Implicit Learning and the Brain Implicit learning refers to situations in which complex information is acquired effortlessly (without a conscious effort), and the result- ing knowledge is difficult to express verbally (e.g., Berry, 1997; Cleeremans, Destrebecqz, & Boyer, 1998; Reber, 1967). Although many types of implicit learning exist, a com- mon process underlies most of them – the rapid, effortless, and untutored detection of patterns of covariation among events (Reber, 1993 ). p. 1

Implicit learning is pervasive across many domains, including influences on social atti- tudes and stereotypes regarding gender and race (Greenwald, Banaji, Rudman, Farnham et al., 2 0 0 2 ), visual pattern learning (Musen & Triesman, 1990), motor response time tasks (Nissen & Bullemer, 1987), syntac- tic language learning (Reber, 1976), pho- netic language learning (Goodsitt, Morgan, & Kuhl, 1993; Kuhl, 2004; Saffran, Aslin, & Newport, 1996), and young children’s imi- tative learning of the tools, artifacts, behav- iors, customs, and rituals of their culture (Meltzoff, 1988a; 2005; Rogoff et al., 2003; Tomasello, 1999). Implicit learning has edu- cational and even evolutionary value inas- much as it enables organisms to adapt to new environments by listening, observing, and interacting with the objects and peo- ple encountered there, even in the absence of formal pedagogy or a conscious effort to learn. p. 2

In contrast to informal learning, for- mal learning is characterized by: (1) the foundations and opportunities 2 3 2 4 the cambridge handbook of the learning sciences presence of universalistic values, criteria, and standards of performance (over the particularism of who is doing the teach- ing); (2) language is the dominant medium of teaching and learning, rather than the richer sensory context of modeling and observation/imitation common to infor- mal learning; and (3) teaching and learn- ing occur out of context, with mathe- matical symbol manipulation a paradigm case. p. 5

In a widely cited Science paper, Scribner and Cole (1973) reviewed many compar- ative cultural studies using cognitive and developmental methodologies to examine thinking and reasoning processes. The dis- tinctions they inferred from the empirical literature between the forms of thinking, acting, and learning in formal education and informal practical life are echoed in later influential writings by Lave (1988), Resnick (1987), and others, right up to today’s con- temporary research at and across the bound- aries of informal and formal learning. Their thesis is that “school represents a special- ized set of educational experiences which are discontinuous from those encountered in everyday life and that it requires and promotes ways of learning and thinking which often run counter to those nurtured in prac- tical daily activities.” (p. 5 5 3 ). Research from Greenfield and Bruner (1966), Luria (1971), Cole, Gay, Glick, and Sharp (1971) and oth- ers was reviewed to reveal the different skills manifest in classification, reasoning, and concept formation performances when individuals had more schooling experience. p. 5

Scribner and Cole identified three distinc- tive features of informal learning: 1. Informal learning is person-oriented, or particularistic, in that expectations of per- formance are based on who a person is instead of what he has accomplished; 2. Informal learning fosters traditionalism (since the elders are accorded the high- est group status); and 3. Informal learning involves fusing emo- tional and intellectual domains. In infor- mal learning, emotional engagement is wrapped together with cognitive involve- ment, in part because the content of knowledge is inseparable from the per- sonal identity of the teacher. p. 5

Scribner and Cole note that informal learn- ing descriptions by anthropologists also describe common mechanisms, e.g., mime- sis, identification, and cooperation (Fortes, 1938; referred to as imitation, identification, and empathy by Mead, 1964). They con- sider these three categories to be subsumed under a general domain they call “obser- vational learning” – in contrast to learning acquired primarily through language (also see Rogoff, Paradise, Mejia Arauz, Correa- Chavez et al., 2 0 0 3 for a discussion of an orientation toward learning they call “intent participation,” which relies heavily on obser- vation of adult activities). p. 5

presence of universalistic values, criteria, and standards of performance (over the particularism of who is doing the teach- ing); (2) language is the dominant medium of teaching and learning, rather than the richer sensory context of modeling and observation/imitation common to infor- mal learning; and (3) teaching and learn- ing occur out of context, with mathe- matical symbol manipulation a paradigm case. p. 6

