Prior research suggests that stress cues are particularly important for English-hearing infants' detection of word boundaries. It is unclear, though, how infants learn to attend to stress as a cue to word segmentation. This series of experiments was designed to explore infants' attention to conflicting cues at different ages. Experiment 1 replicated previous findings: When stress and statistical cues indicated different word boundaries, 9-month-old infants used syllable stress as a cue to segmentation while ignoring statistical cues. However, in Experiment 2, 7-month-old infants attended more to statistical cues than to stress cues. These results raise the possibility that infants use their statistical learning abilities to locate words in speech and use those words to discover the regular pattern of stress cues in English. Infants at different ages may deploy different segmentation strategies as a function of their current linguistic experience.

There are reasons to believe that infant‐directed (ID) speech may make language acquisition easier for infants. However, the effects of ID speech on infants' learning remain poorly understood. The experiments reported here assess whether ID speech facilitates word segmentation from fluent speech. One group of infants heard a set of nonsense sentences spoken with intonation contours characteristic of adult‐directed (AD) speech, and the other group heard the same sentences spoken with intonation contours characteristic of ID speech. In both cases, the only cue to word boundaries was the statistical structure of the speech. Infants were able to distinguish words from syllable sequences spanning word boundaries after exposure to ID speech but not after hearing AD speech. These results suggest that ID speech facilitates word segmentation and may be useful for other aspects of language acquisition as well. Issues of direction of preference in preferential listening paradigms are also considered.

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How do infants learn the sound patterns of their native language? By the end of the 1st year, infants have acquired detailed aspects of the phonology and phonotactics of their input language. However, the structure of the learning mechanisms underlying this process is largely unknown. In this study, 9-month-old infants were given the opportunity to induce specific phonological patterns in 3 experiments in which syllable structure, consonant voicing position, and segmental position were manipulated. Infants were then familiarized with fluent speech containing words that either fit or violated these patterns. Subsequent testing revealed that infants rapidly extracted new phonological regularities and that this process was constrained such that some regularities were easier to acquire than others.

The term statistical learning in infancy research originally referred to sensitivity to transitional probabilities. Subsequent research has demonstrated that statistical learning contributes to infant development in a wide array of domains. The range of statistical learning phenomena necessitates a broader view of the processes underlying statistical learning. Learners are sensitive to a much wider range of statistical information than the conditional relations indexed by transitional probabilities, including distributional and cue-based statistics. We propose a novel framework that unifies learning about all of these kinds of statistical structure. From our perspective, learning about conditional relations outputs discrete representations (such as words). Integration across these discrete representations yields sensitivity to cues and distributional information. To achieve sensitivity to all of these kinds of statistical structure, our framework combines processes that extract segments of the input with processes that compare across these extracted items. In this framework, the items extracted from the input serve as exemplars in long-term memory. The similarity structure of those exemplars in long-term memory leads to the discovery of cues and categorical structure, which guides subsequent extraction. The extraction and integration framework provides a way to explain sensitivity to both conditional statistical structure (such as transitional probabilities) and distributional statistical structure (such as item frequency and variability), and also a framework for thinking about how these different aspects of statistical learning influence each other.Keywords: statistical learning, language development, implicit learning, word learning Humans live in a world filled with statistical regularities. Balls thrown into the air typically fall back to earth; nouns such as dog or boy are typically preceded by articles such as a or the. There is no doubt that learners are sensitive to these statistical regularities. One term to describe the ability to detect and use statistical structure is statistical learning. Saffran, Aslin, and Newport (1996) proposed this term to describe infants' ability to identify word boundaries solely from the statistical relation between sounds in the input. It is now widely acknowledged that infants and adults encode the statistical structure of their environment in a variety of tasks, including sequence learning (e.g., Haith, Wentworth, & Canfield, 1993;Stadler, 1992), category boundary detection (e.g., Maye, Werker, & Gerken, 2002), word-object association (Smith & Yu, 2008), cue-category association (Thiessen & Saffran, 2007), and causal learning (Sobel & Kirkham, 2007). Statistical learning likely plays a role in many different aspects of development, but it is thought to play an especially crucial role in language development. The discovery that infants are capable of benefiting from statistical structure in the input led to a reevaluation of the role of learning in language acquisition, ...

A B S T R A C TConsiderable research indicates that learners are sensitive to probabilistic structure in laboratory studies of artificial language learning. However, the artificial and simplified nature of the stimuli used in the pioneering work on the acquisition of statistical regularities has raised doubts about the scalability of such learning to the complexity of natural language input. In this review, we explore a central prediction of statistical learning accounts of language acquisition -that sensitivity to statistical structure should be linked to real language processes -via an examination of: (1) recent studies that have increased the ecological validity of the stimuli; (2) studies that suggest statistical segmentation produces representations that share properties with real words; (3) correlations between individual variability in statistical learning ability and individual variability in language outcomes; and (4) atypicalities in statistical learning in clinical populations characterized by language delays or deficits.

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Purpose Developmental dyslexia (DD) is commonly thought to arise from phonological impairments. However, an emerging perspective is that a more general procedural learning deficit, not specific to phonological processing, may underlie DD. The current study examined whether individuals with DD are capable of extracting statistical regularities across sequences of passively-experienced speech and non-speech sounds. Such statistical learning is believed to be domain-general, to draw upon procedural learning systems, and to relate to language outcomes. Method DD and control groups were familiarized with a continuous stream of syllables or sine-wave tones, the ordering of which was defined by high or low transitional probabilities across adjacent stimulus pairs. Participants subsequently judged two three-stimulus test items with either high or low statistical coherence as the most similar to the sounds heard during familiarization. Results Like control participants, the DD group was sensitive to the transitional probability structure of the familiarization materials, as evidenced by above-chance performance. However, DD participants’ performance was significantly poorer than controls across linguistic and non-linguistic stimuli. Additionally, reading-related measures were significantly correlated with statistical learning performance of both speech and non-speech material. Conclusions Results are discussed in light of procedural learning impairments among participants with DD.