This chapter presents semantic frameworks that model the general capability of language to refer to atomic, as well as non-atomic entities. Two approaches are developed and discussed in detail throughout the chapter: a set-theoretical approach and an approach in which entities are modeled as atomic and plural individuals. After the formal introduction of the two approaches, the chapter shows how number marking in language can be represented and how other concepts related to semantic number, in particular, distributivity, cumulativity and collectivity, have been analyzed in formal semantic theories. We introduce a framework in which we can start exploring in a computationally explicit way how complex, mechanistically specified, and production-based cognitive models of linguistic skills, e. We focus on a basic learning algorithm tabular Q-learning and a very simple task, namely lexical decision LD , framed as a sequential decision problem, with the goal of learning when a specific rule should be fired. Learning is faster and less noisy for shorter LD tasks fewer stimuli , but the Q-learning agent manages to learn longer tasks fairly well. Realistically long LD tasks and more complex models, e. This open access book introduces a general framework that allows natural language researchers to enhance existing competence theories with fully specified performance and processing components. Gradually developing increasingly complex and cognitively realistic competence-performance models, it provides running code for these models and shows how to fit them to real-time experimental data.
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The syntax of infinitives in Czech
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