The Attentional "White Bear" Evades Visual Working Memory
Bibliographic record
Abstract
In visual search, cueing a feature (e.g., color) of a to-be-ignored item typically offers no attentional advantage and sometimes interferes with task performance. Since resource allocation models contend that the resolution of visual working memory (VWM) representations is determined by the distribution of attention across items, we sought to investigate the effect of featurally cueing task-irrelevant items on VWM. In Experiment 1, participants were presented displays of isoluminant diamonds that varied in hue and were to indicate the location of a "chip" that was present on the top or bottom of one of the diamonds. Prior to target presentation, a cue indicated the color of one non-target diamond that could be ignored (Ignore condition), or provided no such information (Neutral condition). Our results suggested that participants were unable to suppress attention to the cued items; performance was equal for both conditions. Experiment 2 employed a similar cueing procedure, but with a delayed estimation task. Participants studied displays of briefly presented colored squares then reported the color of one probed item following a 900 ms delay. Critically, ignore and neutral cues were presented before study displays. After decomposing response errors into a three-parameter mixture model, we found that participants held more precise representations of studied items in the Ignore compared to the Neutral condition. Experiment 3 again used the delayed estimation task, but now intermixed delays of 300 ms and 1500 ms to determine if the cueing benefit occurred at encoding or during maintenance. Again, we found precision to be higher in the Ignore condition regardless of delay length. These findings suggest that while cueing features of task irrelevant items is not sufficient to suppress attention to these items, such cues do benefit the encoding of task-relevant items. Meeting abstract presented at VSS 2018
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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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 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".