Naomi Feldman

We incorporate social reasoning about groups of informants into a model of word learning, and show that the model accounts for infant looking behavior in tasks of both word learning and recognition. Simulation 1 models an experiment where 16-month-old infants saw familiar objects labeled either correctly or incorrectly, by either adults or audio talkers. Simulation 2 reinterprets puzzling data from the Switch task, an audiovisual habituation procedure wherein infants are tested on familiarized associations between novel objects and labels. Eight-month-olds outperform 14-month-olds on the Switch task when required to distinguish labels that are minimal pairs (e.g., “buk” and “puk”), but 14-month-olds' performance is improved by habituation stimuli featuring multiple talkers. Our modeling results support the hypothesis that beliefs about knowledgeability and group membership guide infant looking behavior in both tasks. These results show that social and linguistic development interact in non-trivial ways, and that social categorization findings in developmental psychology could have substantial implications for understanding linguistic development in realistic settings where talkers vary according to observable features correlated with social groupings, including linguistic, ethnic, and gendered groups.

Although the Japanese reflexive zibun can be bound both locally and across clause boundaries, the third-person pronoun kare cannot take a local antecedent. These are properties that children need to learn about their language, but we show that the direct evidence of the binding possibilities of zibun is sparse and the evidence of kare is absent in speech to children, leading us to ask about children’s knowledge. We show that children, unlike adults, incorrectly reject the long-distance antecedent for zibun, and while being able to access this antecedent for a non-local pronoun kare, they consistently reject the local antecedent for this pronoun. These results suggest that children’s lack of matrix readings for zibun is not due to their understanding of discourse context but the properties of their language understanding.

Many domains of linguistic research posit feature bundles as an explanation for various phenomena. Such hypotheses are often evaluated on their simplicity (or parsimony). We take a complementary approach. Specifically, we evaluate different hypotheses about the representation of person features in syntax on the basis of their implications for learning the Person Case Constraint (PCC). The PCC refers to a phenomenon where certain combinations of clitics (pronominal bound morphemes) are disallowed with ditransitive verbs. We compare a simple theory of the PCC, where person features are represented as atomic units, to a feature-based theory of the PCC, where person features are represented as feature bundles. We use Bayesian modeling to compare these theories, using data based on realistic proportions of clitic combinations from child-directed speech. We find that both theories can learn the target grammar given enough data, but that the feature-based theory requires significantly less data, suggesting that developmental trajectories could provide insight into syntactic representations in this domain.

Categorical effects are found across speech sound categories, with the degree of these effects ranging from extremely strong categorical perception in consonants to nearly continuous perception in vowels. We show that both strong and weak categorical effects can be captured by a unified model. We treat speech perception as a statistical inference problem, assuming that listeners use their knowledge of categories as well as the acoustics of the signal to infer the intended productions of the speaker. Simulations show that the model provides close fits to empirical data, unifying past findings of categorical effects in consonants and vowels and capturing differences in the degree of categorical effects through a single parameter.

Infant-directed speech (IDS) has distinctive properties that differ from adult-directed speech (ADS). Why it has these properties -- and whether they are intended to facilitate language learning -- is matter of contention. We argue that much of this disagreement stems from lack of a formal, guiding theory of how phonetic categories should best be taught to infant-like learners. In the absence of such a theory, researchers have relied on intuitions about learning to guide the argument. We use a formal theory of teaching, validated through experiments in other domains, as the basis for a detailed analysis of whether IDS is well-designed for teaching phonetic categories. Using the theory, we generate ideal data for teaching phonetic categories in English. We qualitatively compare the simulated teaching data with human IDS, finding that the teaching data exhibit many features of IDS, including some that have been taken as evidence IDS is not for teaching. The simulated data reveal potential pitfalls for experimentalists exploring the role of IDS in language learning. Focusing on different formants and phoneme sets leads to different conclusions, and the benefit of the teaching data to learners is not apparent until a sufficient number of examples have been provided. Finally, we investigate transfer of IDS to learning ADS. The teaching data improves classification of ADS data, but only for the learner they were generated to teach; not universally across all classes of learner. This research offers a theoretically-grounded framework that empowers experimentalists to systematically evaluate whether IDS is for teaching.

We propose a language production model that uses dynamic discourse information to account for speakers' choices of referring expressions. Our model extends previous rational speech act models (Frank and Goodman, 2012) to more naturally distributed linguistic data, instead of assuming a controlled experimental setting. Simulations show a close match between speakers' utterances and model predictions, indicating that speakers' behavior can be modeled in a principled way by considering the probabilities of referents in the discourse and the information conveyed by each word.

Infants segment words from fluent speech during the same period when they are learning phonetic categories, yet accounts of phonetic category acquisition typically ignore information about the words in which sounds appear. We use a Bayesian model to illustrate how feedback from segmented words might constrain phonetic category learning by providing information about which sounds occur together in words. Simulations demonstrate that word-level information can successfully disambiguate overlapping English vowel categories. Learning patterns in the model are shown to parallel human behavior from artificial language learning tasks. These findings point to a central role for the developing lexicon in phonetic category acquisition and provide a framework for incorporating top-down constraints into models of category learning.

Infants begin to segment words from fluent speech during the same time period that they learn phonetic categories. Segmented words can provide a potentially useful cue for phonetic learning, yet accounts of phonetic category acquisition typically ignore the contexts in which sounds appear. We present two experiments to show that, contrary to the assumption that phonetic learning occurs in isolation, learners are sensitive to the words in which sounds appear and can use this information to constrain their interpretation of phonetic variability. Experiment 1 shows that adults use word-level information in a phonetic category learning task, assigning acoustically similar vowels to different categories more often when those sounds consistently appear in different words. Experiment 2 demonstrates that eight-month-old infants similarly pay attention to word-level information and that this information affects how they treat phonetic contrasts. These findings suggest that phonetic category learning is a rich, interactive process that takes advantage of many different types of cues that are present in the input.

A variety of studies have demonstrated that organizing stimuli into categories can affect the way the stimuli are perceived. We explore the influence of categories on perception through one such phenomenon, the perceptual magnet effect, in which discriminability between vowels is reduced near prototypical vowel sounds. We present a Bayesian model to explain why this reduced discriminability might occur: It arises as a consequence of optimally solving the statistical problem of perception in noise. In the optimal solution to this problem, listeners’ perception is biased toward phonetic category means because they use knowledge of these categories to guide their inferences about speakers’ target productions. Simulations show that model predictions closely correspond to previously published human data, and novel experimental results provide evidence for the predicted link between perceptual warping and noise. The model unifies several previous accounts of the perceptual magnet effect and provides a framework for exploring categorical effects in other domains.