Evidence map›Paper›PMID 32062162›Full record

ArticleJournal of experimental child psychology2020

Specificity of representations in infants' visual statistical learning.

Dylan M Antovich, Stephanie Chen-Wu Gluck, Elizabeth J Goldman, Katharine Graf Estes

Abstract read
In one paragraph

Article in Journal of experimental child psychology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. 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.

Dylan M AntovichUniversity of California, Davis, Davis, CA 95616, USA. Electronic address: dmantovich@ucdavis.edu.
Stephanie Chen-Wu GluckUniversity of Oregon, Eugene, OR 97403, USA.
Elizabeth J GoldmanUniversity of California, Santa Cruz, Santa Cruz, CA 95064, USA.
Katharine Graf EstesUniversity of California, Davis, Davis, CA 95616, USA.

Funding

Linking Statistical Learning to Vocabulary DevelopmentR03HD062755 · NICHD · UNIVERSITY OF CALIFORNIA AT DAVIS · PI GRAF ESTES, KATHARINE · 2010 to 2011
$150k
NICHD NIH HHS R03 HD062755
6 · The paper itself

Abstract

Past work has demonstrated infants' robust statistical learning across visual and auditory modalities. However, the specificity of representations produced via visual statistical learning has not been fully explored. The current study addressed this by investigating infants' abilities to identify previously learned object sequences when some object features (e.g., shape, face) aligned with prior learning and other features did not. Experiment 1 replicated past work demonstrating that infants can learn statistical regularities across sequentially presented objects and extended this finding to 16-month-olds. In Experiment 2, infants viewed test sequences in which one object feature (e.g., face) had been removed but the other feature (e.g., shape) was maintained, resulting in failure to identify familiar sequences. We further probed learning specificity by assessing infants' recognition of sequences when one feature was altered rather than removed (Experiment 3) and when one feature was uncorrelated with the original sequence structure (Experiment 4). In both cases, infants failed to identify sequences in which object features were not identical between learning and test. These findings suggest that infants are limited in their ability to generalize the statistical structure of an object sequence when the objects' features do not align between learning and test.

Indexed as

Probability LearningChild DevelopmentFemaleHumansInfantMalePattern Recognition, VisualGeneralizationHabituation paradigmInfant cognitionObject processingSpecificity of learningVisual statistical learning

Identifiers

PMID32062162
PMCPMC7087448

What OpenQuestion holds

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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.