Who Needs Emotions? The Brain Meets the Robot

eware the passionate robot 341
course of action (apology) that will make amends for the inappropriate
behavior; note that the situation was simplified
because this course of action was consistent with the (nonemotional?)
recognition that another appointment had to be made.
Indeed, many of our action plans are social and require interaction
with others to enlist aid, invite sympathy, bargain, intimidate,
threaten, provide rewards, etc.
The analysis can continue like this until the end of the narrative. This
section has provided a (very restricted) data set on the interaction between
perception, emotion, and action that has brought out the interaction between
cognitive processing that stresses both the “heat” and the state dependence
involved in emotions. Below, I will attempt to make sense of this data set
within an evolutionary framework (see An Evolutionary Approach to Heated
Appraisals). To set the stage for this, in the following section we present a
general evolutionary framework, then an analysis of vision and language
within that framework, and finally a look at the motivational systems which
ground the emotions.
HUGHLINGS JACKSON: AN EVOLUTIONARY
FRAMEWORK
I now offer a general framework for the study of the evolution of brain
mechanisms which will inform the following two sections. Hughlings Jackson
was a 19th century British neurologist who viewed the brain in terms of
levels of increasing evolutionary complexity (Jackson, 1878–79). Influenced
by the then still novel Darwinian concepts of evolution, he argued that damage
to a “higher” level of the brain disinhibited “older” brain regions from
controls evolved later, to reveal evolutionarily more primitive behaviors. My
arguments in this chapter will be structured by my attempt (Arbib, 1989)
to extract computational lessons from Jackson’s views on the evolution of a
system that exhibits hierarchical levels.
The process starts with one or more basic systems to extract
useful information from a particular type of sensory input.
These basic systems make data available which can provide the
substrate for the evolution of higher-level systems to extract new
properties of the sensory input.
The higher-level systems then enrich the information environment
of the basic systems by return pathways.
The basic systems can then be adjusted to exploit the new sources
of information.