Cognition based on implicit cognition

Traditionally it is explicit, declarative knowledge which teacher educators have focused on in teacher education (Borg, 2003b; Wallace, 1991). For example, Fillmore and Snow claim that “[t]eachers need to know that spoken language is composed of units of different sizes: sounds…morphemes…words…phrases…sentences, and discourses”

(Fillmore & Snow, 2002: 20) [Emphasis added]. This stems from views of cognition which stress the superiority of explicitly worked out ideas regardless of the situation.

“The view of persona and human action which has long dominated Western culture is…that intelligent action requires deliberate thought” (Tomlinson, 1999a: 405). In some ways this is hardly surprising as one of academics main jobs is the production of explicit knowledge (Bartels, 2003; Becher & Trowler, 2001), thus they are simply assuming that the products of their labor are important enough for teachers (Bartels, 2004). This means that despite its importance, tacit knowledge has little status in many institutions.

“Although tacit knowledge is important to success, organizations often give no recognition to it” (Sternberg, 1999: 232). One reason organizations tend to favor explicit knowledge is that it is only when ideas are made explicit that they can be clearly debated and empirically investigated.

However, different kinds of cognition each have different advantages and disadvantages.

According to Tomlinson, implicit knowledge can be processed much quicker than explicit knowledge as it does not require working memory capacity: “humans have two major forms of information-processing: (i) a conscious serial mode that is deliberately focused and flexible, but also relatively slow and severely limited in capacity and (ii) a tacit parallel mode which is a very fast processor of much information simultaneously, but which is relatively inflexible and not open to direct access or control” (Tomlinson, 1999a: 415). Tomlinson (1999a) points out that declarative knowledge (knowing about something) can also be implicit. For example, you may notice that something is odd or wrong about an object or a situation, but not be able to explicitly say what that is.

Furthermore, most knowledge likely consists of a mixture of knowing how and knowing about. In an MA exam on SLA one not only needs to know facts, models and research results; one also needs to know how to talk about them in ways that are seen as legitimate in the SLA community (Wright & Bolitho, 1997; Hedgcock, 2002; Kramsch, 2005; Ramanathan, 2002). Knowing also combines both explicit and implicit knowledge, rather than just one. For example, “explicit cognitive processes involve implicit processes embedded within them” (Tomlinson, 1999a: 13). Therefore, Tomlinson suggests that instead of thinking in dualist terms of declarative vs. procedural or implicit vs. explicit, we should think of knowledge in terms of two continuums. One continuum would range from “capacity for action” (i.e. being able to do things) to “awareness of

reality”. The second continuum would track “the level of consciousness at which the knowledge is held”: the explicit to implicit continuum (Tomlinson, 1999a: 416).

Many claim that implicit knowledge is what teachers use when engaged in their practice (Eraut, 2000a, 2000b; Furlong, 2000). “The knowledge embedded in expert systems is largely informal knowledge” (Bereiter & Scardamalia, 1993: 52-3). According to Schön

“the workaday life of the professional depends on tacit knowing-in-action” (Schön, 1983: 49). Research on cognition and the brain supports the idea that most cognition (even academics) is implicit. “Consciousness and its sidekick, natural language, are new kids on the evolutionary block – unconscious processing is the rule rather than the exception throughout evolution. And the coin of the evolutionarily old unconscious mental realm is nonverbal processing” (LeDoux, 1996: 71). First of all, practitioners often find it difficult to explain what they do, which is often a sign of implicit knowledge (Bartels, 1999; Bereiter & Scardamalia, 1993; Schön, 1983). Furthermore, implicit knowledge, unlike explicit knowledge, would not need to be processed in working memory, so relying on knowledge that is mainly implicit would relieve the pressure on the “cognitive bottleneck” and allow teachers to process more knowledge more quickly.

This is especially important for teachers because “the number and complexity of professional decisions made every working day by teachers…is such that they cannot be explained only in terms of the conscious application of specific, taught ‘skills’”

(Wallace, 1991: 50).

It has been shown that implicit knowledge can guide the participation in complex tasks.

For example, Klayman (1988) had participants look at a computer screen with a few geometrical shapes on it. A straight line would begin from various points in the screen and participants would have to judge where the line would end. As with the artificial grammar experiments, participants became relatively accurate at predicting the endpoint of the lines, but could not explain how they knew where the line would end. Lewicki and his colleagues produced the same findings with similar tasks (Lewicki, 1985, 1986a, 1986b; Lewicki, Czyzewska, & Hoffman, 1987; Lewicki, Hill, & Bizot, 1988).

