverse validity effect. First, like previous studies, we replicated the validity effect when the first stimulus was responded to. Second, unlike previous studies, when the first stimulus was not responded to, in four experiments we consistently observed an inverse validity effect such that faster search times occurred when the initial stimulus was contained in a distractor. Third, when we changed the second task from a visual search to stimulus presented in isolation, only the inverse validity effect was found. Fourth, when we increased the overlap between the first and second response buttons, the inverse validity effect increased. Based on our findings, we argue that the validity effect is driven by biased competition; responding to the first stimulus increases the attentional weights assigned to that stimulus's features such that it wins the competition for selection in the search phase. The inverse validity effect, however, is driven by feature binding into event files as there is a partial repetition of the event file formed from the first response when responding to the search event. Meeting abstract presented at VSS 2016
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.032 | 0.004 |
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".