The Interaction of Explicit and Implicit Learning: An Integrated Model

The Interaction of Explicit and Implicit Learning: An Integrated Model Paul Slusarz (kmicic@ideaworks.com) Ron Sun (rsun@cecs.missouri.edu) Department of CECS University of Missouri-Columbia Columbia, MO 65211, USA Abstract This paper explicates the interaction between the implicit and explicit learning processes in skill acquisition, con- trary to the common tendency in the literature of studying each type of learning in isolation. It highlights the inter- action between the two types of processes and its vari- ous effects on learning, including the synergy effect. This work advocates an integrated model of skill learning that takes into account both implicit and explicit processes; moreover, it embodies a bottom-up approach (first learn- ing implicit knowledge and then explicit knowledge on its basis) towards skill learning. The paper shows that this approach accounts for various effects in the process control task data, in addition to accounting for other data reported elsewhere. Introduction The role of implicit learning in skill acquisition and the distinction between implicit and explicit learning have been widely recognized in recent years (see, e.g., Re- ber 1989, Stanley et al 1989, Willingham et al 1989, Proctor and Dutta 1995, Anderson 1993), Although im- plicit learning has been actively investigated, complex and multifaceted interaction between the implicit and the explicit and the importance of this interaction have not been universally recognized; to a large extent, such in- teraction has been downplayed or ignored, with only a few notable exceptions. Research has been focused on showing the lack of explicit learning in various learning settings (see especially Lewicki et al 1987) and on the controversies stemming from such claims. Similar over- sight is also evident in computational simulation models of implicit learning (with few exceptions such as Cleere- mans 1994). Despite the lack of studies of interaction, it has been gaining recognition that it is difficult, if not impossible, to find a situation in which only one type of learning is engaged (Reber 1989, Seger 1994, but see Lewicki et al 1987). Our review of existing data (see Sun et al 2001) has indicated that, while one can manipulate con- ditions to emphasize one or the other type, in most situ- ations, both types of learning are involved, with varying amounts of contributions from each (see, e.g., Sun et al 2001; see also Stanley et al 1989, Willingham et al 1989). Likewise, in the development of cognitive architec- tures (e.g., Rosenbloom et al 1993, Anderson 1993), the distinction between procedural and declarative knowl- edge has been proposed for a long time, and advocated or adopted by many in the field (see especially Ander- son 1993). The distinction maps roughly onto the dis- tinction between the explicit and implicit knowledge, because procedural knowledge is generally inaccessible while declarative knowledge is generally accessible and thus explicit. However, in work on cognitive architec- tures, focus has been almost exclusively on “top-down” models (that is, learning first explicit knowledge and then implicit knowledge on the basis of the former), the bottom-up direction (that is, learning first implicit knowl- edge and then explicit knowledge, or learning both in parallel) has been largely ignored, paralleling and reflect- ing the related neglect of the interaction of explicit and implicit processes in the skill learning literature. How- ever, there are a few scattered pieces of work that did demonstrate the parallel development of the two types of knowledge or the extraction of explicit knowledge from implicit knowledge (e.g, Rabinowitz and Goldberg 1995, Willingham et al 1989, Stanley et al 1989), contrary to usual top-down approaches in developing cognitive ar- chitectures. Many issues arise with regard to the interaction be- tween implicit and explicit processes: (1) How can we best capture implicit and explicit processes computation- ally? (2) How do the two types of knowledge develop along side each other and influence each other’s devel- opment? (3) How is bottom-up learning possible and how can it be realized computationally? (4) How do the two types of knowledge interact during skilled perfor- mance and what is the impact of that interaction on per- formance? For example, the synergy of the two may be produced, as in Sun et al (2001). In this paper, we will focus on the interaction and the synergy resulting from the interaction. A Model Let us look into a model that incorporates both implicit and explicit processes. Representation. The inaccessible nature of implicit knowledge may be captured by subsymbolic distributed representations provided by a backpropagation network (Rumelhart et al 1986). This is because representational units in a distributed representation are capable of ac- complishing tasks but are subsymbolic and generally not