Syntactic classification of acquired structural regularities

Syntactic Classification of Acquired Structural Regularities Christian Forkstam (christian.forkstam@cns.ki.se) Cognitive Neurophysiology Research Group, Karolinska Institutet Karolinska hospital N8, 171 76 Stockholm, Sweden. F.C. Donders Centre for Cognitive Neuroimaging Radboud University Nijmegen, The Netherlands. Karl Magnus Petersson (karl.magnus.petersson@fcdonders.ru.nl) F.C. Donders Centre for Cognitive Neuroimaging Radboud University Nijmegen, The Netherlands. Cognitive Neurophysiology Research Group, Karolinska Institutet Karolinska hospital N8, 171 76 Stockholm, Sweden. CSI, Center for intelligent systems, Universidade do Algarve, Faro, Portugal. from artificial grammars (e.g., Seger, Prabhakaran, Poldrack, & Gabrieli, 2000; Skosnik et al., 2002). For example, Petersson et al. (2004) investigated a grammaticality classification task using an implicit acquisition paradigm without feedback in which the participants were only exposed to positive examples (i.e., well-formed consonant strings) generated by the Reber grammar. The results showed that artificial syntactic violations activated Broca’s region (Brodmann’s area (BA) 44/45). In the current study we tested the validity of this finding in a modified experimental design, using classification strings that were balanced for substring familiarity relative the acquisition string-set, independent of grammatical status; and sequential instead of whole string presentation paradigm for the strings. Abstract In this paper we investigate the neural correlates of syntactic classification of an acquired grammatical sequence structure in an event-related FMRI study. During acquisition, participants were engaged in an implicit short-term memory task without performance feedback. We manipulated the statistical frequency-based and rule-based characteristics of the classification stimuli independently in order to investigate their role in artificial grammar acquisition. The participants performed reliably above chance on the classification task. We observed a partly overlapping corticostriatal processing network activated by both manipulations including inferior prefrontal, cingulate, inferior parietal regions, and the caudate nucleus. More specifically, the left inferior frontal BA 45 and the caudate nucleus were sensitive to syntactic violations and endorsement, respectively. In contrast, these structures were insensitive to the frequency-based manipulation. Implicit statistical learning Keywords: Artificial Grammar; Functional Neuroimaging; FMRI; Inferior Frontal Cortex; Caudate Nucleus. A complementary perspective on AGL views this as a model for investigating implicit learning (Forkstam & Petersson, 2005). Reber (1967) defined implicit learning as the process by which an individual comes to respond appropriately to the statistical structure inherent in the input. Thus, he argued, the capacity for generalization that the participants show in grammaticality classification is based on the implicit acquisition of structural regularities reflected in the input sample. Reber (1967) suggested that humans acquire implicit knowledge of the underlying structure through an inductive statistical learning process and that this knowledge is put to use during classification. Support for the implicit character of AGL comes for example from lesion studies on amnesic patients. Knowlton and Squire (1996) investigated amnesic patients and normal controls on a classical and a transfer version of the AGL task. The patients and their normal controls performed similarly on both AGL tasks while the amnesic patients showed no explicit recollection of whole-item or fragment (i.e., bi- or tri-gram) information. Based on the results from the transfer version they argued that AGL depends on the implicit acquisition of both abstract and exemplar-specific information. Knowlton and Squire (1996) suggested that the latter indicates that distributional information of local sequential regularities is acquired, while the former suggests Introduction Humans possess adaptive mechanisms capable of implicitly extracting structural information solely from observation (Stadler & Frensch, 1998), as indicated by for example artificial grammar learning (AGL). Reber (1967) suggested that humans can learn artificial grammars implicitly by an abstraction process intrinsic to natural language acquisition. Chomsky, following von Humboldt, suggested that natural language is an example of the ‘infinite use of finite means’. The simplest relevant formal model incorporating this idea is represented by the family of right-linear phrase structure grammars, which can be implemented in the finite-state architecture (FSA), are typically used in AGL. It has recently been suggested that AGL is a relevant model for investigating aspects of language learning in infants (Gomez & Gerken, 2000), and second language learning in adults (Friederici, Steinhauer, & Pfeifer, 2002). Recent functional magnetic resonance imaging (FMRI) results indicate that language related brain regions are engaged in artificial grammar processing (Petersson, Forkstam, & Ingvar, 2004) and a number of FMRI studies have investigated implicit learning of material generated