Evidence map›Paper›PMID 36944234›Full record

ArticleNeural computation2023

Body Mechanics, Optimality, and Sensory Feedback in the Human Control of Complex Objects.

Reza Sharif Razavian, Mohsen Sadeghi, Salah Bazzi, Rashida Nayeem, Dagmar Sternad

Abstract read
In one paragraph

Article in Neural computation, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
–field-weighted citation impact
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

13 citing papers in PubMed.

  1. Article
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  4. Human-Aware Control for Physically Interacting Robots.Bioengineering (Basel, Switzerland) · 2025
    Article
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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

5 authors.

Reza Sharif RazavianDepartment of Mechanical Engineering, Northern Arizona University, Flagstaff, AZ 86011, U.S.A. razavian.reza@nau.edu.
Mohsen SadeghiDepartment of Biology and Department of Electrical and Computer Engineering, Northeastern University, Boston, MA 02115, U.S.A. m.sadeghi@northeastern.edu.
Salah BazziDepartment of Electrical and Computer Engineering and Institute for Experiential Robotics, Northeastern University, Boston, MA 02115, U.S.A. s.bazzi@northeastern.edu.
Rashida NayeemDepartment of Electrical and Computer Engineering, Northeastern University, Boston, MA 02115, U.S.A. nayeem.r@northeastern.edu.
Dagmar SternadDepartments of Biology, Electrical and Computer Engineering, and Physics, Institute for Experiential Robotics, Northeastern University, Boston, MA 02115, U.S.A. d.sternad@northeastern.edu.

Funding

Predictability in complex object controlR37HD087089 · NICHD · NORTHEASTERN UNIVERSITY · PI Dagmar Sternad · 2022 to 2026
$3.0M
CRCNS Research Proposal: Collaborative Research: Neural Basis of Motor ExpertiseR01NS120579 · NINDS · NORTHEASTERN UNIVERSITY · PI BATISTA, AARON PAUL, STERNAD, DAGMAR · 2020 to 2025
$2.0M
Predictability in Complex Object ControlR01HD087089 · NICHD · NORTHEASTERN UNIVERSITY · PI STERNAD, DAGMAR · 2015 to 2020
$1.8M
NICHD NIH HHS R01 HD087089NICHD NIH HHS R37 HD087089NINDS NIH HHS R01 NS120579
6 · The paper itself

Abstract

Humans are adept at a wide variety of motor skills, including the handling of complex objects and using tools. Advances to understand the control of voluntary goal-directed movements have focused on simple behaviors such as reaching, uncoupled to any additional object dynamics. Under these simplified conditions, basic elements of motor control, such as the roles of body mechanics, objective functions, and sensory feedback, have been characterized. However, these elements have mostly been examined in isolation, and the interactions between these elements have received less attention. This study examined a task with internal dynamics, inspired by the daily skill of transporting a cup of coffee, with additional expected or unexpected perturbations to probe the structure of the controller. Using optimal feedback control (OFC) as the basis, it proved necessary to endow the model of the body with mechanical impedance to generate the kinematic features observed in the human experimental data. The addition of mechanical impedance revealed that simulated movements were no longer sensitively dependent on the objective function, a highly debated cornerstone of optimal control. Further, feedforward replay of the control inputs was similarly successful in coping with perturbations as when feedback, or sensory information, was included. These findings suggest that when the control model incorporates a representation of the mechanical properties of the limb, that is, embodies its dynamics, the specific objective function and sensory feedback become less critical, and complex interactions with dynamic objects can be successfully managed.

Indexed as

Feedback, SensoryMovementBiomechanical PhenomenaFeedbackHumansMotor Skills

Identifiers

PMID36944234
PMCPMC10246336

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.