Statistical Learning of Nonadjacencies Predicts On-line Processing of Long-Distance Dependencies in Natural Language

Statistical Learning of Nonadjacencies Predicts On-line Processing of Long-Distance Dependencies in Natural Language Jennifer B. Misyak (jbm36@cornell.edu) and Morten H. Christiansen (christiansen@cornell.edu) Department of Psychology, Cornell University, Ithaca, NY 14853 USA J. Bruce Tomblin (j-tomblin@uiowa.edu) Department of Communication Sciences and Disorders, University of Iowa, Iowa City, IA 52242 USA Abstract noisy finite-state grammar within a visual SRT task to study the encoding of contingencies varying in temporal distance; and of Hunt and Aslin (2001), who employed a visual SRT paradigm for examining learners’ processing of sequential transitions varying in conditional and joint probabilities. Howard, Howard, Dennis and Kelly (2008) also adapted the visual SRT to manipulate the types of statistics governing triplet structures; and Remillard (2008) controlled nth-order adjacent and nonadjacent conditional information to probe SRT learning for visuospatial targets. Across these studies, participants evinced complex, procedural knowledge of the sequence-embedded relations upon extensive training over 20, 48, 6 or 4 sessions, respectively, spanning separate days. Reaction time measures throughout exposure enabled insights into the processing of the structural dependencies. In similar vein, we introduce a new paradigm that directly instantiates an artificial language within an adapted SRT task. Distinct from the aforementioned work, the paradigm specifically endeavors to capture the continuous timecourse of statistical processing, rather than contrasting/altering the forms of statistical information. The paradigm is designed for the briefer exposure periods prototypic of many AGL studies and flexibly accommodates the use of linguistic stimuli-tokens and auditory cues. More generally, the task shares similarities to standard AGL designs in the language- like nature of string-sequences, the smaller number of training exemplars, and the greater transparency to natural language structure. Crucially however, it uses the dependent variable of reaction times and a modified two-choice SRT layout to indirectly assess learning while focusing attention through a cover task. By coupling strengths intrinsic to AGL and SRT methods respectively, the ‘AGL-SRT paradigm’ is intended to complement existing approaches in SL research. Understanding how learners process nonadjacent relations constitutes an ongoing area of SL work, with importance for theories implicating SL in language acquisition/processing. Natural language abounds with long-distance dependencies that proficient learners must track on-line (e.g., as in subject-verb agreement, clausal embeddings, and relationships between auxiliaries and inflected morphemes). Even with the growing bulk of SL work aiming to address the acquisition of nonadjacencies (e.g., Gomez, 2002; Newport & Aslin, 2004; Onnis, Christiansen, Chater & Gomez, 2003; Pacton & Perruchet, 2008; inter alia), it is yet unknown exactly how such learning unfolds, the precise mechanisms subserving it, and the degree to which SL of Statistical learning (SL) research aims to clarify the potential role that associative-based learning mechanisms may play in language. Understanding learners’ processing of nonadjacent statistical structure is vital to this enterprise, since language requires the rapid tracking and integration of long-distance dependencies. This paper builds upon existing nonadjacent SL work by introducing a novel paradigm for studying SL on- line. By capturing the temporal dynamics of the learning process, the new paradigm affords insights into the time course of learning and the nature of individual differences. Across 3 interrelated experiments, the paradigm and results thereof are used to bridge knowledge of the empirical relation between SL and language within the context of nonadjacency learning. Experiment 1 therefore charts the micro-level trajectory of nonadjacency learning and provides an index of individual differences in the new task. Substantial differences are further shown to predict participants’ sentence processing of complex, long-distance natural dependencies in Experiment 2. SRN simulations in Experiment 3 then closely capture key patterns of human nonadjacency processing, attesting to the efficacy of associative-based learning mechanisms that appear foundational to performance in the new, language-linked task. Keywords: Nonadjacent Dependencies; Sentence Processing; Serial Reaction Time Task; Simple Recurrent Network (SRN) Introduction Statistical learning is an inextricably temporal phenomenon, involving the encoding of sequential regularities unfolding over time and space, and the simultaneous shaping of distributional knowledge through ongoing learning experience. Within the past decade, statistical learning (SL) has especially emerged as a key proposed mechanism for acquiring probabilistic dependencies inherent in the time- dependent signal of the speech stream (for reviews, see Gomez & Gerken, 2000; Saffran, 2003). While traditional artificial grammar learning (AGL; Reber, 1967) tasks have been fruitfully deployed towards studying SL, they fail to provide a clear window onto the temporal dynamics of the learning process. In contrast, serial reaction time (SRT; Nissen & Bullemer, 1987) tasks widely used in sequence-learning research trace individuals’ trial-by-trial progress, yet aim to investigate learning for primarily repeating structure. Rarely have methodological advantages of each paradigm been jointly subsumed under a single task for exploring properties of SL. Nonetheless, notable exceptions include the work of Cleeremans and McClelland (1991), who implemented a