An entropy model for artificial grammar learning
A model is proposed to characterize the type of knowledge acquired in Artificial Grammar Learning (AGL). In particular, Shannon entropy is employed to compute the complexity of different test items in an AGL task, relative to the training items. According to this model, the more predictable a test i...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2010-06-01
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| Series: | Frontiers in Psychology |
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| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fpsyg.2010.00016/full |