We investigated the emergence of sociolinguistic indexicality using an artificial-language-learning paradigm. Sociolinguistic indexicality involves the association of linguistic variants with nonlinguistic social or contextual features. Any linguistic variant can acquire "constellations" of such indexical meanings, though they also…

This article evaluates the predictions of an algorithmic-level distributed associative memory model as it introduces, propagates, and resolves ambiguity, and compares it to the predictions of computational-level parallel parsing models in which ambiguous analyses are accounted separately in discrete distributions. By superposing activation…

The meanings of natural language utterances depend heavily on context. Yet, what counts as context is often only implicit in conversation. The utterance "it's warm outside" signals that the temperature outside is relatively high, but the temperature could be high relative to a number of different "comparison classes": other…

Applying machine learning algorithms to automatically infer relationships between concepts from large-scale collections of documents presents a unique opportunity to investigate at scale how human semantic knowledge is organized, how people use it to make fundamental judgments ("How similar are cats and bears?"), and how these judgments…

We propose a cognitive-psychological model of linguistic intuitions about copredication statements. In copredication statements, like "The book is heavy and informative," the nominal denotes two ontologically distinct entities at the same time. This has been considered a problem for standard truth-conditional semantics. In this paper, we…

Retrieval practice of isolated words (e.g., with flashcards) enhances foreign vocabulary learning. However, vocabulary is often encountered in context. We investigated whether retrieval opportunities also enhance contextualized word learning. In two within-subjects experiments, participants encoded 24 foreign words and then read a story to further…

Most words in natural languages are polysemous; that is, they have related but different meanings in different contexts. This one-to-many mapping of form to meaning presents a challenge to understanding how word meanings are learned, represented, and processed. Previous work has focused on solutions in which multiple static semantic…

Comprehenders generate expectations about upcoming lexical items in language processing using various types of contextual information. However, a number of studies have shown that argument roles do not impact neural and behavioral prediction measures. Despite these robust findings, some prior studies have suggested that lexical prediction might be…

Children with developmental language disorder (DLD) served as a test case for determining the role of extant vocabulary knowledge, endogenous attention, and phonological working memory abilities in cross-situational word learning. First-graders (M[subscript age] = 7 years; 3 months), 44 with typical development (TD) and 28 with DLD, completed a…

A key component of research on human sentence processing is to characterize the processing difficulty associated with the comprehension of words in context. Models that explain and predict this difficulty can be broadly divided into two kinds, expectation-based and memory-based. In this work, we present a new model of incremental sentence…

In two experiments, we examined the impact of color on cognitive performance by asking participants to categorize stimuli presented in three different colors: red, green, and gray (baseline). Participants were either asked to categorize the meaning of words as related to the concepts of "go" or "stop" (Experiment 1) or to…

Learners are often constrained by their available study time, typically having to make a trade-off between depth and breadth of learning. Classic experimental paradigms in memory research treat all items as equally important, but this is unlikely the case in reality. Rather, information varies in importance, and people vary in their ability to…

The language we use over the course of conversation changes as we establish common ground and learn what our partner finds meaningful. Here we draw upon recent advances in natural language processing to provide a finer-grained characterization of the dynamics of this learning process. We release an open corpus (>15,000 utterances) of extended…

In order to learn the mappings from words to referents, children must integrate co-occurrence information across individually ambiguous pairs of scenes and utterances, a challenge known as cross-situational word learning. In machine learning, recent multimodal neural networks have been shown to learn meaningful visual-linguistic mappings from…

Redundant modifiers can facilitate referential interpretation by narrowing attention to intended referents. This is intriguing because, on traditional accounts, redundancy should impair comprehension. Little is known, however, about the effects of redundancy on older adults' comprehension. Older adults may show different patterns due to…