Neural measures accounting for flexibility in VSTM
Bibliographic record
Abstract
Recent evidence suggests that visual short-term memory (VSTM) resources can by allocated both continuously and flexibly. However, the neural mechanisms underlying this flexibility remain unclear. Previous studies have isolated the role of the intraparietal sulcus (IPS) in the maintenance of information in VSTM; however, it is unclear whether activity in this region reflects the flexible allocation of memory resources. In the currently study, we used functional magnetic resonance imaging (fMRI) to isolate the neural substrates mediating flexible VSTM resource allocation. Participants completed a delayed-estimation task in which they were cued to remember 1, 2, or 4 items with 100% validity, or in the critical condition, participants were cued to 1 item with 50% validity. This manipulation requires flexible resource allocation across four items. This task was previously shown to reliably influence the distribution of memory resources in a flexible manner according to the cueing probability (Emrich, Lockhart, & Al-Aidroos, 2017). IPS activity showed the expected increase in activity for the memory load manipulations. In the flexible allocation condition the IPS activity demonstrated a level of activity that suggested all four objects in memory while behavioral evidence confirmed that memory resources were flexible allocated. Several regions were identified to be more active in the flexible memory allocation condition relative to a load four condition, the largest of which were the bilateral insula, cingulate gyrus, and right dorsolateral prefrontal cortex (dlPFC). Additionally, mixed linear effects modeling revealed IPS activity did not significantly predict absolute recall error; in contrast, right dlPFC activity significantly predicted absolute recall error. These results suggest that VSTM precision is in part determined by flexible resource allocation mediated by top-down attentional mechanisms. Meeting abstract presented at VSS 2018
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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 | 0.004 |
| 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 | 0.000 |
| 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".