On the computational utility of consciousness
We propose a computational framework for understanding and modeling human
consciousness. This framework integrates many existing theoretical
perspectives, yet is sufficiently concrete to allow simulation experiments.
We do not attempt to explain qualia (subjective experience and feelings), but
instead ask what differences exist within the cognitive information processing
system when a person is conscious of some information versus when that
information is unconscious. The central idea we explore is that the contents
of consciousness correspond to temporally stable states in an interconnected
network of specialized computational modules. Each module is an
associative memory that operates in two stages: (1) a fast, essentially
feedforward, input-output mapping that attempts to achieve an appropriate
response to a given input, and (2) a slower relaxation search that is
concerned with achieving semantically well-formed states. It is the stable
attractors of the relaxation search that reach conscious awareness. To
illustrate the operation of a module, we model performance on a simple
arithmetic task and show that the sequence of stable states in our model
corresponds roughly to the conscious mental states people experience when
performing this task. What might be the computational utility of stable
states within the cognitive architecture? Our simulations show that
periodically settling to stable states improves performance by cleaning up
inaccuracies and noise, forcing decisions, and helping to keep the system on
track toward a solution.
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