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The stability of these states of cross-perception and synchrony is a direct conse-
quence of the reciprocal influence between the agents.
We have seen there that literature stresses two main results concerning synchrony.
First, synchrony of non-verbal behaviours during verbal-interactions is a necessary ele-
ment for a good interaction to take place: synchrony reflects the quality of the interac-
tion. Second, synchrony has been described and modelled as a phenomenon emerging
from the dynamical coupling between agents during non-verbal interactions. In this
paper, we propose to conciliate these two results in a model of synchrony emergence
during verbal interactions.
We propose and test in simulation a model of verbal communication which links
the emergence of synchrony of non-verbal behaviours to the level of shared informa-
tion between interactants: if partners understand each other, synchrony will arise, and
conversely if they do not understand each other enough, synchrony could not arise. By
constructing this model of agents able to interact as humans do, on the basis of psychol-
ogy, neuro-imaging and modelisation results, that are both the understanding of humans
and the believability of artifacts (e.g. virtual humans) which are assessed.
In Sect.2 we describe the architecture principle and show how a level of understand-
ing can be linked to non-verbal behaviours. In Sect.3, we test this architecture, i.e. we
test in simulation a dyad of architectures which interact together. We characterise the
conditions of emergence of coupling and synchrony between the two virtual agents.
Finally, in Sect.4, we discuss these results and their outcomes.
2
Model Principle
We propose a model accounting for the emergence of synchrony depending directly on
a shared level of understanding between agents. This model is based on the four next
properties of humans' interactions:
P1.
To emit or receive a discourse modify the internal state of the agent [25].
P2.
Non-verbal behaviours reflect the internal states [14].
P3.
Humans are particularly sensitive to synchrony, as a cue of the interaction quality
and and the mutual understanding between participants [6,22,24].
P4.
Synchrony can be modelled as a phenomenon emerging from the dynamical cou-
pling of agents [23,19,1]
The model of agent we propose in the present section is implemented in Sect.3 as a
Neural Network (NN). Groups of neurons are vectors of variables represented by capital
letters (e.g.
V
Input
∈
1
,
1]
m
) and the weights matrices which
modulate the links between these groups are represented by lower case letters (e.g.
u
1
,
1]
n
and
S
[
−
∈
[
−
1
,
1]
m×n
): we obtain equations such as
u
V
Input
=
S
. For sake of simplicity, in
both the description of the model principle (this section) and in its implementation and
tests (Sect.3) groups of neurons and weights matrices are reduced to single numerical
variables (
∈
[
−
·
1
,
1]
).
In the next two subsections, we model the two first properties, P1 and P2. We de-
scribe how the non-verbal behaviour can be linked to a level of mutual understanding.
∈
[
−
