The Interaction of the Explicit and the Implicit in Skill Learning: A Dual-Process Approach Ron Sun Rensselaer Polytechnic Institute Paul Slusarz University of Missouri—Columbia Chris Terry University of Alabama This article explicates the interaction between implicit and explicit processes in skill learning, in contrast to the tendency of researchers to study each type in isolation. It highlights various effects of the interaction on learning (including synergy effects). The authors argue for an integrated model of skill learning that takes into account both implicit and explicit processes. Moreover, they argue for a bottom-up approach (first learning implicit knowledge and then explicit knowledge) in the integrated model. A variety of qualitative data can be accounted for by the approach. A computational model, CLARION, is then used to simulate a range of quantitative data. The results demonstrate the plausibility of the model, which provides a new perspective on skill learning. The role of implicit learning in skill acquisition and the distinc- tion between implicit and explicit learning have been widely recognized in recent years (see, e.g., Cleeremans, Destrebecqz, & Boyer, 1998; Proctor & Dutta, 1995; Reber, 1989; Seger, 1994; Stadler & Frensch, 1998). However, although implicit 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 (though with a few notable exceptions even early on, e.g., Mathews et al., 1989). 1 Similar oversight is also evident in computational simulation mod- els of implicit learning (with a few exceptions such as Cleeremans, 1993, and Sun, Merrill, & Peterson, 2001). Likewise, in the development of cognitive architectures (e.g., Anderson, 1983, 1993; Meyer & Kieras, 1997; Newell, 1990), the distinction between procedural and declarative knowledge has been adopted by many (Anderson, 1983, 1993). The distinction maps roughly onto that between explicit and implicit knowledge, because procedural knowledge is generally inaccessible whereas declarative knowledge is generally accessible and thus explicit. However, the focus has been mostly on top-down models (i.e., learning first explicit knowledge and then implicit knowledge); the bottom-up direction (i.e., learning first implicit knowledge and then explicit knowledge or learning both in parallel) has been largely ignored, paralleling the related neglect of the interaction of explicit and implicit processes in the skill acquisition literature. Despite such problems, it has been gaining recognition that it is difficult to find a situation in which only one type of learning is engaged (Mishkin, Malamut, & Bachevalier, 1984; Reber, 1989; Seger, 1994; Sun et al., 2001; Willingham, 1998; but see Lewicki, Czyzewska, & Hoffman, 1987). Our review of existing data (see the Existence of Interaction section) has indicated that although one can manipulate conditions to emphasize one or the other type, in most situations, both types of learning are involved, with vary- ing amounts of contributions from each. Many issues arise that we need to examine to better understand the interaction between implicit and explicit processes: How can we capture implicit and explicit processes in com- putational terms? How do the two types of knowledge develop alongside each other and influence each other’s development (e.g., top down versus bottom up)? How can bottom-up learning be realized computationally? How do the two types of knowledge interact during skilled performance, and what is the impact of that interaction on performance? 2 In the following section, Existence of Interaction, we present evidence that points to a complex, multifaceted interaction be- 1 By the explicit, we mean processes involving some form of generalized (or generalizable) knowledge that is consciously accessible. 2 For example, the synergy of the two may result, as described in Sun et al. (2001). Ron Sun, Cognitive Science Department, Rensselaer Polytechnic Insti- tute; Paul Slusarz, Department of Computer Science, University of Mis- souri—Columbia; Chris Terry, Department of Computer Science, Univer- sity of Alabama. This work was supported in part by Office of Naval Research Grant N00014-95-1-0440 and Army Research Institute Contract DASW01-00- K-0012. Thanks to Helen Gigley, Susan Chipman, Michael Drillings, Paul Gade, and Jonathan Kaplan for their support. Thanks to Ed Merrill, Jeff Shrager, Jack Gelfand, Axel Cleeremans, David Roskos-Ewoldsen, and Robert Mathews for discussions and comments. Todd Peterson developed the initial simulator, and Xi Zhang worked on its enhancement. Correspondence concerning this article should be addressed to Ron Sun, Cognitive Science Department, Rensselaer Polytechnic Institute, 110 Eighth Street, Carnegie 302A, Troy, NY 12180. E-mail: rsun@rpi.edu Psychological Review Copyright 2005 by the American Psychological Association 2005, Vol. 112, No. 1, 159 –192 0033-295X/05/$12.00 DOI: 10.1037/0033-295X.112.1.159 159