Since the experiments of Saffran et al. (1996a), there has been a great deal of interest in the question of how statistical regularities in the speech stream might be used by infants to begin to identify individual words. In this work, we use computational modeling to explore the effects of different assumptions the learner might make regarding the nature of words – in particular, how these assumptions affect the kinds of words that are segmented from a corpus of transcribed child-directed speech. We develop several models within a Bayesian ideal observer framework, and use them to examine the consequences of assuming either that words are independent units, or units that help to predict other units. We show through empirical and theoretical results that the assumption of independence causes the learner to undersegment the corpus, with many two- and three-word sequences (e.g. what’s that, do you, in the house) misidentified as individual words. In contrast, when the learner assumes that words are predictive, the resulting segmentation is far more accurate. These results indicate that taking context into account is important for a statistical word segmentation strategy to be successful, and raise the possibility that even young infants may be able to exploit more subtle statistical patterns than have usually been considered. 1

Recent studies of visual statistical learning (VSL) have demonstrated that statistical regularities in sequences of visual stimuli can be automatically extracted, even without intent or awareness. Despite much work on this topic, however, several fundamental questions remain about the nature of VSL. In particular, previous experiments have not explored the underlying units over which VSL operates. In a sequence of colored shapes, for example, does VSL operate over each feature dimension independently, or over multidimensional objects in which color and shape are bound together? The studies reported here demonstrate that VSL can be both object-based and feature-based, in systematic ways based on how different feature dimensions covary. For example, when each shape covaried perfectly with a particular color, VSL was object-based: Observers expressed robust VSL for colored-shape sub-sequences at test but failed when the test items consisted of monochromatic shapes or color patches. When shape and color pairs were partially decoupled during learning, however, VSL operated over features: Observers expressed robust VSL when the feature dimensions were tested separately. These results suggest that VSL is object-based, but that sensitivity to feature correlations in multidimensional sequences (possibly another form of VSL) may in turn help define what counts as an object.

The authors investigated how human adults encode and remember parts of multielement scenes composed of recursively embedded visual shape combinations. The authors found that shape combinations that are parts of larger configurations are less well remembered than shape combinations of the same kind that are not embedded. Combined with basic mechanisms of statistical learning, this embeddedness constraint enables the development of complex new features for acquiring internal representations efficiently without being computationally intractable. The resulting representations also encode parts and wholes by chunking the visual input into components according to the statistical coherence of their constituents. These results suggest that a bootstrapping approach of constrained statistical learning offers a unified framework for investigating the formation of different internal representations in pattern and scene perception.

What mechanisms support the ability of human infants, adults, and other primates to identify words from fluent speech using distributional regularities? In order to better characterize this ability, we collected data from adults in an artificial language segmentation task similar to Saffran, Newport, and Aslin (1996) in which the length of sentences was systematically varied between groups of participants. We then compared the fit of a variety of computational models— including simple statistical models of transitional probability and mutual information, a clustering model based on mutual information by Swingley (2005), PARSER (Perruchet & Vintner, 1998), and a Bayesian model. We found that while all models were able to successfully complete the task, fit to the human data varied considerably, with the Bayesian model achieving the highest correlation with our results.

ABSTRACT—Sequences of speech sounds play a central role in human cognitive life, and the principles that govern such sequences are crucial in determining the syntax and semantics of natural languages. Infants are capable of extracting both simple transitional probabilities and simple algebraic rules from sequences of speech, as demonstrated by studies using ABB grammars (la ta ta, gai mu mu, etc.). Here, we report a striking finding: Infants are better able to extract rules from sequences of nonspeech— such as sequences of musical tones, animal sounds, or varying timbres—if they first hear those rules instantiated in sequences of speech. A hallmark of human language is its abstract character; learners do not simply memorize particular sentences, but rather learn generalizable rules that govern the sequencing of linguistic elements, both familiar and unfamiliar. Proceeding from recent observations that infants are able to extract transitional probabilities from both speech sequences (Saffran, Aslin, & Newport, 1996) and nonspeech sequences (e.g., musical tones: Saffran,

This paper describes a computational model of word segmentation and presents simulation results on realistic acquisition In particular, we explore the capacity and limitations of statistical learning mechanisms that have recently gained prominence in cognitive psychology and linguistics.

One of the first steps in acquiring a morphology system is discovering which phonetic strings correspond to morphemes. These phonetic strings can then be further analyzed in order to determine

Visual categorization of objects has captured the attention of the vision community for decades [10]. The increased popularity of the problem witnessed in the recent years and the advent of powerful computer hardware have led to a seeming success of categorization approaches on the standard datasets such as

While statistical learning (SL) and language acquisition have been perceived as intertwined, such a view must contend with theoretical and empirical challenges. Against the backdrop of criticism leveled at early associationist efforts to account for language, a key concern for current SL approaches is whether it may suffice to enable the detection of long-distance relationships akin to those ubiquitously abounding in natural language. In Experiment 1, we extend results from previous work on the learning of nonadjacent dependencies to the learning of long-distance relations spanning three intervening elements; such learning is shown to obtain under two separate contexts. In Experiment 2, we additionally test the strength of SL and language's proposed relatedness by documenting the nature of correlations in individual differences between the two. Both experiments support the thesis that SL may overlap with mechanisms for language, while raising questions as to the singularity or duality of such underlying mechanism(s).

Disentangling bottom-up and top-down processing in adult category learning is notoriously difficult. Studying category learning in infancy provides a simple way of exploring category learning while minimizing the contribution of top-down information. Three- to 4-month-old infants presented with cat or dog images will form a perceptual category representation for cat that excludes dogs and for dog that includes cats. The authors argue that an inclusion relationship in the distribution of features in the images explains the asymmetry. Using computational modeling and behavioral testing, the authors show that the asymmetry can be reversed or removed by using stimulus images that reverse or remove the inclusion relationship. The findings suggest that categorization of nonhuman animal images by young infants is essentially a bottom-up process. Few in cognitive science would dispute the argument that both bottom-up (i.e., perceptually driven) and top-down (i.e., conceptually driven) processes are involved in adult categorization. Numerous studies have discussed the relationship between these two mechanisms of categorization (e.g., French, 1995; Murphy & Kaplan, 2000; Schyns, Goldstone, & Thibaut, 1998). However, in adults, perceptual and conceptual processes are deeply intertwined, making them difficult to isolate and study independently (Goldstone & Barsalou, 1998).