The electrophysiological indicators of fronto-central networks for visual control during goal-directed reaching
Bibliographic record
Abstract
In this study the neural correlates of goal-directed reaching were investigated under varying conditions of visual feedback. Recent studies have revealed a negative brain potential occurring over fronto-central regions of the brain following peak velocity of a reaching movement. Specifically, when large errors are encountered, the amplitude of this potential is greater as compared to movements with less error (Torrecillos et al., 2014). While it is suggested that this brain potential is a consequence of error detection (Torrecillos et al., 2014), others have suggested that it may simply reflect the motor output associated with the decelerating limb (Kirsch & Henninghausen, 2010). In order to further investigate what this potential holds we systematically manipulated the availability of visual feedback of the hand and target. Participants performed aiming movements to two target locations, with vision of 1) the hand and target; 2) hand only; 3) target only; 4) without vision of the hand and target. If indeed this brain potential is reflective of visual feedback and error processing, we may expect to see a decrease in its amplitude when visual feedback is unavailable as individuals are unable to assess error with real-time vision. Behavioural results were consistent with previous research, notably a decrease in trajectory amendments associated with online control for conditions where vision of the hand is withheld (e.g., Heath, 2005). For the electrophysiological results, we also found a modulation of the negative brain potential over frontal and central electrodes, associated with the variations in visual feedback and target location. Furthermore, wavelet analyses revealed a corresponding modulation in the activity in the theta frequency band with respect to visual feedback availability. In all, these findings support the idea that the fronto-central neural structures are involved with the real-time assessment of reach errors. Acknowledgments: NSERC
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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".