Bibliographic record
Abstract
When grasping-to-use a familiar tool we often make use of stored information about the functional identity of the tool in order to guide our actions. At present, exactly which brain areas play an important role in representing this information is poorly understood. For example, is information regarding the functional significance of familiar objects stored within those areas mediating object-directed actions or does this information stem from other areas of the brain? During functional magnetic resonance imaging (fMRI), participants viewed short movies depicting different types of grasping actions made towards familiar tools. In one condition, the tool was grasped appropriately such that its function could be performed without further postural adjustments (e.g. a fork was grasped by the handle with the tines facing away from the actor). In the other condition, the tool was grasped inappropriately such that its function could not be performed without further postural adjustments (e.g. a fork was grasped by the handle with the tines facing toward the actor). We hypothesized that the viewing of functionally appropriate grasping actions would resonate more strongly with those areas involved in the processing of object function. Our results showed significantly stronger activity for the viewing of functionally appropriate as compared with inappropriate grasping actions within several brain areas previously implicated in higher-level object perception. In contrast, areas implicated in the control of object-directed actions did not distinguish between the two conditions. These findings suggest that information about the functional significance of objects is stored within areas specialized for object perception and not within areas specialized for object-directed action. Thus, object utilization based on stored knowledge of object function must involve a complex interplay between systems specialized for object perception and those specialized for the control of actions.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.056 | 0.021 |
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".