Evidence map›Paper›PMID 25911418›Full record

ArticleSocial cognitive and affective neuroscience2015

In our own image? Emotional and neural processing differences when observing human-human vs human-robot interactions.

Yin Wang, Susanne Quadflieg

Abstract read
In one paragraph

Article in Social cognitive and affective neuroscience, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing 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

16 citing papers in PubMed.

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  12. Brain stimulation to left prefrontal cortex modulates attentional orienting to gaze cues.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2019
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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.

Yin WangDivision of Psychology, New York University|Abu Dhabi, Abu Dhabi, UAE.
Susanne QuadfliegDivision of Psychology, New York University|Abu Dhabi, Abu Dhabi, UAE susanne.quadflieg@nyu.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Notwithstanding the significant role that human-robot interactions (HRI) will play in the near future, limited research has explored the neural correlates of feeling eerie in response to social robots. To address this empirical lacuna, the current investigation examined brain activity using functional magnetic resonance imaging while a group of participants (n = 26) viewed a series of human-human interactions (HHI) and HRI. Although brain sites constituting the mentalizing network were found to respond to both types of interactions, systematic neural variation across sites signaled diverging social-cognitive strategies during HHI and HRI processing. Specifically, HHI elicited increased activity in the left temporal-parietal junction indicative of situation-specific mental state attributions, whereas HRI recruited the precuneus and the ventromedial prefrontal cortex (VMPFC) suggestive of script-based social reasoning. Activity in the VMPFC also tracked feelings of eeriness towards HRI in a parametric manner, revealing a potential neural correlate for a phenomenon known as the uncanny valley. By demonstrating how understanding social interactions depends on the kind of agents involved, this study highlights pivotal sub-routes of impression formation and identifies prominent challenges in the use of humanoid robots.

Indexed as

Interpersonal RelationsRoboticsSocial PerceptionAdolescentAdultBrain MappingCerebral CortexEmotionsFemaleHumansMagnetic Resonance ImagingMaleTheory of MindYoung Adultimpression formationmind attributionsperson construalperson dyadssocial robotics

Identifiers

PMID25911418
PMCPMC4631149

What OpenQuestion holds

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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.