Evidence map›Paper›PMID 36111622›Full record

ArticleThe European journal of neuroscience2022

Domain-specific and domain-general neural network engagement during human-robot interactions.

Ann Hogenhuis, Ruud Hortensius

Open access · bronzeAbstract read
In one paragraph

Article in The European journal of neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.8field-weighted citation impact, top 30% of its field
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

4 citing papers in PubMed, 7 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. 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 at 1 institution in 1 country.

Ann HogenhuisLiberal Arts and Sciences, Utrecht University, Utrecht, The Netherlands.
Ruud HortensiusDepartment of Psychology, Utrecht University, Utrecht, The Netherlands.ORCID 0000-0002-5279-6202
Utrecht University · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To what extent do domain-general and domain-specific neural network engagement generalize across interactions with human and artificial agents? In this exploratory study, we analysed a publicly available functional MRI (fMRI) data set (n = 22) to probe the similarities and dissimilarities in neural architecture while participants conversed with another person or a robot. Incorporating trial-by-trial dynamics of the interactions, listening and speaking, we used whole-brain, region-of-interest and functional connectivity analyses to test response profiles within and across social or non-social, domain-specific and domain-general networks, that is, the person perception, theory-of-mind, object-specific, language and multiple-demand networks. Listening to a robot compared to a human resulted in higher activation in the language network, especially in areas associated with listening comprehension, and in the person perception network. No differences in activity of the theory-of-mind network were found. Results from the functional connectivity analysis showed no difference between interactions with a human or robot in within- and between-network connectivity. Together, these results suggest that although largely similar regions are activated when speaking to a human and to a robot, activity profiles during listening point to a dissociation at a lower level or perceptual level, but not higher order cognitive level.

Indexed as

RoboticsBrainBrain MappingComprehensionHumansMagnetic Resonance ImagingNeural Networks, Computerhuman-robot interactionsocial cognitionsocial interactiontwo-person neuroscience

Identifiers

PMID36111622
PMCPMC9828180
OpenAlexW4296027838

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.