Evidence map›Paper›PMID 34918270›Full record

ArticlePsychonomic bulletin & review2022

A number-line task with a Bayesian active learning algorithm provides insights into the development of non-symbolic number estimation.

Sang Ho Lee, Dan Kim, John E Opfer, Mark A Pitt, Jay I Myung

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Article in Psychonomic bulletin & review, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

5 authors.

Sang Ho LeeDepartment of Psychology, The Ohio State University, 212 Psychology Building, 1835 Neil Avenue, Columbus, OH, 43210, USA. lee.7285@osu.edu.ORCID http://orcid.org/0000-0002-8316-2886
Dan KimDepartment of Psychology, The Ohio State University, 212 Psychology Building, 1835 Neil Avenue, Columbus, OH, 43210, USA.
John E OpferDepartment of Psychology, The Ohio State University, 212 Psychology Building, 1835 Neil Avenue, Columbus, OH, 43210, USA.
Mark A PittDepartment of Psychology, The Ohio State University, 212 Psychology Building, 1835 Neil Avenue, Columbus, OH, 43210, USA.
Jay I MyungDepartment of Psychology, The Ohio State University, 212 Psychology Building, 1835 Neil Avenue, Columbus, OH, 43210, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To characterize numerical representations, the number-line task asks participants to estimate the location of a given number on a line flanked with zero and an upper-bound number. An open question is whether estimates for symbolic numbers (e.g., Arabic numerals) and non-symbolic numbers (e.g., number of dots) rely on common processes with a common developmental pathway. To address this question, we explored whether well-established findings in symbolic number-line estimation generalize to non-symbolic number-line estimation. For exhaustive investigations without sacrificing data quality, we applied a novel Bayesian active learning algorithm, dubbed Gaussian process active learning (GPAL), that adaptively optimizes experimental designs. The results showed that the non-symbolic number estimation in participants of diverse ages (5-73 years old, n = 238) exhibited three characteristic features of symbolic number estimation.

Indexed as

AlgorithmsAdolescentAdultAgedBayes TheoremChildChild, PreschoolHumansMathematicsMiddle AgedNormal DistributionYoung AdultActive learningCognitive developmentCognitive modelingGaussian processHierarchical Bayesian modelingNumerical cognition

Identifiers

PMID34918270

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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.