Evidence map›Paper›PMID 38698023›Full record

ArticleNPJ science of learning2024

Body-part specificity for learning of multiple prior distributions in human coincidence timing.

Yoshiki Matsumura, Neil W Roach, James Heron, Makoto Miyazaki

Abstract read
In one paragraph

Article in NPJ science of learning, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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.

Yoshiki MatsumuraGraduate School of Integrated Science and Technology, Shizuoka University, Hamamatsu, Japan.
Neil W RoachSchool of Psychology, University of Nottingham, Nottingham, UK.ORCID http://orcid.org/0000-0002-4155-745X
James HeronSchool of Optometry and Vision Science, University of Bradford, Bradford, UK.
Makoto MiyazakiGraduate School of Integrated Science and Technology, Shizuoka University, Hamamatsu, Japan. miyazaki-makoto@inf.shizuoka.ac.jp.ORCID http://orcid.org/0000-0002-8346-1069

Funding

MEXT | Japan Society for the Promotion of Science (JSPS) KAKEHI(17KK0004)MEXT | Japan Society for the Promotion of Science (JSPS) KAKEHI(19H01087)MEXT | Japan Society for the Promotion of Science (JSPS) KAKEHI(22H00502)MEXT | Japan Society for the Promotion of Science (JSPS) KAKEHI(22K18263)
6 · The paper itself

Abstract

During timing tasks, the brain learns the statistical distribution of target intervals and integrates this prior knowledge with sensory inputs to optimise task performance. Daily events can have different temporal statistics (e.g., fastball/slowball in baseball batting), making it important to learn and retain multiple priors. However, the rules governing this process are not yet understood. Here, we demonstrate that the learning of multiple prior distributions in a coincidence timing task is characterised by body-part specificity. In our experiments, two prior distributions (short and long intervals) were imposed on participants. When using only one body part for timing responses, regardless of the priors, participants learned a single prior by generalising over the two distributions. However, when the two priors were assigned to different body parts, participants concurrently learned the two independent priors. Moreover, body-part specific prior acquisition was faster when the priors were assigned to anatomically distant body parts (e.g., hand/foot) than when they were assigned to close body parts (e.g., index/middle fingers). This suggests that the body-part specific learning of priors is organised according to somatotopy.

Identifiers

PMID38698023
PMCPMC11066023

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.