Bibliographic record
Abstract
A core set of fronto-parietal brain regions is implicated in a wealth of cognitive functions.Researchers have suggested that working memory, a fundamental element in many higher-order processes, is the underlying mechanism supported by this network.However, the idea that the same fronto-parietal network underlies the qualitatively different tasks employed throughout visual working memory research is contentious.Instead, systems neuroscience has adopted the view that cognition arises out of the dynamic interaction of several large-scale networks.A hierarchical split divides these networks into an extrinsic system, which governs attention to the external environment, and an intrinsic system, which guides internally-directed processes.Given visual working memory involves a two-way connection between perceptual input and internal representations, the current dissertation uses converging methodologies to explore whether tasks that vary in their exogenous and endogenous attentional demands are likely supported by different network dynamics.A quantitative meta-analysis used stress as a paradigm to investigate the differential effects of exogenous and endogenous distraction on visual working memory task performance.This analysis was followed by a controlled stress study that examined whether endogenous distraction, instigated by a psychosocial stressor, differentially influenced visual maintenance versus mental rotation.Finally, an electroencephalographic study was conducted where participants were required to store visual information despite an ongoing external distractor.Taken together, the data presented from these three studies suggest key differences between maintenance and Corbetta, M.,
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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 | 0.000 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".