Evidence map›Paper›PMID 30266152›Full record

ReviewTrends in cognitive sciences2018

A Dynamical Systems Perspective on Flexible Motor Timing.

Evan D Remington, Seth W Egger, Devika Narain, Jing Wang, Mehrdad Jazayeri

Abstract readReview
In one paragraph

Review in Trends in cognitive sciences, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 66 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
66citing papers in PubMed, 1 pooled it
–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

66 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Dynamical Cognitive Science! Wherefore Art Thou?Topics in cognitive science · 2026
    Article
  5. Article
  6. Article
  7. Subspace communication in the hippocampal-retrosplenial axis.bioRxiv : the preprint server for biology · 2026
    Article
  8. Article
  9. Article
  10. Motor cortex flexibly deploys a high-dimensional repertoire of subskills.bioRxiv : the preprint server for biology · 2025
    Article
  11. Article
  12. BRAID: Input-driven nonlinear dynamical modeling of neural-behavioral data.... International Conference on Learning Representations · 2025
    Article
  13. Article
  14. Article
  15. Article
  16. Ramping cells in the rodent medial prefrontal cortex encode time to past and future events via real Laplace transform.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
  17. Article
  18. Article
  19. Nonlinear manifolds underlie neural population activity during behaviour.bioRxiv : the preprint server for biology · 2024
    Article
  20. Article

6 more citing papers are in PubMed but not listed here.

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.

Evan D RemingtonMcGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; These authors contributed equally to this work.
Seth W EggerMcGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; These authors contributed equally to this work.
Devika NarainMcGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Netherlands Institute for Neuroscience, Amsterdam, BA 1105, The Netherlands; Erasmus Medical Center, Rotterdam, The Netherlands.
Jing WangMcGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Bioengineering, University of Missouri, Columbia, MO 65201, USA.
Mehrdad JazayeriMcGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. Electronic address: mjaz@mit.edu.

Funding

Neural mechanisms of timing in the oculomotor systemR01NS078127 · NINDS · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI JAZAYERI, MEHRDAD · 2014 to 2018
$1.7M
EPA EP-C-18-008NINDS NIH HHS R01 NS078127
6 · The paper itself

Abstract

A hallmark of higher brain function is the ability to rapidly and flexibly adjust behavioral responses based on internal and external cues. Here, we examine the computational principles that allow decisions and actions to unfold flexibly in time. We adopt a dynamical systems perspective and outline how temporal flexibility in such a system can be achieved through manipulations of inputs and initial conditions. We then review evidence from experiments in nonhuman primates that support this interpretation. Finally, we explore the broader utility and limitations of the dynamical systems perspective as a general framework for addressing open questions related to the temporal control of movements, as well as in the domains of learning and sequence generation.

Indexed as

Models, NeurologicalNeural Networks, ComputerSystems AnalysisAnimalsExecutive FunctionHumansMotor ActivityNerve NetTime Perceptiondynamical systemsflexible timinglearningmovement planningmovement sequencessensorimotor control

Identifiers

PMID30266152
PMCPMC6166486

What OpenQuestion holds

Textmetadata
LicenceTDM
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.