Effects of distractor interference cannot be mitigated by predictive cues
Bibliographic record
Abstract
The contents of visual working memory (VWM) guide daily behaviours. However, VWM is severely capacity limited, making it critical to protect its contents from distracting information. VWM contents are subject to interference in numerous ways, one of which is a bias referred to as “attractive pull”, wherein reports of remembered features are biased toward distractor features (Huang & Sekuler, 2010; Rademaker, Bloem, De Weerd, & Sack, 2015). Here we investigated if we can protect VWM contents against such interference by making distractor presence predictable. Participants remembered the orientation of a target Gabor across a short delay, and we sometimes presented a distractor Gabor during this delay. To assess the effects of attractive pull, we manipulated the orientation difference between the target and distractor Gabors, with larger differences expected to increase the “pull” of the distractor. We also manipulated target encoding time by varying target/distractor stimulus onset asynchrony (SOA), with increased “pull” expected for shorter encoding times. To assess for control over attractive pull, some blocks included a cue at the beginning of the trial to indicate whether or not a distractor would be presented on that trial (predictive blocks) while other blocks did not provide any information (non-predictive blocks). As expected, attractive pull increased with decreasing SOAs, and with larger differences between the target and distractor orientations. However, the predictive cue was not able to mitigate these effects. We suggest that even if presented with a predictive cue prior to target feature encoding, participants cannot effectively prepare to protect VWM contents.
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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 | 0.010 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".