Page 89 - Socially Intelligent Agents Creating Relationships with Computers and Robots

72 Socially Intelligent Agents

to the feet. The type of stimuli are calculated on the basis of a minimal time
unit or chunk. When a chunk ends, information about stimuli—their number
and type—is analyzed and the different emotions are assigned intensity levels
according to the various stimulation patterns in our emotion activation model.
The emotion with the highest intensity defines the emotional state and expres-
sion of the robot. This model of emotion activation is implemented by means
of a timed finite state machine described in [3].

3. Playing with Feelix
Two aspects of Feelix’s emotions have been investigated: the understand-
ability of its facial expressions, and the suitability of the interaction patterns.
6
Emotion recognition tests , detailed in [3], are based on subjects’ judgments
of emotions expressed by faces, both in movement (the robot’s face) and still
(pictures of humans). Our results are congruent with findings about recogni-
tion of human emotional expressions reported in the literature (e.g., [5]). They
show that the “core” basic emotions of anger, happiness, and sadness are most
easily recognized, whereas fear was mostly interpreted as anxiety, sadness, or
surprise. This latter result also confirms studies of emotion recognition from
pictures of human faces, and we believe it might be due to structural sim-
ilarities among those emotional expressions (i.e. shared AUs) or/and to the
need of additional expressive features. Interestingly, children were better than
adults at recognizing emotional expressions in Feelix’s caricaturized face when
they could freely describe the emotion they observed, whereas they performed
worse when given a list of descriptors to choose from. Contrary to our initial
guess, providing a list of descriptors diminished recognition performance for
most emotions both in adults and in children.
The plausibility of the interactions with Feelix has been informally assessed
by observing and interviewing the same people spontaneously interacting with
the robot. Some activation patterns (those of happiness and sadness) seem to be
very natural and easy to understand, while others present more difficulty (e.g.,
it takes more time to learn to distinguish between the patterns that activate sur-
prise and fear, and between those that produce fear and anger). Some interest-
ing “mimicry” and “empathy” phenomena were also found. In people trying
to elicit an emotion from Feelix, we observed their mirroring—in their own
faces and in the way they pressed the feet—the emotion they wanted to elicit
(e.g., displaying an angry face and pressing the feet with much strength while
trying to elicit anger). We have also observed people reproducing Feelix’s
facial expressions during emotion recognition, this time with the reported pur-
pose of using proprioception of facial muscle position to assess the emotion
observed. During recognition also, people very often mimicked Feelix’s ex-