Object-location binding in visual working memory prevents effective updating
Bibliographic record
Abstract
Despite the importance of updating visual working memory (VWM) representations in our dynamic visual world, memory performance has been shown to be reliably worse for spatially dynamic objects compared to static objects. This is possibly due to robust binding of object features to their original locations. Here, we conducted two experiments (preregistered) to test the effectiveness of intentionally updating object spatial information in a dynamic spatial context. Each trial began with the presentation of four different colored squares within white placeholders. In Experiment 1, subjects were instructed to maintain and update all four memory items; in Experiment 2, subjects were retroactively cued to maintain and update only two of the memory items, allowing subjects to drop the adjacent non-cued memory items. In half of the trials, during the subsequent memory delay, the placeholders would rotate one position clockwise or counterclockwise. The experiment consisted of two instruction blocks: Update (instructed to mentally update the items’ locations when the placeholders rotated) and Ignore-Rotation (instructed to maintain the memory of the items in their original locations even when the placeholders rotated). At the end of the trial, participants were presented with a spatial probe and reported their memory of the color of the item in that location (original or updated, depending on instruction block) on a continuous color wheel. In Experiment 1, we observed large performance decrements when participants had to both intentionally update and, critically, attempt to ignore task-irrelevant rotations. In Experiment 2, even with a reduced load on VWM resources (only maintaining/updating two items), performance decrements persisted in both instruction contexts when the placeholders rotated. Analysis of systematic feature errors across both experiments revealed greater misreporting of nontargets when the display rotated, compared to static trials, regardless of instruction and even when those features were meant to be dropped from VWM.
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 | 0.007 |
| 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 | 0.001 |
| Research integrity | 0.001 | 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".