Implicit Learning

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Seger (1994, p. 63) defined implicit learning as "learning complex information without complete verbalisable knowledge of what is learned". Implicit learning is of relevance here because of its relationship to implicit memory. As Seger (1994, p. 165) pointed out, "there is probably no firm dividing line between implicit memory and implicit learning".


One task used to study implicit learning is artificial grammar learning, in which the participants learn to decide whether strings of letters conform to the rules of an artificial grammar. There is progressive improvement in performance, but participants cannot explain the rules they are using (Reber, 1989).

Berry and Broadbent (1984) studied implicit learning by using a complex task in which a sugar-production factory had to be managed to maintain a specified level of sugar output. Participants learned to perform this task effectively, but most of them could not report the principles underlying their performance. Those participants whose reports revealed good knowledge of these principles tended to perform the task less well than those with poor knowledge. This suggests that the task information available to conscious awareness was of no value to the learners.

Subsequent research on complex control tasks has suggested that people have more conscious access to relevant knowledge about the task than emerged in the study by Berry and Broadbent (1984). For example, McGeorge and Burton (1989) had their participants perform a complex task, and then added the task information they supplied into a computer simulation of the task. For about one-third of the participants, this simulation produced performance comparable to that of the average participant.

A potential problem with the study by Berry and Broadbent (1984) is that the participants may have had conscious access to task-relevant knowledge, but found it hard to express this knowledge in words. Evidence of implicit learning avoiding that problem was reported by Howard and Howard (1992). They used a task in which an asterisk appeared in one of four positions on a screen, under each of which there was a key. The task was to press the key corresponding to the position of the asterisk as rapidly as possible. The position of the asterisk over trials conformed to a complex pattern. The participants showed clear evidence of learning the pattern by responding more and more quickly to the asterisk. However, when asked to predict where the asterisk would appear next, their performance was at chance level. Thus, there seemed to be implicit learning of the pattern.

Brain regions

Implicit learning can be studied by means of neuroimaging. Grafton, Hazeltine, and Ivry (1995) obtained PET scans from participants engaged in implicit learning of motor sequences. Various brain areas were activated, including the motor cortex and the supplementary motor area. Thus, brain areas that control movements of the limbs are activated during implicit motor learning.

What about explicit learning? Grafton et al. (1995) used the same motor-sequence task under conditions that made it easier for the participants to become consciously aware of the sequence. They compared the PET scans of participants who were or were not aware of the sequence. The key finding was as follows: "Explicit learning and awareness of the sequences required more activations in the right premotor cortex, the dorsolateral prefrontal cortex associated with working memory, the anterior cingulate, areas in the parietal cortex concerned with voluntary attention, and the lateral temporal cortical areas that store explicit memories" (Gazzaniga et al., 1998, p. 279).

The various findings reported by Grafton et al. (1995) indicate that different brain areas are involved in implicit and explicit learning. This is important evidence for the distinction between these two kinds of learning.

Theoretical considerations

A key theoretical question is whether learning is possible with little or no conscious awareness of what has been learned. Shanks and St. John (1994) proposed two criteria for learning to be regarded as unconscious:

1. Information criterion: The information that the participants are asked to provide on the awareness test must be the information that is responsible for their improved level of performance.

2. Sensitivity criterion: "We must be able to show that our test of awareness is sensitive to all of the relevant knowledge (Shanks & St. John, 1994, p. 11). The point here is that participants may be consciously aware of more task-relevant knowledge than appears on an insensitive awareness test, and this may lead us to underestimate their consciously accessible knowledge.

The two criteria proposed by Shanks and St. John (1994) may seem reasonable, but are hard to use in practice. However, Shanks and St. John argued that the sensitivity criterion could be replaced provided that the performance and awareness tests resemble each other as closely as possible. This was precisely what was done in the study by Howard and Howard (1992), and their findings provide strong support for implicit learning. The evidence from neuroimaging also points to the same conclusion.

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