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Predictive Mechanisms Are Not Involved the Same Way during Human-Human vs. Human-Machine Interactions: A Review

Overview of attention for article published in Frontiers in Neurorobotics, October 2017
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Title
Predictive Mechanisms Are Not Involved the Same Way during Human-Human vs. Human-Machine Interactions: A Review
Published in
Frontiers in Neurorobotics, October 2017
DOI 10.3389/fnbot.2017.00052
Pubmed ID
Authors

Aïsha Sahaï, Elisabeth Pacherie, Ouriel Grynszpan, Bruno Berberian

Abstract

Nowadays, interactions with others do not only involve human peers but also automated systems. Many studies suggest that the motor predictive systems that are engaged during action execution are also involved during joint actions with peers and during other human generated action observation. Indeed, the comparator model hypothesis suggests that the comparison between a predicted state and an estimated real state enables motor control, and by a similar functioning, understanding and anticipating observed actions. Such a mechanism allows making predictions about an ongoing action, and is essential to action regulation, especially during joint actions with peers. Interestingly, the same comparison process has been shown to be involved in the construction of an individual's sense of agency, both for self-generated and observed other human generated actions. However, the implication of such predictive mechanisms during interactions with machines is not consensual, probably due to the high heterogeneousness of the automata used in the experimentations, from very simplistic devices to full humanoid robots. The discrepancies that are observed during human/machine interactions could arise from the absence of action/observation matching abilities when interacting with traditional low-level automata. Consistently, the difficulties to build a joint agency with this kind of machines could stem from the same problem. In this context, we aim to review the studies investigating predictive mechanisms during social interactions with humans and with automated artificial systems. We will start by presenting human data that show the involvement of predictions in action control and in the sense of agency during social interactions. Thereafter, we will confront this literature with data from the robotic field. Finally, we will address the upcoming issues in the field of robotics related to automated systems aimed at acting as collaborative agents.

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

Geographical breakdown

Country Count As %
Unknown 102 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 15 15%
Student > Ph. D. Student 14 14%
Researcher 10 10%
Student > Bachelor 9 9%
Student > Doctoral Student 6 6%
Other 18 18%
Unknown 30 29%
Readers by discipline Count As %
Psychology 33 32%
Engineering 10 10%
Neuroscience 8 8%
Computer Science 6 6%
Medicine and Dentistry 3 3%
Other 8 8%
Unknown 34 33%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 24 October 2017.
All research outputs
#14,083,124
of 23,005,189 outputs
Outputs from Frontiers in Neurorobotics
#305
of 878 outputs
Outputs of similar age
#174,074
of 325,897 outputs
Outputs of similar age from Frontiers in Neurorobotics
#6
of 21 outputs
Altmetric has tracked 23,005,189 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 878 research outputs from this source. They receive a mean Attention Score of 4.1. This one has gotten more attention than average, scoring higher than 62% 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 325,897 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 21 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 66% of its contemporaries.