We trained a computational model (the Chunk-Based Learner; CBL) on a longitudinal corpus of child–caregiver interactions in English to test whether one proposed statistical learning mechanism—backward transitional probability—is able to predict children's speech productions with stable accuracy throughout the first few years of development. We predicted that the model less accurately reconstructs children's speech productions as they grow older because children gradually begin to generate speech using abstracted forms rather than specific “chunks” from their speech environment. To test this idea, (...) we trained the model on both recently encountered and cumulative speech input from a longitudinal child language corpus. We then assessed whether the model could accurately reconstruct children's speech. Controlling for utterance length and the presence of duplicate chunks, we found no evidence that the CBL becomes less accurate in its ability to reconstruct children's speech with age. (shrink)

Phonemes have variant pronunciations depending on context. For instance, in American English, the [t] in pat [pæt] and the [d] in pad [pæd] are both realized with a tap [ɾ] when the –ing suffix is attached, [pæɾɪŋ]. We show that despite greater distributional and acoustic support for the [t]-tap alternation, 12-month-olds successfully relate taps to stems with a perceptually-similar final [d], not the dissimilar final-[t]. Thus, distributional learning of phonological alternations is constrained by infants' preference for the alternation of perceptually-similar (...) segments. Further, the ability to relate variant surface forms emerges between 8- and 12-months. Our findings of biased learning provide further empirical support for a role for perceptual similarity in the acquisition of linguistically-relevant categories. We discuss the implications of our findings for phonological theory, language acquisition and models of the mental lexicon. (shrink)

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. (shrink)

Before the end of the first year of life, infants begin to lose the ability to perceive distinctions between sounds that are not phonemic in their native language. It is typically assumed that this developmental change reflects the construction of language-specific phoneme categories, but how these categories are learned largely remains a mystery. Peperkamp, Le Calvez, Nadal, and Dupoux (2006) present an algorithm that can discover phonemes using the distributions of allophones as well as the phonetic properties of the allophones (...) and their contexts. We show that a third type of information source, the occurrence of pairs of minimally differing word forms in speech heard by the infant, is also useful for learning phonemic categories and is in fact more reliable than purely distributional information in data containing a large number of allophones. In our model, learners build an approximation of the lexicon consisting of the high-frequency n-grams present in their speech input, allowing them to take advantage of top-down lexical information without needing to learn words. This may explain how infants have already begun to exhibit sensitivity to phonemic categories before they have a large receptive lexicon. (shrink)

Cognitive Science, Volume 46, Issue 2, February 2022.

To acquire one’s native phonological system, language-specific phonological categories and relationships must be extracted from the input. The acquisition of the categories and relationships has each in its own right been the focus of intense research. However, it is remarkable that research on the acquisition of categories and the relations between them has proceeded, for the most part, independently of one another. We argue that this has led to the implicit view that phonological acquisition is a “two-stage” process: Phonetic categories (...) are first acquired and then subsequently mapped onto abstract phoneme categories. We present simulations that suggest two problems with this view: First, the learner might mistake the phoneme-level categories for phonetic-level categories and thus be unable to learn the relationships between phonetic-level categories; on the other hand, the learner might construct inaccurate phonetic-level representations that prevent it from finding regular relations among them. We suggest an alternative conception of the phonological acquisition problem that sidesteps this apparent inevitability and acquires phonemic categories in a single stage. Using acoustic data from Inuktitut, we show that this model reliably converges on a set of phoneme-level categories and phonetic-level relations among subcategories, without making use of a lexicon. (shrink)

An important topic in the evolution of language is the kinds of grammars that can be computed by humans and other animals. Fitch and Hauser () approached this question by assessing the ability of different species to learn 2 grammars, (AB)n and An Bn. An Bn was taken to indicate a phrase structure grammar, eliciting a center‐embedded pattern. (AB)n indicates a grammar whose strings entail only local relations between the categories of constituents. F&H's data suggest that humans, but not tamarin (...) monkeys, learn an An Bn grammar, whereas both learn a simpler (AB)n grammar (). In their experiments, the A constituents were syllables pronounced by a female voice, whereas the B constituents were syllables pronounced by a male voice. This study proposes that what characterizes the An Bn exemplars is the distributional regularities of the syllables pronounced by either a male or a female rather than the underlying, more abstract patterns. This article replicates F&H's data and reports new controls using either categories similar to those in F&H or less salient ones. This article shows that distributional regularities explain the data better than grammar learning. Indeed, when familiarized with An Bn exemplars, participants failed to discriminate A3 B2 and A2 B3 from An Bn items, missing the crucial feature that the number of As must equal the number of Bs. Therefore, contrary to F&H, this study concludes that no syntactic rules implementing embedded nonadjacent dependencies were learned in these experiments. The difference between human linguistic abilities and the putative precursors in monkeys deserves further exploration. (shrink)

An important distinction between phonology and syntax has been overlooked. All phonological patterns belong to the regular region of the Chomsky Hierarchy, but not all syntactic patterns do. We argue that the hypothesis that humans employ distinct learning mechanisms for phonology and syntax currently offers the best explanation for this difference.