@misc{cogprints798,
          editor = {U. Schmid and J.F. Krems and F. Wysotzki},
           title = {Explicit learning in ACT-R},
          author = {Niels A. Taatgen},
       publisher = {Pabst, Lengerich},
            year = {1999},
           pages = {233--252},
         journal = {Mind modelling: a cognitive science approach to reasoning, learning and discovery},
        keywords = {Cognitive modelling, cognitive modeling, ACT-R, discrimination-shift learning, skill learning, implicit learning, explicit learning, balanced beam, insight theory},
             url = {http://cogprints.org/798/},
        abstract = {A popular distinction in the learning literature is the distinction between implicit and explicit learning. Although many studies elaborate on the nature of implicit learning, little attention is left for explicit learning. The unintentional aspect of implicit learning corresponds well to the mechanistic view of learning employed in architectures of cognition. But how to account for deliberate, intentional, explicit learning? This chapter argues that explicit learning can be explained by strategies that exploit implicit learning mechanisms. This idea is explored and modelled using the ACT-R theory (Anderson, 1993). An explicit strategy for learning facts in ACT-R?s declarative memory is rehearsal, a strategy that uses ACT-R?s activation learning mechanisms to gain deliberate control over what is learned. In the same sense, strategies for explicit procedural learning are proposed. Procedural learning in ACT-R involves generalisation of examples. Explicit learning rules can create and manipulate these examples. An example of these explicit rules will be discussed. These rules are general enough to be able to model the learning of three different tasks. Furthermore, the last of these models can explain the difference between adults and children in the discrimination-shift task.}
}

<script src="//archive-bar.soton.ac.uk/archive-bar.js"></script>