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Can a Humanoid Face be Expressive? A Psychophysiological Investigation

Overview of attention for article published in Frontiers in Bioengineering and Biotechnology, May 2015
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  • Good Attention Score compared to outputs of the same age (68th percentile)
  • Good Attention Score compared to outputs of the same age and source (76th percentile)

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Title
Can a Humanoid Face be Expressive? A Psychophysiological Investigation
Published in
Frontiers in Bioengineering and Biotechnology, May 2015
DOI 10.3389/fbioe.2015.00064
Pubmed ID
Authors

Nicole Lazzeri, Daniele Mazzei, Alberto Greco, Annalisa Rotesi, Antonio Lanatà, Danilo Emilio De Rossi

Abstract

Non-verbal signals expressed through body language play a crucial role in multi-modal human communication during social relations. Indeed, in all cultures, facial expressions are the most universal and direct signs to express innate emotional cues. A human face conveys important information in social interactions and helps us to better understand our social partners and establish empathic links. Latest researches show that humanoid and social robots are becoming increasingly similar to humans, both esthetically and expressively. However, their visual expressiveness is a crucial issue that must be improved to make these robots more realistic and intuitively perceivable by humans as not different from them. This study concerns the capability of a humanoid robot to exhibit emotions through facial expressions. More specifically, emotional signs performed by a humanoid robot have been compared with corresponding human facial expressions in terms of recognition rate and response time. The set of stimuli included standardized human expressions taken from an Ekman-based database and the same facial expressions performed by the robot. Furthermore, participants' psychophysiological responses have been explored to investigate whether there could be differences induced by interpreting robot or human emotional stimuli. Preliminary results show a trend to better recognize expressions performed by the robot than 2D photos or 3D models. Moreover, no significant differences in the subjects' psychophysiological state have been found during the discrimination of facial expressions performed by the robot in comparison with the same task performed with 2D photos and 3D models.

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 41 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Hungary 1 2%
Switzerland 1 2%
Unknown 39 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 10 24%
Researcher 7 17%
Student > Master 4 10%
Student > Bachelor 2 5%
Student > Doctoral Student 2 5%
Other 2 5%
Unknown 14 34%
Readers by discipline Count As %
Psychology 8 20%
Engineering 7 17%
Computer Science 7 17%
Arts and Humanities 1 2%
Business, Management and Accounting 1 2%
Other 3 7%
Unknown 14 34%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 30 September 2015.
All research outputs
#7,896,290
of 25,373,627 outputs
Outputs from Frontiers in Bioengineering and Biotechnology
#1,320
of 8,501 outputs
Outputs of similar age
#87,486
of 280,382 outputs
Outputs of similar age from Frontiers in Bioengineering and Biotechnology
#12
of 51 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 8,501 research outputs from this source. They receive a mean Attention Score of 3.5. This one has done well, scoring higher than 84% 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 280,382 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 68% of its contemporaries.
We're also able to compare this research output to 51 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.