Working memory prioritization changes bidirectional interactions with visual inputs.
Bibliographic record
Abstract
Items stored in visual working memory often differ in priority. Typically, observers will shift their internal attention toward items that are relevant for impending behavior and away from those that may become relevant later. These distinct states of priority are theorized to influence bidirectional interactions between memoranda and new visual inputs by modulating their susceptibility to retroactive and proactive report biases, respectively. However, prior research has produced limited and conflicting evidence on this topic due to reliance on inconsistent retrodictive cues (retro-cues) that incentivize memory prioritization. To address this, we used a double-serial retro-cue paradigm that incentivized the complete prioritization of one of two unfamiliar shape memoranda for use in a comparison with a perceptual probe before a second, independent cue instructed observers to report one of their two memories (Experiments 1-2) or the perceptual probe itself (Experiment 2). We found that observers reported robust retroactive and proactive biases, but that only retroactive biases were modulated by prioritization. Reports of prioritized memories were more precise and contained smaller attractive biases toward the probe than unprioritized memories, whereas probe reports were biased comparably toward each. These findings reveal an asymmetrical effect of prioritization on the reciprocal interactions between new and existing visual representations. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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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 | 0.007 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".