What’s the status of the relationship between complexity and dimensionality in visual working memory? It’s complicated.
Bibliographic record
Abstract
Working memory (WM) has been studied extensively for the past few decades relying on tightly controlled stimuli that varied based on 2D surface features (e.g., color, orientation, etc.). Although there are some attempts at assessing WM for more complex items, most studies have reported mixed results from dichotomous choice paradigms. Moreover, real-world objects have dimensionality, and are often highly complex, and yet studies have reported increases in performance when compared to abstract items. Thus, the effects of complexity and dimensionality on WM performance remains unclear. In this study, we used a continuous report, delayed-recall task to evaluate the psychophysical properties of memory representations for stimuli that vary in complexity/dimensionality. In Exp.1 (N=45), we used a load manipulation (i.e.,1,2, or 4 items) in which participants were required to report the orientation of a either simple lines or complex 3D stimuli. Overall recall error was worse for complex 3D shapes than for lines. Moreover, using a mixture model, we demonstrate only guess rates are affected by complexity. A Bayesian model selection analysis confirmed that that for most participants precision increased with set size for both simple and complex shapes, whereas guess rates increased only for the complex stimuli. In Exp.2 (N=55), we compared delayed-recall performance for simple lines, complex 2D stimuli and 3D stimuli. WM capacity(k) was also obtained using a change-detection task for all stimuli, as well as for colored squares. Results corroborate the findings from Exp.1, and demonstrated reduced performance for complex 2D stimuli compared to similar 3D stimuli. Partial correlations, controlling for color or line capacity, also revealed some stimulus-specific effects on recall performance independent of general memory capacity. Together, these results demonstrate that complexity and dimensionality have different effects on VWM performance, and also reveal that some aspects of performance on a delayed-recall task may be stimulus specific.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".