Evidence map›Paper›PMID 37935780›Full record

ArticleScientific reports2023

Robot occupations affect the categorization border between human and robot faces.

Junyi Shen, Guyue Tang, Shinichi Koyama

Abstract read
In one paragraph

Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

3 authors.

Junyi ShenUniversity of Tsukuba, Graduate School of Comprehensive Human Sciences, Tsukuba, 305-8574, Japan.
Guyue TangUniversity of Tsukuba, Graduate School of Comprehensive Human Sciences, Tsukuba, 305-8574, Japan.
Shinichi KoyamaUniversity of Tsukuba, Institute of Art and Design, Tsukuba, 305-8574, Japan. skoyama@geijutsu.tsukuba.ac.jp.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The Uncanny Valley hypothesis implies that people perceive a subjective border between human and robot faces. The robot-human border refers to the level of human-like features that distinguishes humans from robots. However, whether people's perceived anthropomorphism and robot-human borders are consistent across different robot occupations remains to be explored. This study examined the robot-human border by analyzing the human photo proportion represented by the point of subjective equality in three image classification tasks. Stimulus images were generated by morphing a robot face photo and one each of four human photos in systematically changed proportions. Participants classified these morphed images in three different robot occupational conditions to explore the effect of changing robot jobs on the robot-human border. The results indicated that robot occupation and participant age and gender influenced people's perceived anthropomorphism of robots. These can be explained by the implicit link between robot job and appearance, especially in a stereotyped context. The study suggests that giving an expected appearance to a robot may reproduce and strengthen a stereotype that associates a certain appearance with a certain job.

Indexed as

Occupational DiseasesRoboticsHumansOccupations

Identifiers

PMID37935780
PMCPMC10630393

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.