Yang returns to the problem of referential ambiguity, addressed in the opening paper by Gleitman and Trueswell. Using a computational approach, he argues that “big data” approaches to resolving referential ambiguity are destined to fail, because of the inevitable computational explosion needed to keep track of contextual associations present when a word is uttered. Yang tests several computational models, two of which depend on one‐trial learning, as described in Gleitman and Trueswell’s paper. He concludes that such models outperform cross‐situational learning (...) models, thereby formalizing, reinforcing, and advancing the conclusions offered by Gleitman and Trueswell. (shrink)

We evaluate here the performance of four models of cross-situational word learning: two global models, which extract and retain multiple referential alternatives from each word occurrence; and two local models, which extract just a single referent from each occurrence. One of these local models, dubbed Pursuit, uses an associative learning mechanism to estimate word-referent probability but pursues and tests the best referent-meaning at any given time. Pursuit is found to perform as well as global models under many conditions extracted from (...) naturalistic corpora of parent-child interactions, even though the model maintains far less information than global models. Moreover, Pursuit is found to best capture human experimental findings from several relevant cross-situational word-learning experiments, including those of Yu and Smith (), the paradigm example of a finding believed to support fully global cross-situational models. Implications and limitations of these results are discussed, most notably that the model characterizes only the earliest stages of word learning, when reliance on the co-occurring referent world is at its greatest. (shrink)

Newport addresses a fundamental question in language learning: When, why, and how do learners come to form rules, given linguistic input that varies probabilistically? She presents several case studies that confirm and extend a long‐standing theme of her work: that young learners tend to form rules from variable input, whereas adult learners store and use its statistical probabilities. Thus, child and adult learners use quite different kinds of computations when learning language; the consequence is that operating on the very same (...) input to learning results in quite different outcomes of learning. (shrink)

Ünal and Papafragou examine what one might call learning “really hard” meanings, that is, evidentials, which linguistically mark the source of information associated with use of a given verb. They explore the relationship between children’s mastery of the relevant nonlinguistic conceptual underpinnings and the linguistic marking of evidentials, and find that the two kinds of mastery show different developmental trajectories, with the relationship far from one‐to‐one. This case study points to the complex and nuanced relationships between language learning and nonlinguistic (...) understanding in the same domain. (shrink)

Lidz also ponders the theory of syntactic bootstrapping by asking why it is that learners are considered to retain little of extralinguistic environments (i.e., their observations) during word learning, while being able to retain detailed representations of linguistic context, for example, the multiple syntactic frames in which a verb appears. Lidz argues that learners value syntactic information over extralinguistic context from the beginning of learning, consistent with syntactic bootstrapping as a key device for verb learning.

Landau also pushes the role of syntax and its mapping to semantics in learning what some would consider “easy words”—the simplest spatial prepositions in English, in and on. Taking as a starting point that the syntactic distribution of a word is a reflex of its meaning, Landau shows that careful study of how children and adults linguistically encode a range of containment and support configurations reveals a special status for “core” configurations in each domain. She proposes that children come to (...) learning spatial terms with the expectation that configurations reflecting core concepts of containment and support will be expressed with the maximally simple, unmarked, linguistic expression. (shrink)

Landau introduces the volume with a selective review of Lila R. Gleitman’s intellectual history, emphasizing the theoretical roots of her research. These include influences of Zellig Harris and Noam Chomsky, her creation of “The Great Verb Game” (which paved the way for the theory of syntactic bootstrapping), the importance of natural “deprivation” experiments, and how they shed light on understanding what the data for learning really might be, and her life as an empiricist, driven by data to nativist conclusions. The (...) introduction also provides brief summaries of each contributed paper. (shrink)

Prepositions name spatial relationships. But they are also used to convey abstract, non-spatial relationships —raising the question of how the abstract uses relate to the concrete spatial uses. Despite considerable success in delineating these relationships, no general account exists for the two most frequently extended prepositions: in and on. We test the proposal that what is preserved in abstract uses of these prepositions is the relative degree of control between the located object and the reference object. Across four experiments, we (...) find a continuum of greater figure control for on and greater ground control for in. These findings bear on accounts of semantic structure and language change, as well as on second language instruction. (shrink)

Framed in psychological terms, the basic question of linguistic theory is what is stored in memory, and in what form. Traditionally, what is stored is divided into grammar and lexicon, where grammar contains the rules and the lexicon is an unstructured list of exceptions. We develop an alternative view in which rules of grammar are simply lexical items that contain variables, and in which rules have two functions. In their generative function, they are used to build novel structures, just as (...) in traditional generative linguistics. In their relational function, they capture generalizations over stored items in the lexicon, a role not seriously explored in traditional linguistic theory. The result is a highly structured lexicon with rich patterns among stored items. We further explore the possibility that this sort of structuring is not specific to language, but appears in other cognitive domains as well, such as the structure of physical objects, of music, and of geographical and social knowledge. The differences among cognitive domains do not lie in this overall texture, but in the materials over which stored relations are defined. The challenge is to develop theories of representation in these other domains comparable to that for language. (shrink)

Gleitman and Trueswell’s “The easy words” forms a pair with their earlier paper, “Hard words,” completing a circle in which the authors ask how “easy” words (e.g., concrete nouns) are learned. They take up the hypothesis of “cross‐situational learning,” and argue that accumulating observations actually hinders learning if the mechanism requires holding all exemplars in memory over time. They present an alternative hypothesis, “Propose but Verify,” wherein people use one‐trial learning to confirm or disconfirm their current hypothesis—a mechanism distinctly different (...) from cross‐situational learning. (shrink)

Fisher, Jin, and Scott push a central assumption of syntactic bootstrapping: that learners have a universal bias to map each noun in a sentence onto a participant role (i.e., argument of the verb). They propose two enrichments: First, that children use both semantic and syntactic information in representing nouns that accompany a verb; second, that children expect continuity across a discourse. They provide evidence for both learning mechanisms among young children, further spelling out the precise mechanisms underlying syntactic bootstrapping.