Evidence map›Paper›PMID 30942536›Full record

ReviewTopics in cognitive science2019

Understanding the Neural Bases of Implicit and Statistical Learning.

Laura J Batterink, Ken A Paller, Paul J Reber

Abstract readReview
In one paragraph

Review in Topics in cognitive science, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers.

0numbers the graph read from it
0cells of the map it votes in
50citing 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

50 citing papers in PubMed.

  1. Article
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  9. Neural mechanisms of flexible perceptual inference.PLoS computational biology · 2025
    Article
  10. Article
  11. Visual Statistical Learning Alters Low-Dimensional Cortical Architecture.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025
    Article
  12. Article
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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

3 authors.

Laura J BatterinkDepartment of Psychology, Brain and Mind Institute, Western University.
Ken A PallerDepartment of Psychology, Northwestern University.
Paul J ReberDepartment of Psychology, Northwestern University.

Funding

TRAINING PROGRAM IN THE NEUROSCIENCE OF HUMAN COGNITIONT32NS047987 · NINDS · NORTHWESTERN UNIVERSITY AT CHICAGO · PI Rodrigo M Braga, Christina Maria Zelano · 2006 to 2026
$5.6M
NINDS NIH HHS T32 NS047987
6 · The paper itself

Abstract

Both implicit learning and statistical learning focus on the ability of learners to pick up on patterns in the environment. It has been suggested that these two lines of research may be combined into a single construct of "implicit statistical learning." However, by comparing the neural processes that give rise to implicit versus statistical learning, we may determine the extent to which these two learning paradigms do indeed describe the same core mechanisms. In this review, we describe current knowledge about neural mechanisms underlying both implicit learning and statistical learning, highlighting converging findings between these two literatures. A common thread across all paradigms is that learning is supported by interactions between the declarative and nondeclarative memory systems of the brain. We conclude by discussing several outstanding research questions and future directions for each of these two research fields. Moving forward, we suggest that the two literatures may interface by defining learning according to experimental paradigm, with "implicit learning" reserved as a specific term to denote learning without awareness, which may potentially occur across all paradigms. By continuing to align these two strands of research, we will be in a better position to characterize the neural bases of both implicit and statistical learning, ultimately improving our understanding of core mechanisms that underlie a wide variety of human cognitive abilities.

Indexed as

Probability LearningBrainHumansMemorySerial LearningEEGfMRIImplicit learningNeural basisNeuroimagingNeuropsychologyNeuroscienceStatistical learning

Identifiers

PMID30942536
PMCPMC7723461

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.