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Modeling the effect of co-occurring distractor referents on word learning using the Rescorla-Wagner model and propose-but-verify

Braak, van, Krijn Dirk (2018) Modeling the effect of co-occurring distractor referents on word learning using the Rescorla-Wagner model and propose-but-verify. Bachelor's Thesis, Artificial Intelligence.

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Abstract

Word learning is an actively studied subject of research in linguistics and cognition. Several theories have been proposed to describe word learning. Two of these theories are cross situational learning and propose-but-verify. Roembke and McMurray (2016) conducted experiments studying the effects of co-occurring distractor referents on word learning. This current research will study the effects of co-occurring distractor referents by modeling the experiments of Roembke and McMurray (2016) using models of cross situational learning and propose-butverify in the form of Rescorla-Wagner learning and a model of propose-but-verify as proposed by Trueswell, Medina, Hafri, and Gleitman (2013). Both models predict that co-occurring distractor referents impede word learning. The Rescorla-Wagner however can not be directly related to choice accuracy but can be used as predictor for the most likely choice. The propose-but-verify model does predict accuracy but seems to lack a gradual learning component. A combination of both models might be necessary to accurately predict the effect of co-occurring distractor referents on word learning.

Item Type: Thesis (Bachelor's Thesis)
Supervisor:
Supervisor nameSupervisor E mail
Spenader, J.K.J.K.Spenader@rug.nl
Rij-Tange, J.C. vanJ.C.van.Rij@rug.nl
Degree programme: Artificial Intelligence
Thesis type: Bachelor's Thesis
Language: English
Date Deposited: 13 Sep 2018
Last Modified: 12 Jul 2019 12:08
URI: http://fse.studenttheses.ub.rug.nl/id/eprint/18562

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