fMRI Repetition Suppression for Familiar But Not Arbitrary Actions with Tools
Bibliographic record
Abstract
For humans, daily life is characterized by routine interaction with many different tools for which corresponding actions are specified and performed according to well-learned procedures. The current study used functional MRI (fMRI) repetition suppression (RS) to identify brain areas underlying the transformation of visually defined tool properties to corresponding motor programs for conventional use. Before grasping and demonstrating how to use a specific tool, participants passively viewed either the same (repeated) tool or a different (non-repeated) tool. Repetition of tools led to reduced fMRI signals (RS) within a selective network of parietal and premotor areas. Comparison with newly learned, arbitrarily defined control actions revealed specificity of RS for tool use, thought to reflect differences in the extent of previous sensorimotor experience. The findings indicate that familiar tools are visually represented within the same sensorimotor areas underlying their dexterous use according to learned properties defined by previous experience. This interpretation resonates with the broader concept of affordance specification considered fundamental to action planning and execution whereby action-relevant object properties (affordances) are visually represented in sensorimotor areas. The current findings extend this view to reveal that affordance specification in humans includes learned object properties defined by previous sensorimotor experience. From an evolutionary perspective, the neural mechanisms identified in the current study offer clear survival advantage, providing fast efficient transformation of visual information to appropriate motor responses based on previous experience.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".