Evidence map›Paper›PMID 37731753›Full record

ArticleEurope's journal of psychology2023

The Estimation of Physical Distances Between Oneself and a Social Robot: Am I as Far From the Robot as It is from Me?

Jean-Baptiste Lanfranchi, Sophie Lemonnier

Abstract read
In one paragraph

Article in Europe's journal of psychology, 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

2 authors.

Jean-Baptiste LanfranchiUniversité de Paris Cité and Univ. Gustave Eiffel, LaPEA, Boulogne-Billancourt, France.ORCID https://orcid.org/0000-0001-9484-6311
Sophie LemonnierUniversité de Lorraine, PErSEUs, Metz, France.ORCID https://orcid.org/0000-0002-9650-7054

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Research on the perception of interpersonal distance has shown the existence of an asymmetry effect which depends on the reference point of the estimation: the distance from oneself to others can be perceived as longer or shorter than the distance from others to oneself. The mechanism underlying this asymmetric effect is related to the object's cognitive salience. The self often functions as a habitual reference point and therefore one's own salience may be higher than that of other objects. In this case, an egocentric asymmetry effect appears with a perceived shorter distance from others to oneself. However, if others are more salient than oneself, then the reverse can happen (allocentric asymmetry effect). The present work investigates if asymmetry in self-other(s) distance perception changes when the other is a social robot. An experiment was conducted with 174 participants who were asked to estimate the distance between themselves and both robotic and human assistants on a schematic map of a hospital emergency room (between-subjects design). With robust ANOVA, the results showed that the participants felt closer to the human assistant than to the robot, notably when the person served as the estimation reference point. Perceived distances to the social robot were not significantly distorted. If a rather allocentric effect with the human assistant might reflect an affiliation goal on the part of the participants, the absence of effect with the social robot forces us to reconsider its humanization. This could nevertheless reflect a purely mechanical and utilitarian conception of it.

Indexed as

distance perceptionegocentric/allocentric asymmetry effectshuman-robot interactionself-conceptsocial robot

Identifiers

PMID37731753
PMCPMC10508198

What OpenQuestion holds

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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.