Properties of a flexible visual short-term memory resource
Bibliographic record
Abstract
For the last few decades, there has been considerable debate as to whether visual short-term memory (VSTM), the capacity limited memory system for the short-term storage of visual information, is a continuous or discrete resource. One property that has been identified that is consistent with a continuous resource is flexible allocation; in a delayed- recall task, when cues manipulate the priority (task relevance) of the sample items to decouple resource allocation from set-size, performance has been found to vary with the proportion of allocated resources. However, the extent and limits of this property have yet to be identified. Moreover, discrete resources could account for some previous findings through probabilistic encoding. The current thesis aimed to examine the properties and limits of flexible resource allocation in VSTM. In Chapter 2, I examined whether VSTM resources could be allocated to three levels of attentional priority. Although possible, examining individual differences in the strategies participants used revealed that the majority of participants do not use all three priority levels. Chapter 3 investigated an alternative resource allocation strategy, whereby the strategic use of a discrete memory resource to store the most relevant memory items would be encoded probabilistically. Response precision better matched predictions of flexible allocation. Further, I directly tested probabilistic encoding by estimating the proportion of “in-memory responses” and comparing it with individuals’ estimated capacity. Results again did not support a probabilistic encoding strategy. A criticism of flexible allocation is that very low precision memories are indistinguishable from out-of-memory responses. In Chapter 4, I examined flexible allocation using a two alternative forced choice (2AFC) task intermixed with continuous response trials, as 2AFC can show evidence of weak memory through better-than-chance recognition performance. Results demonstrated that participants performed better than chance for very low-priority items. This suggests that these items are stored as low-resolution memory representations, rather than being out of memory altogether. Collectively, these studies reveal properties of a flexible VSTM resource. Taken together, these data further suggest that any model that cannot accommodate a dynamic, flexible resource should be abandoned.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".