  Font Size:  Unsurpervised Learning in Hybrid Cognitive
Architectures Yury Vinokurov, Christian Lebiere, Dean Wyatte,
Seth Herd, Randall O'Reilly  Last modified: 2012-07-15   We
present a model of unsupervised learning in the hybrid SAL
(Synthesis of ACT-R and Leabra) architecture. This model follows
the hypothesis that higher evaluative cognitive mechanisms can
serve to provide training signals for perceptual learning. This
addresses the problem that supervised learning seems necessary
for strong perceptual performance, but explicit feedback is rare
in the real world and difficult to provide for artificial
learning systems. The hybrid model couples the perceptual
strengths of Leabra with ACT-R's cognitive mechanisms,
specifically its declarative memory, to evolve its own symbolic
representations of objects encountered in the world. This is
accomplished by presenting the objects to the Leabra visual
system and committing the resulting representation to ACT-R's
declarative memory. Subsequent presentations are