Differences in Inter-Item Salience at Encoding Produce Imbalanced Attentional and Representational Visual Working Memory Biases
Bibliographic record
Abstract
We examined whether the competitiveness of a given visual working memory representation, with regard to memory-driven capture (Study 1) and inter-item memory distortions (Study 2), is modulated by salience disparities present at encoding. In both studies, individuals encoded the colors of two slanted rectangles embedded within a broader cluster of vertically oriented rectangles. For the task-relevant stimuli, salience was operationalized according to the rotation of each item, with one (high-salience) item oriented to a 45-degree angle, which popped-out from the background, and one (low-salience) item oriented to a 12-degree angle, which was less differentiated from the background items. In Study 1, individuals had to either report the color of a probed item or perform a visual search task (unpredictably). To measure memory-driven capture, a task-irrelevant color-singleton was always present in the visual search displays, which could match the color of the high-salience item, the color of the low-salience item, or neither of the two memorized colors. Here, we found memory-driven capture to be limited to the high-salience item. To examine the role of encoding salience on inter-item memory distortions, in Study 2, the colors of the two memorized items were held to a constant difference of 45-degrees in a circular hue space. Additionally, probed items were tested using 2AFC judgments in which a visually similar lure was positioned either towards or away from the unprobed item in hue space. Through these judgements, we found representations of the low-salience items to be distorted by high-salience items (i.e., attractive bias), while low-salience items had no effect on representations of high-salience items. Overall, we demonstrate that, when salience-based differences are present at encoding, the most salient item holds a competitive advantage, in that, it more strongly influences which visual inputs are prioritized in the environment and skews the representations of less salient items.
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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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".