Listeners use descriptive contrast to disambiguate novel referents and make inferences about novel categories

In the face of unfamiliar language or objects, description is one cue people can use to learn about both. Beyond narrowing potential referents to those that match a descriptor, listeners could infer that a described object is one that contrasts with other relevant objects of the same type (e.g., “The tall cup” contrasts with another, shorter cup). This contrast may be in relation to other objects present in the environment or to the referent’s category. In two experiments, we investigate whether listeners use descriptive contrast to resolve reference and make inferences about novel referents’ categories. While participants use size adjectives contrastively to guide novel referent choice, they do not reliably do so using color adjectives (Experiment 1). Their contrastive inferences go beyond the current referential context: participants use description to infer that a novel object is atypical of its category (Experiment 2). Overall, people are able to use descriptive contrast to resolve reference and make inferences about a novel object’s category, allowing them to infer new word meanings and learn about new categories’ feature distributions

From "um" to "yeah": Producing, predicting, and regulating information flow in human conversation

Conversation demands attention. Speakers must call words to mind, listeners must make sense of them, and both together must negotiate this flow of information, all in fractions of a second. We used large language models to study how this works in a large-scale dataset of English-language conversation, the CANDOR corpus. We provide a new estimate of the information density of unstructured conversation, of approximately 13 bits/second, and find significant effects associated with the cognitive load of both retrieving, and presenting, that information. We also reveal a role for backchannels -- the brief yeahs, uh-huhs, and mhmms that listeners provide -- in regulating the production of novelty: the lead-up to a backchannel is associated with declining information rate, while speech downstream rebounds to previous rates. Our results provide new insights into long-standing theories of how we respond to fluctuating demands on cognitive resources, and how we negotiate those demands in partnership with others.Comment: 18 pages, 4 figures, comments welcom

Remarkable Features: Using Descriptive Contrast to Express and Infer Typicality

We mention what is remarkable while letting the unremarkable go unsaid. Thus, while language can tell us a lot about the world, it does not veridically reflect the world: people are more likely to talk about atypical features (e.g., "purple carrot") than typical features (e.g., "[orange] carrot"). In this dissertation, I characterize how people selectively describe the features of things and examine the implications of this selective description for how children and adults learn from language. In Chapter 1, I show that adults speaking to other adults, caregivers speaking to children, and children themselves tend to mention the atypical more than the typical features of concrete things. Language is structured to emphasize what is atypical—so how can one learn about what things are typically like from language? In this chapter I also show that distributional semantics models that use word co-occurrence to derive word meaning (word2vec) do not capture the typicality of adjective–noun pairs well. I also examine the performance of two more sophisticated language models (BERT and GPT-3); these models have input unlike what children have access to, but provide useful bounds on the typicality information learnable from applying simple training objectives to language alone. However, people can learn about typicality in other ways: in Chapter 2, I show that people infer that mentioned features are atypical. That is, when a novel object is called a "purple toma," adults infer that tomas are less commonly purple in general. This inference is captured by a model in the Rational Speech Act framework that posits that listeners reason about speakers' communicative goals. In Chapter 3, I ask: do children themselves infer that mentioned features are atypical? I find preliminary evidence that 5- to 6-year-old children who reliably respond on our typicality measure tend toward making contrastive rather than associative inferences; further work is necessary to confirm this finding and test younger children's contrastive inferences. Overall, this dissertation examines how language does not directly reflect the world, but selectively picks out remarkable facets of it, and what this implies for how adults, children, and language models learn

Listeners use descriptive contrast to disambiguate novel referents

People often face referential ambiguity; one cue to resolve it is adjectival description. Beyond narrowing potential referentsto those that match a descriptor, listeners may infer that a described object is one that contrasts with other present objectsof the same type (tall cup contrasts with another, shorter cup). This contrastive inference guides the visual identificationof a familiar referent as an utterance progresses (Sedivy et al., 1999). We extend this work, asking whether listeners usethis type of inference to guide explicit referent choice when reference is ambiguous, and whether this varies with adjectivetype. We find that participants consistently use size adjectives contrastively, but not color adjectives (Experiment 1)evenwhen color is described with more relative language (Experiment 2) or emphasized with prosodic stress (Experiment 3).Listeners can use adjective contrast to disambiguate a novel words referent, but do not treat all adjective types as equallycontrastive

Available referents and prompt specificity influence induction of feature typicality

Prior work suggests that speakers and listeners use discourse pragmatics to constrain potential referents and make infer-ences about the relationship of a novel referent to its category. This work addresses the use of discourse specificity andavailable referents in combination to make inferences about category feature typicality. In a visual search task and sub-sequent typicality rating task, participants ratings of typicality for an novel object’s color were affected by whether theobjects color was specified in the search prompt (e.g., Find the (blue) dax), the color of distractor objects (same as ordifferent from target), and the shape of distractor objects (same as or different from target). Specification of target colorin the prompt decreased typicality ratings, in keeping with work suggesting that over-informative utterances can induceinference of atypicality

Children hear more about what is atypical than what is typical

How do children learn the typical features of objects in the world? For many objects, this information must come from the language they hear. However, language does not veridically reflect the world: People are more likely to talk about atypical features (e.g., "purple carrot") than typical features (e.g., "orange carrot"). Does the speech children hear from their parents also overrepresent atypical features? We examined the typicality of adjectives produced by parents in a large, longitudinal corpus of parent-child interaction. Across nearly 2000 unique adjective–noun pairs, we found parents’ adjectives predominantly mark atypical features of objects, although parents of very young children are relatively more likely to comment on typical features as well. We then used vector space models to show that learning the typical features of common categories from linguistic input alone is challenging even with sophisticated statistical inference techniques

The Purple Carrot: Feature Typicality in Description Across Development

This project evaluates the relative co-occurrence of adjective-noun pairs in child-directed speech across development

Children hear more about what is atypical than what is typical

How do children learn the typical features of objects in theworld? For many objects, this information must come from thelanguage they hear. However, language does not veridicallyreflect the world: People are more likely to talk about atypicalfeatures (e.g., “purple carrot”) than typical features (e.g., “or-ange carrot”). Does the speech children hear from their parentsalso overrepresent atypical features? We examined the typical-ity of adjectives produced by parents in a large, longitudinalcorpus of parent-child interaction. Across nearly 2000 uniqueadjective–noun pairs, we found parents’ adjectives predomi-nantly mark atypical features of objects, although parents ofvery young children are relatively more likely to comment ontypical features as well. We then used vector space models toshow that learning the typical features of common categoriesfrom linguistic input alone is challenging even with sophisti-cated statistical inference techniques