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Over my fake body: body ownership illusions for studying the multisensory basis of own-body perception

Overview of attention for article published in Frontiers in Human Neuroscience, March 2015
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (98th percentile)
  • High Attention Score compared to outputs of the same age and source (95th percentile)

Mentioned by

news
6 news outlets
blogs
1 blog
twitter
50 X users
facebook
1 Facebook page
googleplus
1 Google+ user
reddit
1 Redditor

Citations

dimensions_citation
390 Dimensions

Readers on

mendeley
638 Mendeley
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Title
Over my fake body: body ownership illusions for studying the multisensory basis of own-body perception
Published in
Frontiers in Human Neuroscience, March 2015
DOI 10.3389/fnhum.2015.00141
Pubmed ID
Authors

Konstantina Kilteni, Antonella Maselli, Konrad P. Kording, Mel Slater

Abstract

Which is my body and how do I distinguish it from the bodies of others, or from objects in the surrounding environment? The perception of our own body and more particularly our sense of body ownership is taken for granted. Nevertheless, experimental findings from body ownership illusions (BOIs), show that under specific multisensory conditions, we can experience artificial body parts or fake bodies as our own body parts or body, respectively. The aim of the present paper is to discuss how and why BOIs are induced. We review several experimental findings concerning the spatial, temporal, and semantic principles of crossmodal stimuli that have been applied to induce BOIs. On the basis of these principles, we discuss theoretical approaches concerning the underlying mechanism of BOIs. We propose a conceptualization based on Bayesian causal inference for addressing how our nervous system could infer whether an object belongs to our own body, using multisensory, sensorimotor, and semantic information, and we discuss how this can account for several experimental findings. Finally, we point to neural network models as an implementational framework within which the computational problem behind BOIs could be addressed in the future.

X Demographics

X Demographics

The data shown below were collected from the profiles of 50 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 638 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 3 <1%
Italy 2 <1%
Japan 2 <1%
Brazil 2 <1%
Sweden 1 <1%
United Kingdom 1 <1%
Netherlands 1 <1%
Spain 1 <1%
Canada 1 <1%
Other 2 <1%
Unknown 622 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 126 20%
Student > Master 91 14%
Student > Bachelor 77 12%
Researcher 73 11%
Student > Doctoral Student 41 6%
Other 94 15%
Unknown 136 21%
Readers by discipline Count As %
Psychology 178 28%
Neuroscience 79 12%
Computer Science 63 10%
Engineering 44 7%
Agricultural and Biological Sciences 21 3%
Other 91 14%
Unknown 162 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 90. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 12 May 2024.
All research outputs
#486,268
of 25,916,093 outputs
Outputs from Frontiers in Human Neuroscience
#213
of 7,773 outputs
Outputs of similar age
#5,546
of 279,463 outputs
Outputs of similar age from Frontiers in Human Neuroscience
#9
of 184 outputs
Altmetric has tracked 25,916,093 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,773 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.1. This one has done particularly well, scoring higher than 97% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 279,463 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 98% of its contemporaries.
We're also able to compare this research output to 184 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 95% of its contemporaries.