Beyond Transitional Probabilities: Human Learners Impose a Parsimony Bias in Statistical Word Segmentation

Beyond Transitional Probabilities: Human Learners Impose a Parsimony Bias in Statistical Word Segmentation Michael C. Frank Harry Tily mcfrank@mit.edu Department of Brain and Cognitive Sciences Massachusetts Institute of Technology hjt@stanford.edu Department of Linguistics Stanford University Inbal Arnon Sharon Goldwater inbal.arnon@manchester.ac. uk Department of Linguistics University of Manchester sgwater@inf.ac. uk School of Informatics University of Edinburgh Abstract of the speech stream, however. Lexicon-based learners like PARSER (Perruchet & Vinter, 1998) and Bayesian lexical models (Brent, 1999; Goldwater, Griffiths, & Johnson, 2009) have also been proposed as possible models of segmentation. Though these models differ on several dimensions, all assume that learners attempt to learn a consistent lexicon—a set of word forms that is combined to form the training sequence— and they do this by preferring small lexicons composed of frequent, short words. Human infants and adults are able to segment coherent se- quences from unsegmented strings of auditory stimuli after only a short exposure, an ability thought to be linked to early language acquisition. Although some research has hypothe- sized that learners succeed in these tasks by computing tran- sitional probabilities between syllables, current experimen- tal results do not differentiate between a range of models of different computations that learners could perform. We cre- ated a set of stimuli that was consistent with two different lexicons—one consisting of two-syllable words and one of three-syllable words—but where transition probabilities would not lead learners to segment sentences consistently according to either lexicon. Participants’ responses formed a distribution over possible segmentations that included consistent segmen- tations into both two- and three-syllable words, suggesting that learners do not use pure transitional probabilities to segment but instead impose a bias towards parsimony on the lexicons they learn. Keywords: Word segmentation; statistical learning; computa- tional modeling. Two previous studies have examined whether this kind of model could provide a good fit to human learning perfor- mance. The first contrasted recognition of sub-parts of the words from a speech stream and found that PARSER, like hu- man learners, failed to discriminate sub-parts of words after training (Giroux & Rey, 2009). The second study found that a parsimony-biased chunk-finding model better accounted for human performance across a range of experiments in the vi- sual domain than a purely associative model (Orb´an, Fiser, Aslin, & Lengyel, 2008). Thus, both of these studies sug- gest that human learners do not simply represent association probabilities in statistical learning. Introduction Human adults, infants, and even members of other species have the ability to identify statistically coherent sequences in unsegmented streams of stimuli after only a very short ex- posure (Saffran, Aslin, & Newport, 1996; Saffran, Newport, & Aslin, 1996; Hauser, Newport, & Aslin, 2001). This seg- mentation ability is extremely robust, operates across a wide range of modalities (Conway & Christiansen, 2005), and has been hypothesized to play an important role in early language acquisition (Kuhl, 2004). Nevertheless, relatively little is known about the computations underlying statistical segmen- tation. In one influential study, Saffran, Newport, and Aslin (1996) exposed participants to a simple artificial language which consisted of six trisyllabic words concatenated to- gether to form a continuous speech steam. After only a few minutes of exposure, participants were able to distinguish words in this language from strings that did not occur with the same frequency. They speculated that participants could succeed by computing syllable-to-syllable transitional proba- bilities (TPs) and segmenting the speech stream at local min- ima in TP. There are many possible computations by which learn- ers could extract coherent units from the statistical structure Our current study asks what kinds of learning biases op- erate in statistical learning. Our study makes use of a novel language whose transition statistics support not just one but a range of possible coherent segmentations: training data could be interpreted as a sequence of sentences of six words from a lexicon of two-syllable words or a sequence of sentences of four words from a lexicon of three-syllable words (where all words appeared with approximately the same frequency). TPs for a single sentence in this language are shown in Fig- ure 1. A learner using pure TPs to segment the language would not recover either lexicon but would instead either segment the language into sets of six-syllable words or else segment inconsistently into a mix of two- and three-syllable words. Thus, our language was designed to test whether hu- man learners would learn more parsimonious lexicons than those implied by pure transition statistics. Experiment 1 validates two methodological innovations: a web-based interface for data collection and a dependent measure which directly evaluates participants’ word segmen- tation judgments. Experiment 2 uses these methods to test