TY - JOUR
T1 - Towards cognitively plausible data science in language research
AU - Milin, Petar
AU - Divjak, Dagmar
AU - Dimitrijević, Strahinja
AU - Baayen, R. Harald
PY - 2016/11/1
Y1 - 2016/11/1
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/84996599817
U2 - 10.1515/cog-2016-0055
DO - 10.1515/cog-2016-0055
M3 - Article
SN - 0936-5907
VL - 27
SP - 507
EP - 526
JO - Cognitive Linguistics
JF - Cognitive Linguistics
IS - 4
ER -