Modulation of cortical motor outputs by the symbolic meaning of visual stimuli
Bibliographic record
Abstract
The observation of an action modulates motor cortical outputs in specific ways, in part through mediation of the mirror neuron system. Sometimes we infer a meaning to an observed action based on integration of the actual percept with memories. Here, we conducted a series of experiments in healthy adults to investigate whether such inferred meanings can also modulate motor cortical outputs in specific ways. We show that brief observation of a neutral stimulus mimicking a hand does not significantly modulate motor cortical excitability (Study 1) although, after prolonged exposure, it can lead to a relatively nonspecific modulation (Study 2). However, when such a neutral stimulus is preceded by exposure to a hand stimulus, the latter appears to serve as a prime, perhaps enabling meaning to the neutral stimulus, which then modulates motor cortical excitability in accordance with mirror neuron-driving properties (Studies 2 and 3). Overall results suggest that a symbolic value ascribed to an otherwise neutral stimulus can modulate motor cortical outputs, revealing the influence of top-down inputs on the mirror neuron system. These findings indicate a novel aspect of the human mirror neuron system: an otherwise neutral stimulus can acquire specific mirror neuron-driving properties in the absence of a direct association between motor practice and perception. This significant malleability in the way that the mirror neuron system can code otherwise meaningless (i.e. arbitrarily associated) stimuli may contribute to coding communicative signals such as language. This may represent a mirror neuron system feature that is unique to humans.
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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".