Reference Paper
SHARED
UNDERSTANDING AND SYNCHRONY EMERGENCE
Synchrony as an Indice of the Exchange
of Meaning between Dialog Partners
Ken
Prepin and Catherine Pelachaud
Overview of the Paper
In face-to-face dialog, synchrony one of the most crucial
parameters claimed by the psychologist. The quality of the interaction is
perceived by the human from the verbal and non-verbal synchrony. An artificial
agent should be able to synchronize with its human counterpart to give the
human a feeling of natural interaction. In this the authors present a dynamic
model of verbal and non-verbal communication. In test simulations, they show
that if the partners in dyad understand each other, then the synchrony emerges,
else synchrony disrupted.
They design an interaction model between two agents, agent1
and agent2. Each agent’s state is represented by variable S. Speech produced by each agent is represented by Vact and the speech heard by each
agent, the perceived signal is presented by Vper.
So the dyadic communication model is represented by Figure1. Here, two ‘level
of understanding’ parameters are presented by u and u’.
Again, agent’s internal states are reflected by its
non-verbal behavior. The no-verbal behavior of the agent is a function of its
internal state. Now after incorporating its non-verbal response, the model
looks like Figure2. Here each agent shows some non-verbal act, presented by NVact.
Figure2
Moreover, humans are sensitive to perceived behavior and
synchrony. So, a modification of this model is needed, which should include the
perceived non-verbal behavior of the agent. So, after perceiving the verbal (Vper) and non-verbal (NVper) act of other agent, the agent’s
internal state is changed. Figure3 presents the updated model.
These agents have internal dynamics which control their
behavior and also they must be influenced by other’s behavior. The model can be
expressed by the following equations.
Now, replacing the internal states S, the following equations
are obtained. From these equations, it is observed that, the agents are not
only influenced by the state of others, but also influenced by their own state.
Evaluation
To simulate the result, they use neural network simulator
Leto/Prometheus. This simulator updates the whole network at each time. Agent’s
internal state is presented by relaxation oscillators. In each step, the neuron
feeds the oscillator of both agents. The relaxation oscillator value increases
linearly and decreases rapidly when it reaches the threshold value 0.95.
Then they simulate for 5000 time step simulates. They
consider the signals synchronized if the phase shifts becomes near zero before
time step 3000, are remains consistent later. A synchronization result is shown
in Figure4.
They also try with different values of the model parameters
and simulation. From their simulations, they found that, when the agents
understanding do not differ more than 15%, then the agent’s will eventually be synchronized,
no matter what their initial phase shift was. And, if the understanding differs
more than 15%, then they will be desynchronized.
Validity of the Paper
In this paper, authors show two main results. They show that
dis-synchronization happens for misunderstanding and they are very rapid. They
also show that synchrony is a proof of good interaction. They use a very useful
and strong analysis method, named time lag analysis.
Improvement Scopes
I think the main challenge faced by the researchers in this
field is human unpredictability. It will be nice to see how this model will
work on human-agent interaction in future. Moreover, the main challenges still
remain to implementation this model, understanding the verbal and non-verbal
behavior of human subject.
Further Reading
One of the
interesting articles, which are cited by this paper, is “Nonverbal synchrony and rapport”, by Marianne
LaFrance (Digital Object Identifier: 10.2307/3033875). In the
cited article, the author shows that the posture sharing and rapport are
positively correlated. She also presents a hypothesis that, posture sharing may
be an influential factor of establishing a rapport.
References
[1] K. Prepin and C. Pelachaud, “Shared Understanding and
Synchrony Emergence - Synchrony as an Indice of the Exchange of Meaning between
Dialog Partners,” In proceedings of ICAART2011, International Conference
on Agent and Artificial Intelligence. Rome, It. Vol. 2, pp(25- 34)
[2] M. LaFrance, “Nonverbal synchrony
and rapport: Analysis by the cross-lag panel technique,” in Social Psychology
Quarterly, 1979, Vol. 1, pp(66–70)
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