Artificial Grammar Learning and the Primate Brain
Artificial Grammars (AGs) can be designed to emulate various aspects of human language syntax, such as the different types of relationships between words in a sentence. An interesting empirical question is which animal species can learn various levels of AG complexity. Understanding this could clarify the evolutionary roots of human language and facilitate the development of animal models to study language precursors at the neuronal level. In this talk I will first describe the results from behavioral AG learning work that we have conducted with macaque and marmoset monkeys, two species of nonhuman primates representing different primate evolutionary lineages. Here, I will propose a quantitative approach to relate our findings to those that have been obtained in other animal species (including songbirds) and with different AGs. Then I will describe fMRI results on macaque brain regions that are involved in AG learning and how these results compare to fMRI results in humans and chimpanzees (the latter done in collaboration with Yerkes Primate Research Center in the U.S.). I conclude by overviewing neurophysiology work that is underway in macaques to understand neuronal responses and cortical oscillations associated with AG learning.