Artificial Intelligence 3

Thinking humanly: The cognitive modelling approach
If we are going to say that a given program thinks like a human, we must have some
way of determining how humans think. We need to get inside the actual
workings of human minds. There are two ways to do this: through intro
spection- trying to catch our own thoughts as they go by - and through
psychological experiments. Once we have a sufficiently precise theory
of the mind, it becomes possible to express the theory as a computer
program. If the program's input/output and timing behaviors match
corresponding human behaviors, that is evidence that some of the programs
mechanisms could also be operating in humans. For example, Allen Newell
and Herbert Simon, who developed GPS, the "General Problem Solver"
(Newell and Simon, 1961), were not content to have their program solve
problems correctly. They were more concerned with comparing the trace
of its reasoning steps to traces of human subjects solving the same
problems. The interdisciplinary field of cognitive science brings toge
ther computer models from AI and experimental techniques from psychology
to try to construct precise and testable theories of the workings of the
human mind.
Cognitive science is a fascinating field worthy of an encyclopedia in
itself (Wilson and Keil, 1999). We will not attempt to describe what is
known of human cognition in this article. We will occassionally comment on
similarities or differences between AI techniques and human cognition.
Real cognitive science, however, is necessarily based on experimental
investigation of actual humans or animals, and we assume that the reader
has access only to a computer for experimentation.
In the early days of AI there was often confusion between the
approaches: an author would argue that an algorithm performs well on a
task and that it is therefore a good model of human performance, or vice
versa. Modern authors separate the two kinds of claims; this distinction
has allowed both AI and cognitive science to develop more rapidly. The
two fields continue to fertilize each other, especially in the areas of
vision and natural language. Vision in particular has recently made
advances via an integrated approach that considers neurophysiological
evidence and computational models.