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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Gubian, M. Bergmann, C. Boves, L. |
| Copyright Year | 2010 |
| Description | Author affiliation: Centre for Language & Speech Technology, Radboud University, Nijmegen, The Netherlands (Gubian, M.; Bergmann, C.; Boves, L.) |
| Abstract | 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. |
| Starting Page | 178 |
| Ending Page | 184 |
| File Size | 1104306 |
| Page Count | 7 |
| File Format | |
| ISBN | 9781424469000 |
| e-ISBN | 9781424469024 |
| DOI | 10.1109/DEVLRN.2010.5578847 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-08-18 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Training Pediatrics Analytical models Accuracy Computational modeling Speech Acoustics 5.2 grounding of knowledge and representations 6.1 language learning 6.8 statistical learning |
| Content Type | Text |
| Resource Type | Article |
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