“All mimsy were the borogoves” – a discriminative learning model of morphological knowledge in pseudo-word inflection

Abstract

Grammatical knowledge of native speakers has often been investigated in so-called wug tests, in which participants have to inflect pseudo-word forms (wugs). Typically it has been argued that in inflecting these pseudo-words, speakers apply their knowledge of word formation processes. However, it remains unclear what exactly this knowledge is and how it is learned.According to one theory, the knowledge is best characterized as abstractions and rules that specify how units can be combined. Another theory maintains that it is best characterized by analogy. In both cases the knowledge is learned by association based on positive evidence alone.In this paper, we model the classification of pseudo-words to Maltese plural classes on the basis of phonetic input using a shallow neural network trained with an error-driven learning algorithm. We demonstrate that the classification patterns mirror those of Maltese native speakers in a wug test.Our results indicate that speakers rely on gradient knowledge of a relation between the phonetics of whole words and plural classes, which is learned in an error-driven way.

Maltese as a merger of two worlds: A cross-language approach to phonotactic classification

“…On the basis of the pluralization patterns produced by the participants, they concluded that pluralization in Maltese is best explained through a process of analogy, not the mapping onto a CV pattern: plurals for novel words are created on the basis of phonological similarity to existing forms and the frequency of pluralization patterns. [ 24 , 25 ] have corroborated this finding by modeling the pluralization process with a simple two-layer network which predicted the plural class of singular nouns only on the basis of its phonological word form. The network thus learned to exploit systematic differences between the word forms in the broken and sound group.…”

“…While the broken plural system is likely to be reflected in phonotactics very specific to Semitic languages, the case is less clear in the sound system with its different suffixes whose word-final vowel and consonant sequences may potentially be present in both Semitic and Non-Semitic languages. Concerning these two different morphological classes, [ 24 , 25 ] demonstrated that the two-layer network employed in the present study is very well capable to identify them on the basis of the phonotactic cues of the nouns. This potential impact on the classification brings us to the hypotheses of our present study.…”

Modelling German Word Stress

“…The functionality of the two algorithms has been successfully demonstrated to capture discriminative learning (Bröker & Ramscar 2020;Ramscar 2021) of morphological processes including inflection (Ramscar & Yarlett 2007;Ramscar et al 2013bRamscar et al , 2010Nieder et al 2021Nieder et al , 2022, of processing in the context of reading and listening to morphological simple and complex words (Baayen et al 2011;Arnold et al 2017), and also in other domains such as the learning of phonetic categories (Olejarczuk et al 2018;Nixon 2020;Nixon & Tomaschek 2020 and speech production (Ramscar & Yarlett 2007;Tomaschek et al 2019;Baayen et al 2019;Tomaschek & Ramscar 2022). An introduction to using NDL can be found in Tomaschek (2020) and an excellent overview of how the dynamics of connection weights depend on cue-to-outcome constellations is presented by Hoppe et al (2022).…”