Various aspects of semantic features drive early vocabulary development, but less is known about how the global and local structure of the overall semantic feature space influences language acquisition. A feature network of English words was constructed from a large database of adult feature production norms such that edges in the network…

Similarity is one of the most important relations humans perceive, arguably subserving category learning and categorization, generalization and discrimination, judgment and decision making, and other cognitive functions. Researchers have proposed a wide range of representations and metrics that could be at play in similarity judgment, yet have not…

A large literature suggests that the organization of words in semantic memory, reflecting meaningful relations among words and the concepts to which they refer, supports many cognitive processes, including memory encoding and retrieval, word learning, and inferential reasoning. The co-activation of related items has been proposed as a mechanism by…

Considerable work during the past two decades has focused on modeling the structure of semantic memory, although the performance of these models in complex and unconstrained semantic tasks remains relatively understudied. We introduce a two-player cooperative word game, Connector (based on the boardgame Codenames), and investigate whether…

Previous studies provided evidence for a connection between language processing and language change. We add to these studies with an exploration of the influence of lexical-distributional properties of words in orthographic space, semantic space, and the mapping between orthographic and semantic space on the probability of lexical extinction.…

Although there is a broad consensus that both the procedural and declarative memory systems play a crucial role in language learning, use, and knowledge, the mapping between linguistic types and memory structures remains underspecified: by default, a dual-route mapping of language systems to memory systems is assumed, with declarative memory…

Distributional models of semantics learn word meanings from contextual co-occurrence patterns across a large sample of natural language. Early models, such as LSA and HAL (Landauer & Dumais, 1997; Lund & Burgess, 1996), counted co-occurrence events; later models, such as BEAGLE (Jones & Mewhort, 2007), replaced counting co-occurrences…

Three experiments test the theory that verb meanings are more malleable than noun meanings in different semantic contexts, making a previously seen verb difficult to remember when it appears in a new semantic context. Experiment 1 revealed that changing the direct object noun in a transitive sentence reduced recognition of a previously seen verb,…

There is considerable evidence that language comprehenders derive lexical-semantic meaning by mentally simulating perceptual and motor attributes of described events. However, the nature of these simulations--including the level of detail that is incorporated and contexts under which simulations occur--is not well understood. Here, we examine the…

Subject relative clauses (SRCs) are typically processed more easily than object relative clauses (ORCs), but this difference is diminished by an inanimate head-noun in semantically non-reversible ORCs ("The book that the boy is reading"). In two eye-tracking experiments, we investigated the influence of animacy on online processing of…

"Natural syllogisms" are arguments formally identifiable with categorical syllogisms that have an implicit universal affirmative premise retrieved from semantic memory rather than explicitly stated. Previous studies with adult participants (Politzer, 2011) have shown that the rate of success is remarkably high. Because their resolution…

We explore the following two cognitive questions regarding crosslinguistic variation in lexical semantic systems: Why are some linguistic categories--that is, the associations between a term and a portion of the semantic space--harder to learn than others? How does learning a language-specific set of lexical categories affect processing in that…

The present paper addresses the study of non-arbitrariness in language within a deep learning framework. We present a set of experiments aimed at assessing the pervasiveness of different forms of non-arbitrary phonological patterns across a set of typologically distant languages. Different sequence-processing neural networks are trained in a set…

Children and adults use established global knowledge to generate real-time linguistic predictions, but less is known about how listeners generate predictions in circumstances that semantically conflict with long-standing event knowledge. We explore these issues in adults and 5- to 10-year-old children using an eye-tracked sentence comprehension…

The collection of very large text sources has revolutionized the study of natural language, leading to the development of several models of language learning and distributional semantics that extract sophisticated semantic representations of words based on the statistical redundancies contained within natural language (e.g., Griffiths, Steyvers,…