↓ Skip to main content

Monkeys and Humans Share a Common Computation for Face/Voice Integration

Overview of attention for article published in PLoS Computational Biology, September 2011
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (88th percentile)
  • Good Attention Score compared to outputs of the same age and source (76th percentile)

Mentioned by

blogs
1 blog
twitter
6 X users

Readers on

mendeley
116 Mendeley
citeulike
1 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Monkeys and Humans Share a Common Computation for Face/Voice Integration
Published in
PLoS Computational Biology, September 2011
DOI 10.1371/journal.pcbi.1002165
Pubmed ID
Authors

Chandramouli Chandrasekaran, Luis Lemus, Andrea Trubanova, Matthias Gondan, Asif A. Ghazanfar

Abstract

Speech production involves the movement of the mouth and other regions of the face resulting in visual motion cues. These visual cues enhance intelligibility and detection of auditory speech. As such, face-to-face speech is fundamentally a multisensory phenomenon. If speech is fundamentally multisensory, it should be reflected in the evolution of vocal communication: similar behavioral effects should be observed in other primates. Old World monkeys share with humans vocal production biomechanics and communicate face-to-face with vocalizations. It is unknown, however, if they, too, combine faces and voices to enhance their perception of vocalizations. We show that they do: monkeys combine faces and voices in noisy environments to enhance their detection of vocalizations. Their behavior parallels that of humans performing an identical task. We explored what common computational mechanism(s) could explain the pattern of results we observed across species. Standard explanations or models such as the principle of inverse effectiveness and a "race" model failed to account for their behavior patterns. Conversely, a "superposition model", positing the linear summation of activity patterns in response to visual and auditory components of vocalizations, served as a straightforward but powerful explanatory mechanism for the observed behaviors in both species. As such, it represents a putative homologous mechanism for integrating faces and voices across primates.

X Demographics

X Demographics

The data shown below were collected from the profiles of 6 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 116 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 6 5%
Germany 3 3%
Chile 1 <1%
United Kingdom 1 <1%
France 1 <1%
Spain 1 <1%
Belgium 1 <1%
Unknown 102 88%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 26 22%
Researcher 25 22%
Student > Master 16 14%
Student > Bachelor 8 7%
Professor > Associate Professor 6 5%
Other 18 16%
Unknown 17 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 24 21%
Psychology 23 20%
Neuroscience 19 16%
Medicine and Dentistry 7 6%
Linguistics 3 3%
Other 17 15%
Unknown 23 20%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 11. 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 19 October 2011.
All research outputs
#3,188,379
of 25,373,627 outputs
Outputs from PLoS Computational Biology
#2,813
of 8,960 outputs
Outputs of similar age
#16,611
of 143,313 outputs
Outputs of similar age from PLoS Computational Biology
#28
of 121 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,960 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one has gotten more attention than average, scoring higher than 68% 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 143,313 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 88% of its contemporaries.
We're also able to compare this research output to 121 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 76% of its contemporaries.