Modeling Implicit and Explicit Processes in Recursive Sequence Structure Learning

Modeling Implicit and Explicit Processes in Recursive Sequence Structure Learning Jamie D. Alexandre (jdalexan@ucsd.edu) Department of Cognitive Science, 9500 Gilman Drive La Jolla, CA 92093 USA Keywords: artificial grammar learning; syntax; recursion; serial reaction time task; simple recurrent network; context- free grammars; implicit/explicit processes. the class of regular grammars, which doesn’t shed light onto the acquisition or processing of context-free or recursive structure. The goal of the present study is to obtain estimates of a subject’s online string continuation expectancies while responding to sequences generated by a context-free grammar (palindromes), so that these may be compared with the predictions made by a variety of language models trained on the same input history as the subject. The traditional measures of successful acquisition in artificial grammar experiments – such as grammaticality judgments or recall error rates – are not able to provide the incremental (symbol- by-symbol) expectancy data that we require. We adapt a paradigm first employed by Cleeremans & McClelland (1991), known as a serial reaction time task, in which subjects respond to a sequences of stimuli (with a button mapped onto each stimulus class) by pressing the corresponding button as quickly as possible after perceiving stimulus onset. The resulting reaction times are then correlated with the probabilities generated by the competing computational models. Introduction Surprisal Abstract Recursive structure is viewed as a central property of human language, yet the mechanisms that underlie the acquisition and processing of this structure are subject to intense debate. The artificial grammar learning paradigm has shed light onto syntax acquisition, but has rarely been applied to the more complex, context-free grammars that are needed to represent recursive structure. We adapt the artificial grammar serial reaction time task to study the online acquisition of recursion, and compare human performance to the predictions made by a number of computational language models, chosen to reflect multiple levels and types of syntactic complexity (n-grams, hidden markov models, simple recurrent networks, and Bayesian-induced probabilistic context-free grammars). Evidence is found for a dissociation between explicit and implicit mechanisms of sequence processing, with the SRN more highly correlated with implicit performance, and the PCFG more correlated with explicit awareness of the sequential structure. The nature of linguistic structure, and the computational mechanisms by which humans comprehend it, have long been subject to heated debate. Recursion – the ability to hierarchically embed elements within instances of themselves – has been a central point of contention. Although the recursive structure of language was not a new idea at the time, Chomsky formalized the notion of syntactic recursion, touting it as the fundamental property that allows for human linguistic ability, a thesis he continues to popularize today (Chomsky, 1956; Hauser, Chomsky, & Fitch, 2002). In the Chomskyan tradition, the human syntactic system implements a set of rules that allow for theoretically unbounded levels of recursive embedding (“competence”), but this system is then subject to processing constraints, such as working memory limitations, that explain our limited ability to process recursive structures beyond a few levels of embedding (“performance”). Other theorists, particularly from the connectionist camp, have attempted to explain the (limited) human ability to process recursive structure without hypothesizing unbounded competence, by modeling syntactic processing in systems that do not make use of rules or explicit representations (e.g. Elman, 1990; Pollack, 1990; Christiansen & Chater, 1999). The artificial grammar learning paradigm (initiated by Reber, 1967) has been used to examine processes of syntactic acquisition, but this has been largely restricted to Surprisal, or self-information, is a notion from information theory that quantifies the amount of novel information that a particular event carries with it. An event’s surprisal is defined as its negative log probability: –log( P(x | context) ) The concept of surprisal has been used in psycholinguistics as a potential measure of incremental processing difficulty, and is thus expected to correlate with behavioral measures such as reading times in eye-tracking studies, and response times in self-paced reading studies (Hale, 2001; Levy, 2008). The surprisal model requires that we adopt some measure of the probability of a word’s occurrence given the preceding sentential context. Hale (2001) uses a probabilistic Earley parsing algorithm to generate incremental word probabilities, using the resulting surprisal values to explain the garden path effect. Levy (2008) uses a similar model to explain a wide- range of effects found in the psycholinguistic literature, such as predictability (e.g. effect of Cloze probability), locality effects (e.g. preference for local dependencies), competition/dynamical models (e.g. greater ease in highly constrained contexts), the tuning hypothesis (e.g. effect of structural frequency), and connectionist models (e.g. predictions made by an SRN). The case of the SRN is particularly interesting, because there are significant divergences between the predictions made by an SRN and a