Self-prioritization during stimulus processing is not obligatory
Bibliographic record
Abstract
An emerging literature has suggested that self-relevance automatically enhances stimulus processing (i.e., the self-prioritization effect). Specifically, during shape-label matching tasks, geometric shapes associated with the self are identified more rapidly than comparable stimuli paired with other targets (e.g., friend, stranger). Replicating and extending work that challenges the putative automaticity of this effect, here we hypothesized that self-relevance facilitates stimulus processing only when task sets draw attention to previously formed shape-label associations in memory. The results of a shape-classification task confirmed this prediction. Compared to shapes associated with a friend, those paired with the self were classified more rapidly when participants were required to report who the stimulus denoted (i.e., self or friend). In contrast, self-relevance failed to facilitate performance when participants judged either what the shape was (i.e., triangle or square, diamond or circle) or where it was located on the screen (i.e., above or below fixation). These findings further elucidate the conditions under which self-relevance does-and does not-influence stimulus processing.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".