Running Head: DISSOCIATIONS IN GRAMMAR LEARNING

Three artificial grammar learning experiments investigate the memory processes underlying classification judgments. In Experiment 1, the effects of grammaticality, specific similarity and chunk frequency are found to be similar between classification and recognition. In Experiment 2a and 2b, instructions to exclude "old" and "similar" items under conditions that make conscious recollection difficult are found to dissociate grammaticality and similarity effects in classification. Finally, dividing attention at test in Experiment 3 also dissociates these effects, but the pattern of data underlying the dissociation is different from Experiments 2a and 2b. On the basis of these results, it is concluded that grammaticality effects are based on automatic memory processes, whereas intentional uses of memory largely support the specific similarity effect. It is argued that these results are consistent with a dual-process model of classification, but inconsistent with unitary mechanism accounts. The experiments also indicate that chunk frequency has both implicit and explicit influences on classification judgments. Dissociations in Grammar Learning 3 Dissociations of Grammaticality and Specific Similarity Effects in Artificial Grammar Learning People acquire sensitivity to complex regularities in their world seemingly without complete awareness of what they have learned. Although so-called implicit learning has been studied at length with various paradigms (see Seger, 1994 and Shanks & St. John, 1994 for reviews), by far the most common is artificial grammar learning (Reber, 1967, 1989). A typical artificial grammar learning experiment consists of a training phase and a transfer or test phase. During the training phase, participants are exposed to unpronounceable strings of letters that conform to a Markovian system (finite state grammar) and are asked to memorize them. No mention is made of the underlying rule structure until the beginning of the test phase. Armed with knowledge that there exists a rule structure, but not informed of what this structure is, during the test phase participants are asked to perform a classification task. In this task, participants attempt to discriminate novel strings that conform to the grammar (grammatical) from strings that do not (nongrammatical). The typical result is that they are able to make this discrimination well above chance (i.e., they demonstrate a grammaticality effect), but post-experimental interviews indicate that the knowledge used to make the discrimination cannot be well articulated. Explaining the Grammaticality Effect: The Role of Chunks Some investigators have claimed that performance in artificial grammar experiments can be captured with various types of knowledge about string fragments or "chunks@ (e.g., Redington & Chater, 1996). Usually, this knowledge is assumed to be explicit, whether the emphasis is on simple knowledge of which letters tend to pair together (bigrams) (Perruchet & Pacteau, 1990), positional knowledge of fragments within the string (Dienes, Broadbent & Berry, 1991; Gomez & Schvaneveldt, 1994; Mathews, 1990; Mathews et al., 1989) or conscious rules about which fragments are or are not permissible (Dulany, Carlson & Dewey, 1984). For example, Perruchet and Pacteau (1990) demonstrated that participants using simple, explicit knowledge of bigrams that occurred frequently in the training strings performed in a manner virtually indistinguishable from participants doing the classification task in artificial grammar experiments. Dissociations in Grammar Learning 4 Bigram and trigram frequency strength models use these fragments more implicitly. For example, Servan-Schreiber and Anderson (1990) formulated a "competitive chunking model" in which bi and trigrams, occurring within strings in the training phase, compete for associative strength. Processing the letter strings in the experiment involves a process of forming and utilizing chunks. The authors assumed that the letters of the strings form chunks during memorization and the size of the chunks that are formed is determined by how frequently certain letter combinations occur. If large chunks (formed during training) are used for subsequently processing strings at transfer, then higher familiarity values result. Transfer strings with high familiarity are more likely than those with low familiarity to be rated grammatical. Because of the structure inherent in the grammatical strings, there is an overlap of the constituent fragments between the exemplars. This means that, after memorization, the chunks of grammatical strings are larger. Consequently, grammatical strings are more familiar and also more likely to be rated grammatical. Associative strength, however, is assumed to increment implicitly, which differentiates this model from the viewpoints of Dulany et al. (1984) and from Perruchet and