Visual short-term memory for 3D shapes reveals similar properties to simple 2D features
Bibliographic record
Abstract
In the past two decades, significant advances have been made to understand the psychophysical properties of visual short-term memory (VSTM). Most studies, however, make inferences based on memory for simple surface features of 2D shapes. Here, we examined the role of object complexity and dimensionality on the psychophysical properties of VSTM by comparing orientation memory for 2D lines and complex 3D objects in a delayed-response continuous report task, where memory load (Experiment 1) or axis of rotation (Experiment 2) was manipulated. In both experiments, our results demonstrate an overall cost of complexity that affected participants raw errors as well as their guess rate and response precision derived from mixture modelling. We also demonstrate that participants’ memory performance is correlated between stimulus types and that memory performance for both 2D and 3D shapes is better fit to the variable precision model of VSTM than to tested competing models. Interestingly, the ability to report complex objects is not consistent across axes of rotation. These results indicate that, despite the fact that VSTM shares similar properties for 2D and 3D shapes, VSTM is far from being a unitary process and is affected by stimulus properties such as complexity and dimensionality.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".