More everyday tasks were used by Berry and Broadbent in a series of studies (Berry &

Broadbent, 1984, 1988, 1990). For example, participants learned to estimate the sugar production of a factory depending on the number of factory workers or to decide how to figure out which factory was polluting a river while keeping the number of tests for pollutants to a minimum. In each case there was a rule which depended on several factors to be learned. As in the other studies, Berry and Broadbent found that their participants learned the rules, but could not articulate them. In a study of 39 second graders, Siegler and Stern (1998) found that 90% of the students showed an implicit understanding of inversion problems (by solving math problems) before being able to explain their solutions. While these students were able to eventually explain their problem-solving, this came after they had developed proficiency in solving those types of problems, indicating that implicit knowledge was used for problem-solving and explicit knowledge was more used for explanations after the fact. This has led some to conclude that the use of explicit knowledge is used more for constructing explanations of one’s actions than in carrying out the actions themselves (Camerer & Johnson, 1991).

This is not to say that implicit knowledge cannot be faulty or highly problematic for teachers. “Preservice and inservice teachers can be strongly influenced by intuitions, myths, and folk theories, which at times coincide with well-informed views and which at

others fly in the face of axiomatic principles widely held among LT professionals”

(Hedgcock, 2002: 302). For example, some teachers implicitly decide that students with a different socioeconomic background than themselves will do poorer academically, regardless of actual skill or academic achievement (Hauser-Cram, Sirin & Stipek, 2003).

Therefore, it has been proposed that acquiring implicit knowledge should be accompanied by processes which help make such knowledge explicit in order to examine it and compare it to more rigorous knowledge (Freeman, 1991a; Torff, 1999). However useful explicit knowledge may for developing, examining and influencing implicit knowledge, this should not obscure the fact that ultimately it will be mainly implicit knowledge that teachers will use in teaching and, therefore, it is the ultimate growth of implicit knowledge, not explicit knowledge, which should primarily be the ultimate goal of teacher education and teacher development. This means that implicit knowledge should be a major focus in teacher education programs. “[T]he main case I want to make here is for taking implicit learning much more seriously in teacher preparation, not just passively, but by seeking to harness such features as the ‘exquisite sensitivity’

connectionist studies point to in human awareness” (Tomlinson, 1999b: 534).

SLTE programs, like most higher education programs, focus primarily on the development of explicit knowledge, especially that of academic disciplines. However, there is mounting evidence that people rely principally on their implicit knowledge, not explicit, in guiding their actions. Implicit knowledge is vital to professional performance because it requires little of scarce explicit working memory processing capacity and, thus, allows us to avoid the cognitive bottleneck. This does not mean, however, that all implicit knowledge is helpful. It is important to help teachers make their implicit knowledge explicit so that this can be analyzed and evaluated. However, this can only be done if SLTE programs focus on personal, implicit knowledge rather than discipline-based explicit knowledge.

In addition, humans are adept at using implicit knowledge for learning and reasoning, areas which were once thought to be exclusively controlled by explicit knowledge. For example, Lewicki and his colleagues found that if people start categorizing something according to an implicit rule, they will categorize subsequent information the same way regardless of whether the categorization really fits or not. For example, in one experiment they showed participants slides of brain scans. Certain features in half the brain scans were shown darker, but to an almost unnoticeable extent. (The participants reported not seeing these markings.) The marked brains were given as examples of

“intelligent” brains and the others as examples of “unintelligent brains. Subsequently, the participants rated brains with the markings as more intelligent than non-marked brains, even though they received no further feedback that these brains were really more intelligent (Hill, Lewicki, Czyzewska, & Boss, 1989; Lewicki, Hill, & Sasaki, 1989).

Furthermore, Lewicki was also able to show that such implicit learning is not limited to noticing patterns, but also applied to reasoning. In a series of experiments participants were shown pictures of an actor engaging in everyday activities (opening a jar, drinking from a can, etc.) except the video was processed so that all they saw were lines and dots written on the actors’ arms and legs. In a pattern somewhat similar to figuring out that if

“a=b and b=c then a=c”, participants first learned that if there was a large distance between the stripes on the actor but the dots were small, then this was a likeable person.

Then they learned that if the distance between the stripes was small and there were no dots, then the person was likeable. On subsequent trials they tended to rate the pattern of no stripes and large dots as likeable, even though they had not been trained to notice this.

When asked, none of the participants had explicitly noticed any of these patterns, but still clearly rated pictures according to these rules (Lewicki, Hill, & Czyzeska, 1994).