Towards a computational cognitive linguistics

25 Apr 2022

Usage-based linguistics is predicated upon the premise that languages are dynamic systems that emerge from usage and are shaped by usage in a process that is mediated by general cognitive abilities and functional considerations. They're also generally built on the assumption that we build our knowledge of language from the ground up, using general cognitive abilities.

The general cognitive abilities that have received most attention to date are classification, abstraction and imagination (metaphor, metonymy). Even though the concept of “emergence” plays a key role in usage-based theories, cognitive processes or functions that would enable a system to “emerge” from use have, however, been conspicuously absent from usage-based considerations. We have proposed that learning would be an ideal candidate to take on this role. On a usage-based, emergentist approach to language knowledge, abstractions must be learnable from the input listeners are exposed to if these abstractions are to lay claims to cognitive reality (Divjak, 2015a; Milin et al., 2016). Whereas earlier work explored whether abstract labels can be learned and whether they map onto distributional patterns in the input (cf. Divjak, Szymor & Socha, 2015), more recent work has brought these strands together by modeling directly how selected abstract labels would be learned from input using a cognitively realistic learning algorithm (cf. Milin, Divjak, & Baayen, 2017; Divjak, Milin et al. 2021).

In my talk I will present some of the work I am doing with the Out Of Our Minds team [] that operationalizes emergence through learning by using computational techniques that implement principles of learning. I will argue that allowing what can be learned from input constrain the units and generalizations that linguistic theory allows is one way of honoring the cognitive commitment by which cognitive linguists are bound. Ultimately, such an approach provides valuable insights into the kinds of generalizations or abstractions that can lay claim to cognitive plausibility and may ultimately alter the way in which we think about language.