Empirical correlates of event types - a priming study


Event types (ET) have been widely addressed in linguistics literature, but have received little attention in psycholinguitics, neurolinguistics and computational linguistics research. This thesis dissertation explores the nature of event types from a cognitive point of view: many descriptions and diagnostics on event types are available, but few studies have dealt with the problem of how event types are represented and processed in the mental lexicon. An important prerequisite for this sort of research is the building of a corpus of stimuli that meets our needs (web-based pre-tests were run to test the reliability of the stimuli, which should be balanced to control the variables known to affect processing costs) and an analysis of pre-existing literature in experimental psycholinguistics of event types. Our main concern was to explore new experimental settings in verb semantics psycholinguistics and to adapt them to this specific type of investigation: the choice of the method was narrowed down to the semantic priming paradigm, although the set of stimuli could also be suitable for other experimental settings, such as reading-time studies. The semantic priming paradigm was exploited to contrast processing effects on achievement verbs and activity verbs, which differ with respect to two superordinate features: durativity and resultativity. A series of priming experiments were run to explore differences and interactions between such features and the tense morphology and to evaluate the different contribution of the experimental setting in the observation and measurement of the effect: experiment 1 and experiment 2 followed a similar design and contrasted the effects of different neutral primes; experiment 3 focused on the interaction between event types and Italian tense morphology

Master’s Thesis
Alessandra Zarcone
Alessandra Zarcone
Professor of Language Technologies and Cognitive Assistants

Computational linguist with a background in NLP and in psycholinguistics, working on AI, NLP and human-machine interaction.