Seeking meaning: Examining a cross-situational solution to learn action verbs 
using human simulation paradigm

Zhang, Yayun; Amatuni, A.; Crain, E.; Yu, C.

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Seeking meaning: Examining a cross-situational solution to learn action verbs using human simulation paradigm

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Zhang, Y., Amatuni, A., Crain, E., & Yu, C. (2020). Seeking meaning: Examining a cross-situational solution to learn action verbs using human simulation paradigm. In S. Denison, M. Mack, Y. Xu, & B. C. Armstrong (Eds.), Proceedings of the 42nd Annual Meeting of the Cognitive Science Society (CogSci 2020) (pp. 2854-2860). Montreal, QB: Cognitive Science Society.

Cite as: https://hdl.handle.net/21.11116/0000-000B-2EE4-1

Abstract

To acquire the meaning of a verb, language learners not only need to find the correct mapping between a specific verb and an action or event in the world, but also infer the underlying relational meaning that the verb encodes. Most verb naming instances in naturalistic contexts are highly ambiguous as many possible actions can be embedded in the same scenario and many possible verbs can be used to describe those actions. To understand whether learners can find the correct verb meaning from referentially ambiguous learning situations, we conducted three experiments using the Human Simulation Paradigm with adult learners. Our results suggest that although finding the right verb meaning from one learning instance is hard, there is a statistical solution to this problem. When provided with multiple verb learning instances all referring to the same verb, learners are able to aggregate information across situations and gradually converge to the correct semantic space. Even in cases where they may not guess the exact target verb, they can still discover the right meaning by guessing a similar verb that is semantically close to the ground truth.