On-line Measures of Prediction in a Self-Paced Statistical Learning Task

On-line Measures of Prediction in a Self-Paced Statistical Learning Task Elisabeth A. Karuza 1 (ekaruza@bcs.rochester.edu), Thomas A. Farmer 2 (thomas-farmer@uiowa.edu), Alex B. Fine 3 (abfine@illinois.edu), Francis X. Smith 2 (francis-smith@uiowa.edu), T. Florian Jaeger 1 (fjaeger@bcs.rochester.edu) Department of Brain and Cognitive Sciences, University of Rochester, Rochester, NY 14627 Department of Psychology, University of Iowa, Iowa City, IA 52242 Department of Psychology, University of Illinois at Urbana-Champaign, Champaign, IL 61820 Abstract speech on the basis of probabilities between non-adjacent consonants (or vowels), even when the probabilities associated with adjacent syllables were uninformative. Additionally, sensitivity to non-local statistical patterns has been found to induce knowledge of phrase-level grammatical relationships. Gomez (2002) has argued that the contrast between variant and invariant elements in linguistic input leads to the acquisition of non-local associations. In English, the present progressive can be formed by combining the auxiliary verb “is” and a main verb marked with inflectional suffix “ing”. Thus, “is” and “ing” have a high joint probability (e.g., is eating, is sleeping, is walking, etc.), whereas the intervening main verbs vary widely. In this vein, Gomez created an artificial grammar of the form A-X-B in which pseudowords in the final position (B) were perfectly predictable given pseudowords in the initial position (A), but X items were drawn from a large set of possible elements. She demonstrated that both infants and adults acquired the non- adjacent dependencies between A and B after a period of passive auditory exposure. On the basis of these results, Gomez suggested that the presence of variability affords the ability to detect long-distance dependencies, bolstering claims that statistical learning is one plausible mechanism of grammar induction. Employing an adaptation of Gomez’s A-X-B grammar displayed in the visual modality, the present study capitalizes on a well-established behavioral metric of implicit learning, motor response time, to investigate how the allocation of processing resources during remote dependency learning changes progressively over exposure to patterned input. Importantly, nearly all studies of statistical learning assess acquisition with one off-line post- exposure test (but see, e.g., Karuza et al., 2013). As a result, we are only beginning to uncover the temporal nature of the process by which the naive subject experiences a patterned world and derives knowledge of its underlying structure 1 . As lifelong statistical learners, humans are remarkably sensitive to the unfolding of elements and events in their surroundings. In the present work, we examined the time- course of non-local dependency learning using a self-paced moving window display. We exposed participants to an artificial grammar of shape sequences and extracted processing times, or how long they viewed each shape, over the course of the experiment. On-line learning was quantified as the growing difference in viewing duration between predictable and predictive items. In other words, as participants learned, they processed predictable items increasingly faster. Our results indicate that participants who make implicit predictions as they learn, and have their expectations met, achieve higher learning outcomes on an off- line post-test. Potential links between these findings, obtained with novel stimuli in an experimental context, and the role of prediction in natural language comprehension are considered. Keywords: Statistical Learning; Adaptation; Prediction; Domain-General Processes; On-line Measures Introduction In order to interact effectively with our environment, it is necessary to acquire and adapt internal representations of its structure. This process is driven at least in part by the implicit extraction of statistical patterns. A substantial literature on “statistical learning” has shown empirically that learners tap into task-relevant regularities in order to segment words from continuous speech or uncover spatio- temporal relationships in visual arrays (Saffran, Aslin, & Newport, 1996; Kirkham, Slemmer, Richardson, & Johnson, 2007, respectively). Studies of statistical learning commonly involve manipulation of a specific type of regularity: the conditional probabilities between adjacent elements. For example, in a word segmentation task, a high conditional probability between neighboring syllables might suggest that those syllables form a coherent chunk (i.e., a word). In the natural world, there are, of course, a number of inter-related regularities that learners exploit in the process of extracting structure. For one, relationships exist not only between adjacent items, but also between items that are not in direct proximity. In the case of Semitic languages, for example, many words are formed from triliteral roots in which vowels vary within fixed consonant frames. Implementing this pattern in an artificial context, Newport and Aslin (2004) demonstrated that participants could segment words from Serial reaction time has long been used as an on-line measure of deterministic sequence learning. In these tasks, subjects typically respond with different fingers (i.e., motor responses) to different locations based on a visual cue that denotes the appropriate response to execute (Nissen & Bullemer, 1987). These tasks are sometimes argued to index learned associations between motor responses. Relative to standard SRT tasks, however, the present paradigm makes use of a single motor response (repeatedly