Parieto-occipital alpha power dynamics selectively code for the storage of spatial locations in visual working memory
Bibliographic record
Abstract
Visual working memory (VWM) allows us to actively represent a limited amount of visual information in mind at a given moment. Recent electrophysiological studies have consistently shown that modulations of the power of parieto-occipital alpha activity (8-13Hz) is directly involved in active maintenance of VWM representations. For example, the reduction of the parieto-occipital alpha power observed during the VWM retention interval shows the capacity-limited set size effect predicted by the behavioral measures of VWM capacity (Erikson, et al., 2016; Fukuda, Mance, & Vogel, 2015; Fukuda, Kang, & Woodman, 2016). Furthermore, the topographical distribution of this alpha power modulation during VWM delay can be used to decode the content of VWM (Foster, et al., 2016; Fukuda, Kang, & Woodman, 2016; Samaha, Sprague, & Postle, 2016). In this study, we sought to extend this finding by specifying the nature of the representation in VWM that are reflected in these parieto-occipital alpha power dynamics. More specifically, we had participants maintain location, color, or their conjunction in a short-term memory task while we recorded their electroencephalograms (EEGs). Pattern classification results revealed that location information, but not color information, can be reliably decoded from the topographical distribution of the parieto-occipital alpha power during the retention interval of the memory task. This finding clearly demonstrates the selective sensitivity of the parieto-occipital alpha activity to the storage of spatial locations in VWM. Meeting abstract presented at VSS 2017
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".