The fact that expertise affects notic- ing has a number of important educational implications. One is that merely showing novice students videos of experts doing things does not guarantee that the novices notice all the relevant features (e.g., Michael, Klee, Bransford, & Warren, 1993). Second, an emphasis on expertise and noticing sug- gests that we do not simply learn from experience; instead, we also learn to expe- rience (e.g., Becker, 1953; Goodwin, 1994; Stevens & Hall, 1998). p. 7

We suggest the importance of investigat- ing a third dimension that appears to help drive the development of adaptive expertise: a metacognitive awareness of the distinc- tive roles and trade-offs of the innovation and efficiency dimensions of expertise, and the active design and creative structuring of one’s learning environment in order to support their dual utilities. p. 8

Schwartz, Bransford, and Sears (2 0 0 5 ) have suggested that the concept of adap- tive expertise involves at least two major dimensions; processes that lead to inno- vation or invention and those that lead to efficiency through well-practiced routines (Figure 2.2). Sometimes these two dimensions are characterized as mutually exclusive ends of a continuum (e.g., high and low road transfer, Salomon & Perkins, 1989), yet because there are different processes involved, they are not necessarily exclusive. Adaptive experts are high on both dimensions (e.g., Gentner, Brem, Ferguson, Markman, et al., 1997; Hatano & Inagaki, 1986; Wineburg, 1998). p. 8

One alternative to a direct application view of learning and transfer is a focus on adaptive expertise that has been called “preparation for future learning” (PFL) (Schwartz & Bransford, 1998; Bransford & Schwartz, 1999; Schwartz & Martin, 2004; Martin & Schwartz, 2005; Spiro, Vispoel, Schmitz, Samarapungavan et al., 1987). Here the focus shifts to assessments of a person’s abilities to learn in knowledge- rich environments. When organizations hire p. 8

new employees, they want people who can learn, and they expect them to make flexi- ble and competent use of resources to facili- tate their learning (e.g. texts, computer pro- grams, social networks of friends, and new colleagues). If people are better prepared for future learning, they will be able to transfer that learning better and faster. It is important to emphasize that the PFL perspective is different from the learning- to-learn literature; the main contrast is that PFL is not principally focused on the exis- tence of a set of general, content-free learn- ing skills. The expertise literature (Chi et al., 1988; NRC, 2000a) shows that strategies and knowledge are highly interdependent; for example, knowing a particular scientific concept can influence the hypotheses that one generates to explain world events. Ide- ally, assessments of adaptive expertise would include opportunities for people to try out hunches, receive feedback, and attempt to revise based on the feedback. In contrast, typical tests provide few opportunities for feedback and revision – the only option is to provide one’s initial thoughts, with no opportunities to test them and revise. Schwartz, Bransford, and Sears (2 0 0 5 ) show that assessments of adaptive expertise can reveal the benefits of certain educational experiences, even though those benefits are invisible when standard SPS measures of assessment are used. Many research groups are now exploring innovative ways to mea- sure adaptive expertise (Crawford, Riel & Schlager, 2005; Hatano, 2005; Martin, 2005; Petrosino, 2 0 0 5 ; Schwartz, Blair, Davis, Chang et al., 2005; Walker, 2005). p. 9

The cognitive sciences have principally focused on how routine expertise is acquired, as people get faster and more accu- rate at solving recurrent problems. Cog- nitive theories in this tradition emphasize routinized “scripts,” “schemas,” “frames” and “procedures” (for definitions and examples, see Anderson & Pearson, 1984; Anderson, 1976; Black & Bower, 1980; Bransford & Johnson, 1972; Minsky, 1986; Schank & Abelson, 1977). p. 9