Bridging Working Memory and Imagery: Encoding induced alpha EEG activity reveals similar neural processes
Bibliographic record
Abstract
While both imagery and visual working memory address the mental representation of visual information, it remains unclear whether the representations of information during these processes are mediated by similar mechanisms. Albers et al. (2012) were able to demonstrate that working memory representations can be identified and tracked down during a mental imagery rotation by decoding fMRI activity detected in the primary visual cortex. A recent study by Foster et al. (2016) reported that it is possible to identify the feature of an object held in working memory by applying an encoding model on induced alpha activity (8-15Hz). In an attempt to determine the similarities between imagery and working memory, we replicated Foster et al. (2016) and extended their findings by investigating the behavioural and neural properties imagery. A forward encoding model was applied to EEG activity recorded while participants were holding the orientation of a stimulus in working memory and then transformed through a mental rotation of 60°. The reconstruction of orientation selectivity profiles revealed the orientation of the working memory representation and reliable changes in the mental representation during the imagery manipulation. Furthermore, the behavioural results indicate that the level of precision in the report of the transformed orientation feature is comparable with typical working memory precision. These results suggest that visual working memory and imagery share similar neural and behavioural mechanisms. 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 | 0.001 |
| 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) | 0.001 | 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".