To bind or not to bind: Addressing the question of object representation in visual short-term memory
Bibliographic record
Abstract
Visual short-term memory (VSTM) is a capacity limited resource, which is consistently estimated to hold about four visual items at a time. There is, however, debate in the literature about what constitutes an "item" and how resources are allocated within VSTM. Some research suggests information is stored in VSTM as discrete objects; however, there is also evidence suggesting that within-object features alter VSTM performance. The present study addresses the question of whether VSTM load effects reflect the number of discrete objects and/or the number of within-object features. An electrophysiological correlate of VSTM--the contralateral delay activity (CDA)--was measured while participants performed a lateralized change-detection task, in which to-be-remembered items varied in the number of features and locations. Each trial contained either a solitary simple feature (shape, color, or orientation) or one of two multifeature arrays: three features presented at three separate locations or three features bound at one location. While presenting multiple features--regardless of whether they are at discrete locations or bound within a single object--resulted in greater CDA amplitude relative to a solitary feature, there was a dissociation in the distribution of activity between the two multifeature conditions, such that the CDA at site P1/P2 was sensitive to the number of discrete objects, while activity at P7/P8 was most enhanced when multiple features were bound in one object. The findings demonstrate the inhomogeneity of the CDA and suggest this electrophysiological marker may reflect both discrete object individuation/separation and flexible feature-feature binding in VSTM.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".