Research project
The comparative biology of language learning
A theoretical project on the insights gained by human (including infant), nonhuman animal and computational studies on artificial grammar learning; identifying the critical questions for future research by developing novel experimental and computational approaches to address these issues.
- Duration
- 2016 - 2017
- Contact
- Carel ten Cate
- Funding
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The Netherlands Organisation for Scientific Research (NWO)
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Netherlands Institute for Advanced Study in the Humanities and Social Sciences (NIAS)
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Lorentz Center
- Partners
Prof. Dr. Claartje Levelt, Leiden University Centre for Linguistics
Dr. Willem Jelle Zuidema, Institute for Logic, Language and Computation, University of Amsterdam
Prof. Dr Chris Petkov, Institute of Neuroscience and Centre for Behaviour and Evolution, Newcastle University
Dr. Judit Gervain, Laboratoire Psychologie de la Perception, Université Paris Descartes
The Artificial Grammar Learning (AGL) paradigm is a widely used paradigm to investigate the sequence processing ability of human infants, adults, and non-human animals like songbirds and primates. However, results obtained in the AGL paradigm have also generated debate, and have raised questions that go to the heart of the approach: Are the pattern-detection mechanisms shown in AGL experiments the same as those used to acquire real languages? Do animal experiments really demonstrate meaningful rule-learning abilities and do they provide proper comparisons for human language learning? What is known about the neurobiological substrate, processes and pathways that are involved in sequence processing and how do these compare across species? What do computational models suggest about the rule learning mechanisms and their evolution? How does the use of very different experimental methods and the use of different types of stimuli affect conclusions about learning abilities? In short, a critical reassessment of the AGL approach in general, and of the AGL experimental procedures in particular is needed. This is the topic of the NIAS-Lorentz Theme Group 2016-2017 and a Lorentz workshop.