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Record W2110700282 · doi:10.1016/j.cub.2008.04.061

Flexible Representations of Dynamics Are Used in Object Manipulation

2008· article· en· W2110700282 on OpenAlexafffund
Alaa A. Ahmed, Daniel M. Wolpert, J. Randall Flanagan

Bibliographic record

VenueCurrent Biology · 2008
Typearticle
Languageen
FieldNeuroscience
TopicMotor Control and Adaptation
Canadian institutionsQueen's University
FundersCanadian Institutes of Health ResearchWellcome Trust
KeywordsBiologyDynamics (music)Object (grammar)Evolutionary biologyComputational biologyArtificial intelligenceComputer science

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.241
Threshold uncertainty score0.205

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.136
GPT teacher head0.349
Teacher spread0.213 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations62
Published2008
Admission routes2
Has abstractyes

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