Evidence map›Paper›PMID 37622940›Full record

ArticleBiomimetics (Basel, Switzerland)2023

Facial Anthropomorphic Trustworthiness Scale for Social Robots: A Hybrid Approach.

Yao Song, Ameersing Luximon, Yan Luximon

Abstract read
In one paragraph

Article in Biomimetics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. 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.

Yao SongDigital Convergence Laboratory of Chinese Cultural Inheritance and Global Communication, Sichuan University, Chengdu 610065, China.ORCID 0000-0001-6075-6211
Ameersing LuximonGeorgia Tech Shenzhen Institute, Tianjin University, Shenzhen 518071, China.
Yan LuximonSchool of Design, The Hong Kong Polytechnic University, Hung Hom, Hong Kong 999077, China.ORCID 0000-0003-2843-847X

Funding

This research was funded by supported by Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. PolyU/RGC Project No 15606321), GBA Startup Postdoc Program (SP-21-04), and supported by College of Literature and Journali This research was funded by supported by Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. PolyU/RGC Project No 15606321), GBA Startup Postdoc Program (SP-21-04), and supported by College of Literature and Journali
6 · The paper itself

Abstract

Social robots serve as autonomous systems for performing social behaviors and assuming social roles. However, there is a lack of research focusing on the specific measurement of facial trustworthiness toward anthropomorphic robots, particularly during initial interactions. To address this research gap, a hybrid deep convolution approach was employed in this study, involving a crowdsourcing platform for data collection and deep convolution and factor analysis for data processing. The goal was to develop a scale, called Facial Anthropomorphic Trustworthiness towards Social Robots (FATSR-17), to measure the trustworthiness of a robot's facial appearance. The final measurement scale comprised four dimensions, "ethics concern", "capability", "positive affect", and "anthropomorphism", consisting of 17 items. An iterative examination and a refinement process were conducted to ensure the scale's reliability and validity. The study contributes to the field of robot design by providing designers with a structured toolkit to create robots that appear trustworthy to users.

Indexed as

artificial intelligencefacescalesocial robot

Identifiers

PMID37622940
PMCPMC10452404

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.