Who Needs Emotions? The Brain Meets the Robot

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networks that participate in the focusing of attention on the loved one by
working memory. Bodily responses will also be initiated as outputs of attachment
circuits. These responses contrast with the alarm responses initiated
by fear and stress circuits. We approach rather than try to escape from
or avoid the person, and these behavioral differences are accompanied by
different physiological conditions within the body (James, 1890; Damasio,
1999). This pattern of inputs to working memory from within the brain and
from the body biases us more toward an open and accepting mode of processing
than toward tension and vigilance (Porges, 1998). The net result in
working memory is the feeling of love. This scenario is certainly incomplete,
but it shows how we can build upon research on one emotion to generate
hypotheses about others.
CONCLUSION
This chapter has demonstrated the ways in which a focus on the study of
fear mechanisms, especially the mechanisms underlying fear conditioning,
can enrich our understanding of the emotional brain (LeDoux, 1996). This
work has mapped out pathways involved in fear learning in both experimental
animals and humans and has begun to shed light on interactions between
emotional and cognitive processes in the brain. While the focus on fear conditioning
has its limits, it has proven valuable as a research strategy and provides
a foundation upon which to build a broader understanding of the mind
and brain.
At the same time, there is a disturbing rush to embrace the amygdala as
the new center of the emotional brain. It seems unlikely that the amygdala
is the answer to how all emotions work, and it may not even explain how all
aspects of fear work. There is some evidence that the amygdala participates
in positive emotional behaviors, but that role is still poorly understood.
Understanding fear from the neuroscience point of view is just one of
many ways of understanding emotions in general. Other disciplines can
undoubtedly help. The past few decades have seen the emergence of interdisciplinary
work in computational modeling and neuroscience (Arbib, 2003).
The use of computational modeling techniques has proved essential in understanding
experimentally intractable phenomena such as complex intracellular
signaling pathways involving dozen of simultaneously interacting
chemical species or the way large networks of tens of thousands of neurons
process information (Bialek et al., 1991, 2001; Dayan & Abbott, 2003).
Conversely, neural computation has provided inspiration to many engineers
and computer scientists in fields ranging from pattern recognition to machine
learning (Barto & Sutton, 1997). The topic of emotion is still on the side-