Five Ways in Which Computational Modeling Can Help Advance Cognitive Science: Lessons From Artificial Grammar Learning. (30th October 2019)

• Record Type: Journal Article
• Authors: Zuidema, Willem; French, Robert M.; Alhama, Raquel G.; Ellis, Kevin; O'Donnell, Timothy J.; Sainburg, Tim; Gentner, Timothy Q.
• Is Part Of: Topics in cognitive science. Volume 12:Number 3(2020)
• Page Start: 925
• Page End: 941
• Publication Date: 2019-10-30
• Subjects: Computational modeling -- Neural networks -- Formal grammars -- Bayesian modeling -- Artificial language learning -- Artificial grammar learning; Cognitive science -- Periodicals; Cognitive Science -- Periodicals; 153.05
• DOI: 10.1111/tops.12474
• Languages: English
• ISSNs: 1756-8757

Abstract

Abstract: There is a rich tradition of building computational models in cognitive science, but modeling, theoretical, and experimental research are not as tightly integrated as they could be. In this paper, we show that computational techniques—even simple ones that are straightforward to use—can greatly facilitate designing, implementing, and analyzing experiments, and generally help lift research to a new level. We focus on the domain of artificial grammar learning, and we give five concrete examples in this domain for (a) formalizing and clarifying theories, (b) generating stimuli, (c) visualization, (d) model selection, and (e) exploring the hypothesis space. Abstract : Zuidema et al. illustrate how empirical AGL studies can benefit from computational models and techniques. Computational models can help clarifying theories, and thus in delineating research questions, but also in facilitating experimental design, stimulus generation, and data analysis. The authors show, with a series of examples, how computational modeling can be integrated with empirical AGL approaches, and how model selection techniques can indicate the most likely model to explain experimental outcomes.