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.
Highlights
Computer models have given us a better understanding of everything from the evolution of stars to the evolution of the human eye, from chemical reactions in the ozone layer to animal mating behavior, and much more
We focus on the domain of Artificial Grammar Learning (AGL), a field that employs artificial language stimuli to systematically manipulate certain factors to test for language learning
We have argued in this paper that computational techniques can take cognitive research, in general, and artificial grammar research, in particular, to a new level
Summary
Computer models have given us a better understanding of everything from the evolution of stars to the evolution of the human eye, from chemical reactions in the ozone layer to animal mating behavior, and much more. AGL is a illustrative case because the design of artificial stimuli capturing particular features of natural language, while ruling out other interpretations, is challenging It is a field where many of the same types of debates have happened as in cognitive science at large, and where many of the different computational modeling paradigms have been applied (e.g., Alhama & Zuidema, 2017, 2019; Culbertson et al, 2013; Frank et al, 2010; French et al, 2011; Gagliardi et al, 2017; Kemp et al, 2007; Kirby et al, 2015; Marcus et al, 1999; Pearl et al, 2010; Perruchet et al, 2006; Wonnacott, 2011).
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