Evidence map›Paper›PMID 40009083›Full record

ArticleExperimental brain research2025

Transfer of learned object manipulations between two- and five-digit grasps.

Jordana Ulloa-Marquez, Jennifer Gutterman, Marco Santello, Andrew M Gordon

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Article in Experimental brain research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Jordana Ulloa-MarquezDepartment of Biobehavioral Sciences, Teachers College, Columbia University, New York, NY, 10027, USA.
Jennifer GuttermanDepartment of Biobehavioral Sciences, Teachers College, Columbia University, New York, NY, 10027, USA.
Marco SantelloNeural Control of Movement Laboratory, School of Biological and Health Systems Engineering, Arizona State University, Phoenix, AZ, USA.
Andrew M GordonDepartment of Biobehavioral Sciences, Teachers College, Columbia University, New York, NY, 10027, USA. ag275@tc.columbia.edu.

Funding

Division of Behavioral and Cognitive Sciences BCS-1827725 and BCS-1827752
6 · The paper itself

Abstract

Successful object manipulation involves integrating object properties into a motor plan and scaling fingertip forces through learning. This study investigated whether learned manipulations using a two-digit grip transfer to a five-digit grip and vice versa, focusing on the challenges posed by added degrees of freedom in force distribution. The goal of the task was to exert the necessary compensatory torque (Tcom) and vertical forces to minimize object roll on a visually symmetrical object that with an asymmetrical mass distribution. To examine this, subjects performed blocked consecutive learning trials before switching grip type. Our results support the learning transfer between two-digits and five-digit grasp configurations despite challenges in maintaining perfect stability during the grip switch. Subjects adapted their grip forces (GF), center of pressure (CoP), and Tcom to minimize object roll, with significant improvements observed from novel (1st) to transfer (11th) trials. These findings suggest high-level, effector-independent representations of object manipulation that enable generalization across grip types, though some limitations in force distribution and digit position arise during transfers.

Indexed as

FingersHand StrengthLearningPsychomotor PerformanceTransfer, PsychologyAdultBiomechanical PhenomenaFemaleHumansMaleYoung AdultAnticipatory controlCenter of massFingertip forceGraspHandInternal representationMotor planningPrecision gripTransfer

Identifiers

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.