High-fidelity musculoskeletal modeling reveals a motor planning contribution to the speed-accuracy tradeoff

  1. Mazen Al Borno  Is a corresponding author
  2. Saurabh Vyas
  3. Krishna V Shenoy
  4. Scott L Delp
  1. Stanford University, United States

Abstract

A long-standing challenge in motor neuroscience is to understand the relationship between movement speed and accuracy, known as the speed-accuracy tradeoff. Here, we introduce a biomechanically realistic computational model of three-dimensional upper extremity movements that reproduces well-known features of reaching movements. This model revealed that the speed-accuracy tradeoff, as described by Fitts' law, emerges even without the presence of motor noise, which is commonly believed to underlie the speed-accuracy tradeoff. Next, we analyzed motor cortical neural activity from monkeys reaching to targets of different sizes. We found that the contribution of preparatory neural activity to movement duration variability is greater for smaller targets than larger targets, and that movements to smaller targets exhibit less variability in population-level preparatory activity, but greater movement duration variability. These results propose a new theory underlying the speed-accuracy tradeoff: Fitts' law emerges from greater task demands constraining the optimization landscape in a fashion that reduces the number of 'good' control solutions (i.e., faster reaches). Thus, contrary to current beliefs, the speed-accuracy tradeoff could be a consequence of motor planning variability and not exclusively signal-dependent noise.

Data availability

The source code for the computer simulations and our data are available at https://simtk.org/projects/ue-reaching. Users must first create a free account (https://simtk.org/account/register.php) before they can download the datasets from the site.

Article and author information

Author details

  1. Mazen Al Borno

    Bioengineering, Stanford University, Palo Alto, United States
    For correspondence
    malborno@stanford.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2208-9934
  2. Saurabh Vyas

    Bioengineering, Stanford University, Stanford, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Krishna V Shenoy

    Department of Electrical Engineering, Stanford University, Stanford, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Scott L Delp

    Bioengineering, Stanford University, Stanford, United States
    Competing interests
    The authors declare that no competing interests exist.

Funding

National Institutes of Health (U54EB020405)

  • Scott L Delp

National Institute of Neurological Disorders and Stroke (R01NS076460)

  • Krishna V Shenoy

National Institute of Mental Health (R01MH09964703)

  • Krishna V Shenoy

Defense Advanced Research Projects Agency (N66001-10-C-2010)

  • Krishna V Shenoy

National Institutes of Health (8DP1HD075623)

  • Krishna V Shenoy

Simons Foundation (325380 and 543045)

  • Krishna V Shenoy

National Institutes of Health (5F31NS103409-02)

  • Saurabh Vyas

National Science Foundation (Graduate Fellowship)

  • Saurabh Vyas

Stanford University (Ric Weiland Stanford Graduate Fellowship)

  • Saurabh Vyas

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. J Andrew Pruszynski, Western University, Canada

Ethics

Animal experimentation: All surgical and animal care procedures were performed in accordance with NationalInstitutes of Health guidelines and were approved by the Stanford University InstitutionalAnimal Care and Use Committee (8856).

Human subjects: Subjects gave written informed consent, and consent to publish, approved by the Stanford University Institutional Review Board (42787). The guidelines followed are specified in the Human Research Protection Program (HRPP Stanford University).

Version history

  1. Received: March 18, 2020
  2. Accepted: December 15, 2020
  3. Accepted Manuscript published: December 16, 2020 (version 1)
  4. Version of Record published: January 6, 2021 (version 2)

Copyright

© 2020, Al Borno et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 1,812
    views
  • 232
    downloads
  • 9
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Mazen Al Borno
  2. Saurabh Vyas
  3. Krishna V Shenoy
  4. Scott L Delp
(2020)
High-fidelity musculoskeletal modeling reveals a motor planning contribution to the speed-accuracy tradeoff
eLife 9:e57021.
https://doi.org/10.7554/eLife.57021

Share this article

https://doi.org/10.7554/eLife.57021

Further reading

    1. Neuroscience
    Zilu Liang, Simeng Wu ... Chao Liu
    Research Article

    People form impressions about others during daily social encounters and infer personality traits from others' behaviors. Such trait inference is thought to rely on two universal dimensions: competence and warmth. These two dimensions can be used to construct a ‘social cognitive map’ organizing massive information obtained from social encounters efficiently. Originating from spatial cognition, the neural codes supporting the representation and navigation of spatial cognitive maps have been widely studied. Recent studies suggest similar neural mechanism subserves the map-like architecture in social cognition as well. Here we investigated how spatial codes operate beyond the physical environment and support the representation and navigation of social cognitive map. We designed a social value space defined by two dimensions of competence and warmth. Behaviorally, participants were able to navigate to a learned location from random starting locations in this abstract social space. At the neural level, we identified the representation of distance in the precuneus, fusiform gyrus, and middle occipital gyrus. We also found partial evidence of grid-like representation patterns in the medial prefrontal cortex and entorhinal cortex. Moreover, the intensity of grid-like response scaled with the performance of navigating in social space and social avoidance trait scores. Our findings suggest a neurocognitive mechanism by which social information can be organized into a structured representation, namely cognitive map and its relevance to social well-being.

    1. Neuroscience
    Alina Tetereva, Narun Pat
    Research Article

    One well-known biomarker candidate that supposedly helps capture fluid cognition is Brain Age, or a predicted value based on machine-learning models built to predict chronological age from brain MRI. To formally evaluate the utility of Brain Age for capturing fluid cognition, we built 26 age-prediction models for Brain Age based on different combinations of MRI modalities, using the Human Connectome Project in Aging (n=504, 36–100 years old). First, based on commonality analyses, we found a large overlap between Brain Age and chronological age: Brain Age could uniquely add only around 1.6% in explaining variation in fluid cognition over and above chronological age. Second, the age-prediction models that performed better at predicting chronological age did NOT necessarily create better Brain Age for capturing fluid cognition over and above chronological age. Instead, better-performing age-prediction models created Brain Age that overlapped larger with chronological age, up to around 29% out of 32%, in explaining fluid cognition. Third, Brain Age missed around 11% of the total variation in fluid cognition that could have been explained by the brain variation. That is, directly predicting fluid cognition from brain MRI data (instead of relying on Brain Age and chronological age) could lead to around a 1/3-time improvement of the total variation explained. Accordingly, we demonstrated the limited utility of Brain Age as a biomarker for fluid cognition and made some suggestions to ensure the utility of Brain Age in explaining fluid cognition and other phenotypes of interest.