Language and Sentence Processing

Cue-based retrieval theories of sentence comprehension as sume that syntactically related words (e.g., the subject and the verb) are identified and linked together through a content addressable search in memory, using retrieval cues such as subject and animate. A key prediction is similarity-based interference: When multiple nouns in memory match retrieval cues at the verb, it becomes difficult to retrieve the target subject noun, causing a slowdown in processing time at the verb. Similarity-based interference is consistently observed in reading studies from English, furnishing strong evidence for cue-based retrieval theories. However, in verb-final languages such as German and Hindi, evidence is equivocal. The classi cal cue-based retrieval models fail to explain interference ef fect patterns observed in verb-final languages. Data demand a new theory that can explain interference effects in verb-final as well as verb-medial languages. We explore the predictive memory activation mechanism: the pre-verbal nouns stored in memory strongly preactivate the upcoming verb phrase, which can override the cost of retrieval interference in verb-final lan guages. We implement this mechanism within a cue-based re trieval architecture. We show that the predictive memory ac tivation model can account for interference effects observed in verb-final as well as verb-medial languages. Bayes fac tor analysis revealed strong evidence in favor of the predic tive memory activation model against the cue-based retrieval model. Our modeling results have an important theoretical im plication: Dependency completion is driven by top-down pre dictive activation and bottom-up cue-based retrieval processes.

This is an in-person presentation on July 20, 2026 (15:00 ~ 15:20 EDT).

Under surprisal theory, linguistic representations affect processing difficulty only through the bottleneck of surprisal. Our best estimates of surprisal come from large language models, which have no explicit representation of structural ambiguity. While LLM surprisal robustly predicts reading times across languages, it systematically underpredicts difficulty when structural expectations are violated---suggesting that representations of ambiguity are causally implicated in sentence processing. Particle filter models offer an alternative where structural hypotheses are explicitly represented as a finite set of particles. We prove several algorithmic consequences of particle filter models, including the amplification of garden-path effects. Most critically, we demonstrate that resampling, a common practice with these models, inherently produces real-time digging-in effects---where disambiguation difficulty increases with ambiguous region length. Digging-in magnitude scales inversely with particle count: fully parallel models predict no such effect.

This is an in-person presentation on July 20, 2026 (15:20 ~ 15:40 EDT).