Evidence map›Paper›PMID 32703813›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2020

General learning ability in perceptual learning.

Jia Yang, Fang-Fang Yan, Lijun Chen, Jie Xi, Shuhan Fan, Pan Zhang, Zhong-Lin Lu, Chang-Bing Huang

Open access · bronzeAbstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed
3.7field-weighted citation impact, top 6% of its field
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

26 citing papers in PubMed, 47 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. The neural correlates of domain-general visual ability.Cerebral cortex (New York, N.Y. : 1991) · 2023
    Article
  16. Using global feedback to induce learning of gist of abnormality in mammograms.Cognitive research: principles and implications · 2023
    Article
  17. Article
  18. Current directions in visual perceptual learning.Nature reviews psychology · 2022
    Article
  19. Article
  20. 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

8 authors at 3 institutions in 2 countries.

Jia YangKey Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, 100101 Beijing, China.
Fang-Fang YanKey Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, 100101 Beijing, China.
Lijun ChenKey Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, 100101 Beijing, China.
Jie XiKey Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, 100101 Beijing, China.
Shuhan FanKey Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, 100101 Beijing, China.
Pan ZhangCenter for Neural Science and Department of Psychology, New York University, New York, NY 10003.
Zhong-Lin LuCenter for Neural Science and Department of Psychology, New York University, New York, NY 10003; zhonglin@nyu.edu huangcb@psych.ac.cn.ORCID 0000-0002-7295-727X
Chang-Bing HuangKey Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, 100101 Beijing, China; zhonglin@nyu.edu huangcb@psych.ac.cn.ORCID 0000-0003-4694-0834
Institute of Psychology, Chinese Academy of Sciences · CNNew York University · USNew York University Shanghai · CN

Funding

The Functions and Mechanisms of Perceptual LearningR01EY017491 · NEI · UNIVERSITY OF CALIFORNIA-IRVINE · PI DOSHER, BARBARA A., LU, ZHONG-LIN · 2006 to 2023
$6.1M
NEI NIH HHS R01 EY017491
6 · The paper itself

Abstract

Developing expertise in any field usually requires acquisition of a wide range of skills. Most current studies on perceptual learning have focused on a single task and concluded that learning is quite specific to the trained task, and the ubiquitous individual differences reflect random fluctuations across subjects. Whether there exists a general learning ability that determines individual learning performance across multiple tasks remains largely unknown. In a large-scale perceptual learning study with a wide range of training tasks, we found that initial performance, task, and individual differences all contributed significantly to the learning rates across the tasks. Most importantly, we were able to extract both a task-specific but subject-invariant component of learning, that accounted for 38.6% of the variance, and a subject-specific but task-invariant perceptual learning ability, that accounted for 36.8% of the variance. The existence of a general perceptual learning ability across multiple tasks suggests that individual differences in perceptual learning are not "noise"; rather, they reflect the variability of learning ability across individuals. These results could have important implications for selecting potential trainees in occupations that require perceptual expertise and designing better training protocols to improve the efficiency of clinical rehabilitation.

Indexed as

LearningPerceptionAdultFemaleHumansIndividualityMaleTask Performance and AnalysisYoung Adultgeneral learning abilityindividual differencemultitask continual learningperceptual learning

Identifiers

PMID32703813
PMCPMC7430974
OpenAlexW3045407018

What OpenQuestion holds

Textmetadata
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.