Limitations on flexible allocation of visual short-term memory resources with multiple levels of attentional prioritization.
Bibliographic record
Abstract
Studies suggest that visual short-term memory (VSTM) is a continuous resource that can be flexibly allocated using probabilistic cues that indicate test likelihood (i.e., attentional priority to those items). Previous studies using simultaneous cues have not examined this flexible allocation beyond two distinct levels of priority. Moreover, previous studies have not examined whether there are individual differences in the ability to flexibly allocate VSTM resources, as well as whether this ability benefits from practice. The current study used a continuous report procedure to examine whether participants can use up to three levels of attentional priority to allocate VSTM resources via simultaneous probabilistic spatial cues. Three experiments were performed with differing priority levels, cues, and cue presentation times. Group level analysis demonstrated flexible allocation of VSTM resources, however, there was limited evidence that participants could use three priority levels. A temporal analysis suggested that task fatigue, rather than practice effects, may interact with item priority. A Bayesian individual-differences analysis revealed that a minority of participants were using three levels of attentional priority, demonstrating that, while possible, it is not the predominant pattern of behavior. Thus, we provided evidence that flexible allocation to three attentional-levels is possible under simultaneous cuing conditions for a minority of participants. Flexible allocation to three categories may be interpreted as a skill of high performing participants akin to high memory capacity.
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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 | 0.006 | 0.024 |
| 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 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".