A workshop
University of Oregon - Eugene
July 26-27, 2025
It has long been evident that speakers simultaneously possess abstract knowledge of language that they use to generalize and adapt flexibly to new scenarios, but also retain item-specific knowledge that is derived from their experience with language usage, allowing them to efficiently anticipate familiar situations. However, the relationship between these two types of knowledge, and the extent to which each is recruited in any specific instance of language use, remains a subject of intense debate.
Recent advances in experimental methods and the sharpening of theoretical tools via computationally-implemented models have led to a recent flowering of advances in this area, with theories and models of this trade-off being developed largely independently and in parallel across domains including phonology, lexical semantics, syntax, and psycholinguistics. Further, work across these domains is carried out using a range of methodologies that are rarely brought into conversation with one another, and which offer different, complementary perspectives and types of evidence: experimental linguistics (ex., wug-tests, acceptability judgment tasks), evidence from language acquisition (both L1 and artificial language learning), morphological processing, corpus data, and computational modeling (both simulations studies and theory-driven statistical modeling of empirical data). This workshop is an interdisciplinary meeting to bring these data and methods from across domains to advance towards a theoretically coherent, and broadly empirically supported understanding of the tradeoff between abstraction and item-specific knowledge.
The program will feature a student poster session (see Call for Abstracts) along with invited talks and panel discussions organized into thematic sessions on the following topics:
EVIDENCE: What is the experimental evidence for abstract or item-specific knowledge?
MODELING: What does an adequate computationally-explicit, implemented model of simultaneous item-specific and abstract knowledge look like? What are the representations in this model?
LEARNING: How do speakers learn item-specific and abstract knowledge from the same data at the same time?
BRAIN: What evidence is there for how storage and abstraction are implemented neurally? Are these separate systems, or merely descriptions of different behaviors of a single system?
EVOLUTION: How does storage or abstraction at the level of individual speakers shape a language over time? How have languages evolved to be processable via a combination of storage and abstraction?
Contact: Canaan Breiss (cbreiss@usc.edu), co-organized by Gaja Jarosz, Emily Morgan, and Volya Kapatsinski