Towards cognitively plausible data science in language research

Abstract

Over the past 10 years, Cognitive Linguistics has taken a Quantitative Turn. Yet, concerns have been raised that this preoccupation with quantification and modelling may not bring us any closer to understanding how language works. We show that this objection is unfounded, especially if we rely on modelling techniques based on biologically and psychologically plausible learning algorithms. These make it possible to take a quantitative approach, while generating and testing specific hypotheses that will advance our understanding of how knowledge of language emerges from exposure to usage.

The crosslinguistic acquisition of sentence structure: Computational modeling and grammaticality judgments from adult and child speakers of English, Japanese, Hindi, Hebrew and K'iche'

“…Our starting point when seeking to develop a computational model was the discriminative learning framework outlined in, for example, Ramscar and Yarlett (2007) . One advantage of this framework is that it has already been used to model a number of important phenomena in the child language acquisition literature, including grammatical gender ( Arnon & Ramscar, 2012 ), word-learning (e.g., Baayen, Chuang, Shafaei-Bajestan, & Blevins, 2019 ; Ramscar, Dye, & Klein, 2013 ), and both inflectional and derivational morphology (e.g., Baayen & Smolka, 2019 ; Milin, Divjak, Dimitrijević, & Baayen, 2016 ; Ramscar, Dye, & McCauley, 2013 ; Ramscar & Yarlett, 2007 ). A second advantage of the framework is its simplicity.…”

The Cognitive Processing of Derived Nouns With Ambiguous Suffixes: Behavioral and Eyemovement Study

“…In other words, morphology was not present as a single level of processing per se, but it was a product of the mentioned mapping processing. The Naïve Discriminative Learning (NDL) model demonstrated the success of this a-morphous perspective in the lexical processing in a larger number of research, in both inflectional and derivational morphology (Milin, Divjak, Dimitrijević, & Baayen, 2016;Milin et al, 2017;Plag & Winter Balling, in press). Finally, it is important to mention that this a-morphous perspective in the lexical processing in the field of derivational morphology is very similar to distributed morphology, a derivational morphology perspective from theoretical linguistics (Halle, 1990(Halle, , 1997.…”

The Role of Phonotactic Information in the Processing and Production of Inflectional Morphology

“…Likewise, they are particularly relevant within debates about the mechanisms underlying production and perception of morphologically complex words (Keuleers, 2018). It was shown that the use of cognitively acceptable algorithms, such as Memory-based Learning (Daelemans and van den Bosch, 2005) or Naive Discriminative Learning (Baayen et al 2011), based on the principles of animal and human learning leads to better understanding of how linguistic knowledge arises from the exposure to language and its use (Milin et al 2016).…”

“…Yet, what they all have in common is that they do not rely on rules in the processing of morphologically complex words, even in case of irregular forms 2002). One such model is MBL (Daelemans and van den Bosch, 2005), which represents a computer implementation of the approach based on examples, whose main propositions reflect not only linguistic knowledge but also a possible cognitive architecture and processes present during language use (Baayen, 2011;Keuleers, 2008;Keuleers and Dealemans, 2007;Milin et al, 2011;Milin et al, 2016).…”