We present a theoretical account of implicit and explicit
learning in terms of act-r, an integrated architecture of human
cognition as a computational supplement to Dienes & Perner's
conceptual analysis of knowledge. Explicit learning is explained in
act-r by the acquisition of new symbolic knowledge, whereas
implicit learning amounts to statistically adjusting subsymbolic
quantities associated with that knowledge. We discuss the common
foundation of a set of models that are able to explain data gathered
in several signature paradigms of implicit learning.