Extrastriate Body Area (EBA) Activation is Greatest During Viewing of a Dance Sequence Compared to Visualization and Movement: Evidence for Learning and Expertise Effects
Bibliographic record
Abstract
Overlapping regions of the mirror neuron network (MNN) are activated to varying extents during viewing, visualization, and execution of movement (Grèzes and Decety, 2001). Although recent evidence has implicated the extrastriate body area (EBA) as a compensatory visuomotor processing area (van Nuenen et al., 2012, Urgesi et al., 2007), its role in motor execution continues to be debated (Astafiev et al. 2004; Peelen and Downing 2005) and the influences of learning and expertise on EBA activation remain unknown. To clarify the role of EBA in the MNN, we scanned 11 expert ballet dancers and 10 controls using functional magnetic resonance imaging (fMRI) during three tasks: viewing a ballet dance, visualizing a ballet dance, and a motor localizer task. The expert group was scanned up to four times over a 34-week programme to ascertain any putative learning effects within the EBA. Our results show that the viewing task elicited the strongest bilateral EBA activation (left EBA: F(2,30) = 76.31, P<0.001, right EBA: F(2,34)= 51.171, P<0.001), with evidence for learning effects and increased bilateral activation during the visualization task over time (P<0.05). Finally, significant contralateral EBA activation during movement execution in control subjects only demonstrates its modulation with experience (PBonf<0.05). These results provide a composite of the role played by the EBA as a higher-order visual processing area within the MNN, primarily subserving action observation of complex sequences of naturalistic whole-body movement and modified by experience and motor learning. Meeting abstract presented at VSS 2014
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".