Evidence map›Paper›PMID 37794045›Full record

ArticleScientific reports2023

Perceptual discrimination in the face perception of robots is attenuated compared to humans.

Abdulaziz Abubshait, Patrick P Weis, Ali Momen, Eva Wiese

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 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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.

Abdulaziz AbubshaitItalian Institute of Technology, Genoa, Italy. Abdulaziz.abubshait@iit.it.
Patrick P WeisGeorge Mason University, Fairfax, VA, USA.
Ali MomenGeorge Mason University, Fairfax, VA, USA.
Eva WieseGeorge Mason University, Fairfax, VA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

When interacting with groups of robots, we tend to perceive them as a homogenous group where all group members have similar capabilities. This overgeneralization of capabilities is potentially due to a lack of perceptual experience with robots or a lack of motivation to see them as individuals (i.e., individuation). This can undermine trust and performance in human-robot teams. One way to overcome this issue is by designing robots that can be individuated such that each team member can be provided tasks based on its actual skills. In two experiments, we examine if humans can effectively individuate robots: Experiment 1 (n = 225) investigates how individuation performance of robot stimuli compares to that of human stimuli that either belong to a social ingroup or outgroup. Experiment 2 (n = 177) examines to what extent robots' physical human-likeness (high versus low) affects individuation performance. Results show that although humans are able to individuate robots, they seem to individuate them to a lesser extent than both ingroup and outgroup human stimuli (Experiment 1). Furthermore, robots that are physically more humanlike are initially individuated better compared to robots that are physically less humanlike; this effect, however, diminishes over the course of the experiment, suggesting that the individuation of robots can be learned quite quickly (Experiment 2). Whether differences in individuation performance with robot versus human stimuli is primarily due to a reduced perceptual experience with robot stimuli or due to motivational aspects (i.e., robots as potential social outgroup) should be examined in future studies.

Indexed as

Facial RecognitionRoboticsHumansLearningMotivationTrust

Identifiers

PMID37794045
PMCPMC10550918

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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.