The event related potential technique and microstate analysis of memory guided and visually guided movements.
Bibliographic record
Abstract
While behavioral protocols have primarily been used to examine the use of memory in human movement (Elliott & Madalena, 1987; Westwood, Heath & Roy, 2003), more recent examinations have involved more imaging techniques to determine the underlying brain processes that facilitate this function (Krigolson et al., 2011). This study examines the neural correlates associated with memory guided and visually guided reaching movements using electroencephalography (EEG). Participants performed manual aiming movements to targets under varying visual feedback (full-vision & no-vision) and delay (2 or 5 s) conditions. In experiment 1, conditions were randomized across trials while in experiment 2 conditions were blocked. Using the event related potential (ERP) technique we observed the visually evoked potentials (VEPs) associated with the preview of the target as well as movement related potentials associated with movement execution. The behavioral findings from the study replicated the typical advantage of having visual feedback i.e., greater accuracy and precision with vision. Additionally, the electrophysiological data revealed differences in brain potentials across vision conditions during target encoding and movement execution within the blocked protocol. Notably, brain potentials over parietal electrodes were suppressed in memory guided reaches as compared to visually guided reaches. Moreover, a longer delay period in no-vision revealed greater suppression of brain potentials. A micro-state analysis (i.e., Cartool software) involving the full electrode montage was used to examine the clustering of activity across the scalp over time. The results of this analysis showed differences in microstate duration across vision conditions that not only reinforce the basic ERP findings but also providing greater clarity on the potential underlying neural processes sub-serving manual aiming. Meeting abstract presented at VSS 2014
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".