Erik D. Thiessen

Pattern Induction by Infant Language Learners

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 extraction and integration framework: a two-process account of statistical learning.

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.

Effects of Visual Information on Adults’ and Infants’ Auditory Statistical Learning

Infant and adult learners are able to identify word boundaries in fluent speech using statistical information. Similarly, learners are able to use statistical information to identify word-object associations. Successful language learning requires both feats. In this series of experiments, we presented adults and infants with audio-visual input from which it was possible to identify both word boundaries and word-object relations. Adult learners were able to identify both kinds of statistical relations from the same input. Moreover, their learning was actually facilitated by the presence of two simultaneously present relations. Eight-month-old infants, however, do not appear to benefit from the presence of regular relations between words and objects. Adults, like 8-month-olds, did not benefit from regular audio-visual correspondences when they were tested with tones, rather than linguistic input. These differences in learning outcomes across age and input suggest that both developmental and stimulus-based constraints affect statistical learning.

Impaired Statistical Learning in Developmental Dyslexia.

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 if individuals with DD are capable of extracting statistical regularities across sequences of passively experienced speech and nonspeech 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 3-stimulus test items with either high or low statistical coherence as being the most similar to the sounds heard during familiarization. RESULTS As with control participants, the DD group was sensitive to the transitional probability structure of the familiarization materials as evidenced by above-chance performance. However, the performance of participants with DD was significantly poorer than controls across linguistic and nonlinguistic stimuli. In addition, reading-related measures were significantly correlated with statistical learning performance of both speech and nonspeech material. CONCLUSION Results are discussed in light of procedural learning impairments among participants with DD.

Spectral tilt as a cue to word segmentation in infancy and adulthood

Across a variety of tasks, adults respond differently to syllables with multiple stress cues than to syllables with only one cue to stress. This series of experiments was designed to explore how infants and adults use partial stress as a cue to word boundaries. In the first experiment, 9-month-old infants treated syllables with only one cue to stress (spectral tilt) as a strong cue to word boundaries. The second experiment shows that whereas adults treat syllables with multiple cues to stress as word onsets, they do not consider syllables marked only by spectral tilt to be strong indicators of word boundaries. The third experiment shows that 1-year-old infants are more adultlike than 9-month-olds in their use of stress cues. Taken together, these results suggest a rapid development of stress cue knowledge in infancy, perhaps due to infants’ experience with word segmentation.

iMinerva: A Mathematical Model of Distributional Statistical Learning

Statistical learning refers to the ability to identify structure in the input based on its statistical properties. For many linguistic structures, the relevant statistical features are distributional: They are related to the frequency and variability of exemplars in the input. These distributional regularities have been suggested to play a role in many different aspects of language learning, including phonetic categories, using phonemic distinctions in word learning, and discovering non-adjacent relations. On the surface, these different aspects share few commonalities. Despite this, we demonstrate that the same computational framework can account for learning in all of these tasks. These results support two conclusions. The first is that much, and perhaps all, of distributional statistical learning can be explained by the same underlying set of processes. The second is that some aspects of language can be learned due to domain-general characteristics of memory.

Language experience changes subsequent learning

What are the effects of experience on subsequent learning? We explored the effects of language-specific word order knowledge on the acquisition of sequential conditional information. Korean and English adults were engaged in a sequence learning task involving three different sets of stimuli: auditory linguistic (nonsense syllables), visual non-linguistic (nonsense shapes), and auditory non-linguistic (pure tones). The forward and backward probabilities between adjacent elements generated two equally probable and orthogonal perceptual parses of the elements, such that any significant preference at test must be due to either general cognitive biases, or prior language-induced biases. We found that language modulated parsing preferences with the linguistic stimuli only. Intriguingly, these preferences are congruent with the dominant word order patterns of each language, as corroborated by corpus analyses, and are driven by probabilistic preferences. Furthermore, although the Korean individuals had received extensive formal explicit training in English and lived in an English-speaking environment, they exhibited statistical learning biases congruent with their native language. Our findings suggest that mechanisms of statistical sequential learning are implicated in language across the lifespan, and experience with language may affect cognitive processes and later learning.

How the Melody Facilitates the Message and Vice Versa in Infant Learning and Memory

Infants are often presented with input in which there are multiple related regularities, as is the case in musical input with both melodic and lyrical structure. Adult learners often learn more easily from complex input containing multiple correlated regularities than from simplified input. Do infants also capitalize on complexity, or instead do they benefit from simplified input? In this series of experiments, infants were presented with music in which melodic and lyrical structure predicted each other, or in which only one type of regularity was presented in isolation (melodies alone, or lyrics presented with no melody). Infants learned lyrics more easily when they were paired with a melody than when they were presented alone; similarly, they learned melodies more easily when they were paired with lyrics than when they were presented alone. There are several potential mechanisms that could explain how infants’ learning is facilitated by complex input, suggesting important implications for learning in infants’ natural environments.

Dogs, Bogs, Labs, and Lads: What Phonemic Generalizations Indicate About the Nature of Children’s Early Word‐Form Representations

Whereas young children accept words that differ by only a single phoneme as equivalent labels for novel objects, older children do not (J. F. Werker, C. J. Fennell, K. M. Corcoran, & C. L. Stager, 2002). In these experiments, 106 children were exposed to a training regime that has previously been found to facilitate childrens use of phonemic contrasts (E. D. Thiessen, 2007). The results indicate that the effect of this training is limited to contexts that are highly similar to childrens initial experience with the phonemic contrast, suggesting that early word-form representations are not composed of entirely abstract units such as phonemes or features. Instead, these results are consistent with theories suggesting that childrens early word-form representations retain contextual and perceptual features associated with childrens prior experience with words.

Beyond Word Segmentation: A Two- Process Account of Statistical Learning

The term statistical learning was originally used to describe sensitivity to conditional relations between syllables in the context of word segmentation. Subsequent research has demonstrated that infants are sensitive to many other kinds of statistical information. The range of statistical learning phenomena presents a challenge to prior theories and models, which have primarily focused on a single aspect of learning. From our perspective, sensitivity to conditional information yields discrete representations (such as words). Integration across these representations yields sensitivity to distributional information. To achieve sensitivity to both kinds of statistical information, we propose a framework that combines processes that extract units from the input with processes that compare across these extracted items. We review the literature on statistical learning to show how these processes map onto prior research, and we discuss how the interaction between these processes gives rise to more complex patterns of learning than either process achieves in isolation.