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RESEARCH PRODUCT
Five Ways in Which Computational Modeling Can Help Advance Cognitive Science
Willem ZuidemaRaquel G. AlhamaTimothy Q. GentnerKevin EllisTimothy J. O'donnellTim SainburgRobert M. Frenchsubject
Linguistics and LanguageArtificial grammar learningComputer scienceCognitive Neuroscience[SHS.PSY]Humanities and Social Sciences/PsychologyExperimental and Cognitive PsychologyBayesian inferenceArtificial grammar learningArticle050105 experimental psychology03 medical and health sciences0302 clinical medicineArtificial IntelligenceHumans0501 psychology and cognitive sciencesCognitive scienceComputational modelPsycholinguisticsArtificial neural networkLift (data mining)Model selection05 social sciencesComputational modelingModels TheoreticalArtificial language learningFormal grammarsExperimental researchBayesian modelingVisualizationHuman-Computer InteractionCognitive ScienceNeural Networks ComputerForthcoming Topic: Learning Grammatical Structures: Developmental Cross‐species and Computational Approaches030217 neurology & neurosurgeryNeural networksdescription
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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2019-10-30 | Topics in Cognitive Science |