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Computational Models

Computational Models

  • Instructor(s): Raquel G. Alhama / Andrew Jessop Max Planck Institute for Psycholinguistics
  • Available also as advanced: No

Computational models are an increasingly important tool in language acquisition research, they can help us describe the input, explain the learner’s behavior, and — perhaps most intriguingly — can simulate proposed mechanisms in the child’s mind. On a practical level, computational experiments do not suffer the same constraints as child studies, so that we can explore many conditions.

This tutorial will offer a brief general introduction to modeling followed by a joint programming session. The goal is to simulate infants’ ability to find recurring patterns in speech, which is thought to be a fundamental ability for language learners. Numerous studies have been carried out on the topic, exploring the conditions under which infants can indeed detect recurring patterns. Can all the findings be explained with a single simple mechanism?

Participants will get hands-on experience with the use of the programming language Python, a high level scripting language ideally suitable for both beginners and advanced scientific computing. At the end of the tutorial, participants will have built a working model and thus gained insight into the possibilities and limitations of computational modeling in child language research.

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