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Investigating word learning processes in an artificial agent

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons41950

Bergmann,  Christina
Centre for Language Studies • Radboud University Nijmegen;
International Max Planck Research School for Language Sciences, MPI for Psycholinguistics, Max Planck Society;

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Zitation

Gubian, M., Bergmann, C., & Boves, L. (2010). Investigating word learning processes in an artificial agent. In Proceedings of the IXth IEEE International Conference on Development and Learning (ICDL). Ann Arbor, MI, 18-21 Aug. 2010 (pp. 178 -184). IEEE.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-000F-5A50-1
Zusammenfassung
Researchers in human language processing and acquisition are making an increasing use of computational models. Computer simulations provide a valuable platform to reproduce hypothesised learning mechanisms that are otherwise very difficult, if not impossible, to verify on human subjects. However, computational models come with problems and risks. It is difficult to (automatically) extract essential information about the developing internal representations from a set of simulation runs, and often researchers limit themselves to analysing learning curves based on empirical recognition accuracy through time. The associated risk is to erroneously deem a specific learning behaviour as generalisable to human learners, while it could also be a mere consequence (artifact) of the implementation of the artificial learner or of the input coding scheme. In this paper a set of simulation runs taken from the ACORNS project is investigated. First a look `inside the box' of the learner is provided by employing novel quantitative methods for analysing changing structures in large data sets. Then, the obtained findings are discussed in the perspective of their ecological validity in the field of child language acquisition.