Informativity, topicality, and speech cost: comparing models of speakers’ choices of referring expressions
Is use of a pronoun motivated by topicality or efficiency?
This study formalizes and compares two major hypotheses in speakers’ choices of referring expressions: the topicality model that chooses a form based on the topicality of the referent, and the rational model that chooses a form based on the informativity of the form and its speech cost. Simulations suggest that both the topicality of the referent and the informativity of the word are important to consider in speakers’ choices of reference forms, while a speech cost metric that prefers shorter forms may not be.
Social inference may guide early lexical learning
Assessment of knowledgeability and group membership influences infant word learning.
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.
Japanese children's knowledge of the locality of "zibun" and "kare"
Initial errors in the acquisition of the Japanese local- or long-distance anaphor "zibun."
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.
Modeling the learning of the Person Case Constraint
Adam Liter and Naomi Feldman show that the Person Case Constraint can be learned on the basis of significantly less data, if the constraint is represented in terms of feature bundles.
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.
A unified account of categorical effects in phonetic perception
A statistical model that explains both the strong categorical effects in perception of consonants, and the very weak effects in perception of vowels.
Infant-directed speech is consistent with teaching
Why do we speak differently to infants than to adults? To help answer this question, Naomi Feldman offers a formal theory of phonetic teaching and learning.
Why discourse affects speakers' choice of referring expressions
A probalistic model of the choice between using a pronoun or some other referring expression.
A role for the developing lexicon in phonetic category acquisition
Bayesian models and artificial language learning tasks show that infant acquiosition of phonetic categories can be helpfully constrained by feedback from word segmentation.
Word-level information influences phonetic learning in adults and infants
How do infants learn the phonetic categories of their language? The words they occur can provide a useful cue, shows Naomi Feldman.
The influence of categories on perception: Explaining the perceptual magnet effect as optimal statistical inference
Naomi Feldman develops a Bayesian account of the perceptual magnet effect.
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.