Knowledge Representation, Heuristics, and Awareness in Artificial Grammar Learning

University dissertation from Department of Psychology, Lund University

Abstract: People can become sensitive to the general structure of different parts of the environment, often without studying that general structure directly, but through being incidentally exposed to instances that conform to the structure. When such learning proceeds unintentionally and gives rise to knowledge that is difficult to verbalize it is often referred to as implicit learning. One of the most commonly used experimental paradigms in the study of implicit learning is artificial grammar learning, in which participants are exposed to sequences that conform to a set of rules without being informed about the presence of rules. In a subsequent test phase, participants can usually distinguish between sequences that conform to and sequences that violate the rules, without being able to say much about the underlying rules. There are many different theories about the kind of knowledge representations that underlie sensitivity to general structure in artificial grammar learning, and there are also different viewpoints concerning how to measure the conscious status of the knowledge acquired in artificial grammar learning. Investigating these different theories is important, partly because it may provide an understanding of the extent to which complex learning and abstraction of structure proceeds unconsciously. Study I of this thesis investigated artificial grammar learning and the use of a fluency heuristic, which involves relying on the surprising ease of processing an item as a basis for making a judgment. Other studies have shown that the fluency heuristic is used in a wide variety of judgments (e.g., recognition and preference). Study I showed that participants rely on a fluency heuristic in artificial grammar learning as well, but mainly under non-analytic pro¬ces¬sing conditions when participants were encouraged to respond rapidly and thereby make global judgments about items without processing details to any large extent. This is consistent with the idea that fluency may provide a cue for indirect sensitivity to general structure. Study II investigated the effect of non-analytic processing on the conscious status of knowledge as assessed by confidence judgments. It was found that non-analytic processing increased the availability of conscious knowledge, consistent with the idea that part of the knowledge acquired in artificial grammar learning may be, not inherently unconscious, but of a kind that is available through a non-analytic form of introspection. One possibility is that, relative to more analytic forms of introspection, non-analytic introspection may be more sensitive to the non-focal peripheral contents of consciousness, the so called “fringe consciousness”. This could explain why the knowledge acquired in artificial grammar learning often seems intuitive, even though it is not necessarily unconscious. Study III investigated whether artificial grammar learning gives rise to knowledge that is independent from the surface features of the exposure material. A number of claims have been offered in the literature for such surface-independent knowledge, particularly as a result of extended exposure to regularities. The results clearly suggested that the knowledge formed under observational learning conditions in artificial grammar learning is not independent from the surface features of the exposure material. The results are consistent with a variety of computational models of artificial grammar learning that rely on surface-dependent perceptual representations. Finally, Study IV investigated whether the knowledge acquired in artificial grammar learning is unconscious in the sense that it may be expressed unintentionally. The results showed that, to the extent that knowledge was expressed, it was expressed intentionally. However, the low levels of performance in Study IV limit the generality of the findings. Possible reasons for the low performance are discussed in the context of different models of artificial grammar learning. Taken together, the studies in this thesis illuminate issues regarding both knowledge representation and the conscious status of knowledge in artificial grammar learning. In general, the studies are in line with an episodic framework according to which the general abstract structure of a domain is not automatically extracted. Instead, both learning and awareness proceeds as a function of task demands, intentions, expectations, and processing strategies.

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