Who Needs Emotions? The Brain Meets the Robot

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different from such fear behavior as fleeing or freezing). Emotional
feelings are tied up with notions of consciousness, but it is
well known that one may be conscious of the possible emotional
overtones of a situation yet not feel emotionally involved oneself
(and brain damage may leave a person incapable of emotional
feelings; cf. the Chapter 3 section “A Modern Phineas Gage” in
Damasio, 1994).
Below, we will discuss the notion of emotion as suitable for characterizing
aspects of the behavior and inner workings of robots that share with
humans neither an evolutionary history as flesh-and-blood organisms nor the
facial or vocal expressions which can ground empathy. In particular, we will
return to the question of ecological niches for robots and the issue of to what
extent emotions may contribute to, or detract from, the success of a “species”
of robots in filling their ecological niche.
Elsewhere (e.g., Arbib, 1989), I have developed a theory of schemas as
functional (as distinct from structural) units in a hierarchical analysis of the
brain. Extant schemas may be combined to form new schemas as coordinated
control programs linking simpler (perceptual and motor) schemas to
more abstract schemas which underlie thought and language more generally.
The behavioral phenotype of an organism need not be linked to a localized
structure of the brain but may involve subtle patterns of cooperative
computation between brain regions which form a schema. Selection may thus
act as much on schemas as it does on localized neural structures. Developing
this view, Arbib and Liaw (1995) argued that evolution yields not only
new schemas connected to the old but also reciprocal connections which
modify those older schemas, linking the above Jacksonian analysis to the
language of schema theory.
EVOLUTION OF THE BRAIN MECHANISMS
SUPPORTING VISION AND LANGUAGE
Over the years, I have attempted to create a comparative computational
neuroethology (i.e., a comparative computational analysis of neural mechanisms
underlying animal behavior) in which the brains of humans and other
creatures come to be better understood by seeing homologous mechanisms
as computational variants which may be related to the different evolutionary
history or ecological niche of the creatures that contain them. Arbib
(2003) stresses the notion of “conceptual neural evolution” as a way of understanding
complex neural mechanisms through incremental modeling. Although
somewhat ad hoc, this process of adding features to a model “to see