Explicit Perceptual Comparisons Modulate Memory Biases Induced by Overlapping Visual Input
Bibliographic record
Abstract
It is well-established that visual working memory (VWM) and perception interact to influence behavior. For example, studies have shown that novel visual input can retroactively distort VWM representations by inducing systematic attraction biases. The size of these biases may be determined automatically by the extent to which features shared between visual input and VWM content overlap with one another. However, it may also be the case that explicitly comparing VWM content to visual input plays a causal role in modulating these observed biases. Here, we tested the hypothesis that explicitly comparing a VWM representation to a visual input causally amplifies memory biases that occur naturally as a result of overlapping visual features. In each trial of two separate experiments, participants first encoded a target visual item (i.e., color or shape) into VWM in anticipation of a continuous report that followed a blank delay interval. On a subset of trials, participants were presented a novel probe item during the blank delay and were instructed to compare it to the target held in VWM. The memory biases observed in this task were then compared to those observed in a separate task where the same participants ignored the probe (Experiment 1) or encoded the probe into VWM alongside the target (Experiment 2). We found that individuals reported larger attraction biases in the target item following explicit comparisons than when they ignored or remembered the probe. A follow-up analysis revealed that memory biases were amplified when participants judged the probe to be similar—but not dissimilar—to the target item. This pattern persisted even after the distance between the target and probe items in the stimulus space was matched across trials. Taken together, these findings demonstrate that explicit perceptual comparisons causally modulate VWM biases above and beyond the effects determined by shared featural overlap.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| 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".