A Connectionist Single-Mechanism Account of Rule-Like Behavior in Infancy

A Connectionist Single-Mechanism Account of Rule-Like Behavior in Infancy Morten H. Christiansen (morten@siu.edu) Christopher M. Conway (conway@siu.edu) Department of Psychology; Southern Illinois University Carbondale, IL 62901-6502 USA Suzanne Curtin (curtin@gizmo.usc.edu) Department of Linguistics; University of Southern California Los Angeles, CA 90089-1693 USA Abstract One of the most controversial issues in cognitive science per- tains to whether rules are necessary to explain complex be- havior. Nowhere has the debate over rules been more heated than within the field of language acquisition. Most researchers agree on the need for statistical learning mechanisms in lan- guage acquisition, but disagree on whether rule-learning com- ponents are also needed. Marcus, Vijayan, Rao, & Vishton (1999) have provided evidence of rule-like behavior which they claim can only be explained by a dual-mechanism ac- count. In this paper, we show that a connectionist single- mechanism approach provides a more parsimonious account of rule-like behavior in infancy than the dual-mechanism ap- proach. Specifically, we present simulation results from an ex- isting connectionist model of infant speech segmentation, fit- ting the behavioral data under naturalistic circumstances with- out invoking rules. We further investigate diverging predic- tions from the single- and dual-mechanism accounts through additional simulations and artificial language learning experi- ments. The results support a connectionist single-mechanism account, while undermining the dual-mechanism account. Introduction The nature of the learning mechanisms that infants bring to the task of language acquisition is a major focus of research in cognitive science. With the rise of connectionism, much of the scientific debate surrounding this research has focused on whether rules are necessary to explain language acquisition. All parties in the debate acknowledge that statistical learning mechanisms form a necessary part of the language acquisition process (e.g., Christiansen & Curtin, 1999; Marcus, Vijayan, Rao, & Vishton, 1999; Pinker, 1991). However, there is much disagreement over whether a statistical learning mech- anism is sufficient to account for complex rule-like behavior, or whether additional rule-learning mechanisms are needed. In the past this debate has primarily taken place within spe- cific areas of language acquisition, such as inflectional mor- phology (e.g., Pinker, 1991; Plunkett & Marchman, 1993) and visual word recognition (e.g., Coltheart, Curtis, Atkins & Haller, 1993; Seidenberg & McClelland, 1989). More re- cently, Marcus et al. (1999) have presented results from ex- periments with 7-month-olds, apparently showing that infants acquire abstract algebraic rules after two minutes of expo- sure to habituation stimuli. The algebraic rules are construed as representing an open-ended relationship between variables for which one can substitute arbitrary values, “such as ‘the first item X is the same as the third item Y,’ or more gener- ally, that ‘item I is the same as item J”’ (Marcus et al., 1999, p. 79). Marcus et al. further claim that a connectionist single- mechanism approach based on statistical learning is unable to fit their experimental data. In this paper, we build on earlier work (Christiansen & Curtin, 1999) and present a detailed connectionist model of these infant data, and provide new experimental data that support a statistically-based single- mechanism approach while undermining the dual-mechanism account. In the remainder of this paper, we first show that knowl- edge acquired in the service of learning to segment the speech stream can be recruited to carry out the kind of classification task used in the experiments by Marcus et al. For this pur- pose we took an existing model of early infant speech seg- mentation (Christiansen, Allen & Seidenberg, 1998) and used it to simulate the results obtained by Marcus et al. The simu- lations demonstrate that no rules are needed to account for the data; rather, statistical knowledge related to word seg- mentation can explain the rule-like behavior of the infants in the Marcus et al. study. We then explore the issue of timing in stimuli presentation and present additional simu- lations from which empirical predictions are derived that di- verge from those of the rule-based account. These predictions are tested in experiments with adults. Experiment 1 replicated the results from Marcus et al. using adult subjects. Experi- ment 2 confirmed the predictions from our single-mechanism approach, whereas the dual-mechanism approach cannot ac- count for these results without adding extra machinery to complement the statistical and rule-based components. To- gether, the simulations and the experiments thus suggest that a single-mechanism model provides the most parsimonious account of the empirical data presented here and in Marcus et al., thus obviating the need for a separate rule-based compo- nent. Simulation 1: Rule-Like Behavior without Rules Marcus et al. (1999) used an artificial language learning paradigm to test their claim that the infant has two mecha- nisms for learning language, one that uses statistical informa- tion and another which uses algebraic rules. They conducted three experiments which tested infants’ ability to generalize to items not presented in the familiarization phase of the ex- periment. We focus here on their third experiment because it was controlled for possible confounds found in the first two experiments: differences in phonetic features (Experiment 1) and reduplication 1 (Experiment 2). Marcus et al. claim that Though the control for reduplication was not entirely complete (see Elman, 1999).