Evidence map›Paper›PMID 31244628›Full record

ArticleFrontiers in human neuroscience2019

Touching! An Augmented Reality System for Unveiling Face Topography in Very Young Children.

Michiko Miyazaki, Tomohisa Asai, Ryoko Mugitani

Erratum issuedOpen access · goldAbstract read
In one paragraph

Article in Frontiers in human neuroscience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.7field-weighted citation impact, top 29% 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

0 citing papers in PubMed, 4 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors at 3 institutions in 1 country.

Michiko MiyazakiDepartment of Social Information Studies, Otsuma Women's University, Tokyo, Japan.
Tomohisa AsaiAdvanced Telecommunications Research Institute International (ATR), Kyoto, Japan.
Ryoko MugitaniNTT Communication Science Laboratories, Atsugi, Japan.
Advanced Telecommunications Research Institute International · JPJapan Women's University · JPNTT Basic Research Laboratories · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Developmental body topography, particularly of the face, is a fundamental research topic in the current decade. However, empirical investigation of this topic for very young children faces a number of difficulties related to the task requirements and technical procedures. In this study, we developed a new task to study the spatially-sensed position of facial parts in a self-face recognition task for 2.5- and 3.5-year-old children. Using the technique of augmented reality (AR) and 3D face tracking technology, we presented participants with their projected self-image on a screen, accompanied by a digital mark located on parts of their face. We prepared a cheerful visual and auditory reward on the screen when participants showed correct localization of the mark. We then tested whether they could indicate the position of the mark on their own faces and remain motivated for task repetition. To assess the efficacy of this task, 31 2.5- and 11 3.5-year-old children participated in this study. About half of the 2.5-year-olds and 80% of the 3.5-year-olds could perform more than 30 trials. Our new task, then, was to maintain young children's motivation for task repetition using the cheerful visual and auditory reward. The analysis of localization errors suggested the uniqueness of spatial knowledge of self-face in young children. The efficacy of this new task for studying the development of body image has been confirmed.

Indexed as

augmented realitybody imagebody topographydevelopmentfacepsychology

Identifiers

PMID31244628
PMCPMC6579857
OpenAlexW2951747162

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

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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.