Keywords: Discriminative Learning; Word Processing; Recurrent Neural Networks; Relative Entropy.
Psycholinguistic evidence based on inflectional and derivational word families has emphasised the combined role of Paradigm Entropy and Inflectional Entropy in human word processing. Although the way frequency distributions affect behavioural evidence is clear in broad outline, we still miss a clear algorithmic model of how such a complex interaction takes place and why. The main challenge is to understand how the local interaction of learning and processing principles in morphology can result in global effects that require knowledge of the overall distribution of stems and affixes in word families. We show that principles of discriminative learning can shed light on this issue. We simulate learning of verb inflection with a discriminative recurrent network of specialised processing units, whose level of temporal connectivity reflects the frequency distribution of input symbols in context. We analyse the temporal dynamic with which connection weights are adjusted during discriminative learning, to show that self-organised connections are optimally functional to word processing when the distribution of inflected forms in a paradigm (Paradigm Entropy) and the distribution of their inflectional affixes across paradigms (Inflectional Entropy) diverge minimally.