Flexible Representations of Dynamics Are Used in Object Manipulation
Bibliographic record
Abstract
To manipulate an object skillfully, the brain must learn its dynamics, specifying the mapping between applied force and motion. A fundamental issue in sensorimotor control is whether such dynamics are represented in an extrinsic frame of reference tied to the object or an intrinsic frame of reference linked to the arm. Although previous studies have suggested that objects are represented in arm-centered coordinates [1Shadmehr R. Mussa-Ivaldi F.A. Adaptive representation of dynamics during learning of a motor task.J. Neurosci. 1994; 14: 3208-3224Crossref PubMed Google Scholar, 2Shadmehr R. Moussavi Z.M. Spatial generalization from learning dynamics of reaching movements.J. Neurosci. 2000; 20: 7807-7815Crossref PubMed Google Scholar, 3Malfait N. Shiller D.M. Ostry D.J. Transfer of motor learning across arm configurations.J. Neurosci. 2002; 22: 9656-9660Crossref PubMed Google Scholar, 4Mah C.D. Mussa-Ivaldi F.A. Generalization of object manipulation skills learned without limb motion.J. Neurosci. 2003; 23: 4821-4825Crossref PubMed Google Scholar, 5Bays P.M. Wolpert D.M. Actions and consequences in bimanual interaction are represented in different coordinate systems.J. Neurosci. 2006; 26: 7121-7126Crossref PubMed Scopus (21) Google Scholar, 6Ghez C. Krakauer J.W. Sainburg R. Ghilardi M. Spatial representations and internal models of limb dynamics in motor learning.in: Gazzaniga M.S. The New Cognitive Neurosciences. MIT, Cambridge, MA2004: 501-514Google Scholar], all of these studies have used objects with unusual and complex dynamics. Thus, it is not known how objects with natural dynamics are represented. Here we show that objects with simple (or familiar) dynamics and those with complex (or unfamiliar) dynamics are represented in object- and arm-centered coordinates, respectively. We also show that objects with simple dynamics are represented with an intermediate coordinate frame when vision of the object is removed. These results indicate that object dynamics can be flexibly represented in different coordinate frames by the brain. We suggest that with experience, the representation of the dynamics of a manipulated object may shift from a coordinate frame tied to the arm toward one that is linked to the object. The additional complexity required to represent dynamics in object-centered coordinates would be economical for familiar objects because such a representation allows object use regardless of the orientation of the object in hand.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".