Evidence map›Paper›PMID 33170341›Full record

ReviewExperimental brain research2021

A tale of too many tasks: task fragmentation in motor learning and a call for model task paradigms.

Rajiv Ranganathan, Aimee D Tomlinson, Rakshith Lokesh, Tzu-Hsiang Lin, Priya Patel

Abstract readReview
PubMed Publisher
In one paragraph

Review in Experimental brain research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 2 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

19 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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  16. Is premature theorizing hurting skill acquisition research?Frontiers in sports and active living · 2023
    Article
  17. Article
  18. Ten guidelines for designing motor learning studies.Brazilian journal of motor behavior · 2022
    Article
  19. Always Pay Attention to Which Model of Motor Learning You Are Using.International journal of environmental research and public health · 2022
    Review
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.

Rajiv RanganathanDepartment of Kinesiology, Michigan State University, 308 W Circle Dr, East Lansing, MI, 48824, USA. rrangana@msu.edu.ORCID http://orcid.org/0000-0002-6924-253X
Aimee D TomlinsonDepartment of Kinesiology, Michigan State University, 308 W Circle Dr, East Lansing, MI, 48824, USA.
Rakshith LokeshDepartment of Kinesiology, Michigan State University, 308 W Circle Dr, East Lansing, MI, 48824, USA.
Tzu-Hsiang LinDepartment of Kinesiology, Michigan State University, 308 W Circle Dr, East Lansing, MI, 48824, USA.
Priya PatelDepartment of Kinesiology, Michigan State University, 308 W Circle Dr, East Lansing, MI, 48824, USA.

Funding

National Science Foundation 1823889
6 · The paper itself

Abstract

Motor learning encompasses a broad set of phenomena that requires a diverse set of experimental paradigms. However, excessive variation in tasks across studies creates fragmentation that can adversely affect the collective advancement of knowledge. Here, we show that motor learning studies tend toward extreme fragmentation in the choice of tasks, with almost no overlap between task paradigms across studies. We argue that this extreme level of task fragmentation poses serious theoretical and methodological barriers to advancing the field. To address these barriers, we propose the need for developing common 'model' task paradigms which could be widely used across labs. Combined with the open sharing of methods and data, we suggest that these model task paradigms could be an important step in increasing the robustness of the motor learning literature and facilitate the cumulative process of science.

Indexed as

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Identifiers

What OpenQuestion holds

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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.