Who Needs Emotions? The Brain Meets the Robot

Energy Variance
350
300
250
200
150
100
50
robot emotion 299
0
100 150 200 250 300 350 400 450 500 550
Pitch Mean
Figure 10.7. Feature space of all five classes with respect to energy variance
and pitch mean. There are three distinguishable clusters (roughly partitioned
by arousal and valence) for prohibition, for soothing and neutral, and for
approval and attention.
region. Finally, the neutral samples have low-pitch mean but are divided into
two regions in terms of their energy variance values. The structure of each of
the mini-classifiers follows logically from these observations. Table 10.3 shows
the resulting classification performance using a Gaussian mixture model, updated
with the Expectation-Maximization algorithm, to represent the distribution
of data. The output of each affective intent classifier is treated as an
affective releaser and sent with the others to the affective appraisal phase.
Affective Appraisal
negative
valence
positive
arousal
approval
attention
soothing
neutral
prohibition
In Kismet’s implementation, there is an explicit assessment phase for each
active releaser, of which there are a number of factors that contribute to the
assessment made. The assessment consists of labeling the releaser with affective
tags, a mechanism inspired by Damasio’s (1994) somatic marker
hypothesis, where incoming perceptual, behavioral, or motivational information
is “tagged” with affective information. The tagged value reflects
whether the releaser is expected to be rewarding or punishing to the robot.
low
high